Abstract
ATTD 2023 Invited Speaker Abstracts A-1
ATTD 2023 Oral Abstracts A-38
ATTD 2023 E-Poster Abstracts A-85
ATTD 2023 Late Breaking Abstracts A-246
ATTD 2023 Read By Title A-268
ATTD 2023 Abstract Author Index A-270
OPENING CEREMONY
OPENING LECTURE ‐ THE NEW FACE OF DIABETES
University Hospitals Leuven ‐ KU Leuven, Endocrinology, Leuven, Belgium
Since the first clinical use of insulin, more than one hundred years ago, the face of diabetes has dramatically changed. Diabetes turns out to be a ‘hydra’ with many faces, with many pathophysiological routes, with many diagnostic paths and more importantly with many therapeutic opportunities. The last 20‐30 years have seen an explosion in our knowledge and in our therapeutic approach of people living with diabetes, ranging from the introduction of novel insulins and novel technologies for measuring glucose and administering insulin, to the availability of direct organ protecting agents and disease modifying therapeutics, in particular in type 2 diabetes, but more recently also in type 1 diabetes. Research is moving on rapidly, with the promise of precision medicine for all just around the corner. In the whirlwind of progress, it will remain important to stay focused on what really matters: the quality of life of the person living with diabetes. For people to live longer and healthier lives, not only tools and techniques are important, but even more so education, motivation, accompaniment of the person living with diabetes. Making the person with diabetes a member of the multidisciplinary team will ultimately determine success. The way we communicate all the novelties and make them matter, really matter for those with diabetes, is crucial and we should never forget that there are as many faces to diabetes as there are people living with this disease. Importantly, we need to strive for an all‐inclusive strategy in diabetes care: access to care should be there for all… independent on age, gender, where you are born in the world, your socio‐economic status…. And probably that is the greatest challenge to be faced in the next years. A challenge however this community can and WILL overcome.
PLENARY (1) – CGM USE IN TYPE 2 DIABETES AND BEYOND
USE OF CGM WITH PEOPLE WITH DIABETES TYPE 2 NOT TREATED WITH INSULIN
International Diabetes Center, Healthpartners Institute, Minneapolis, United States of America
“CGM‐First,” “CGM‐Standard of Care,” “CGM‐ Most significant advance in diabetes management since the discovery of insulin!”
Amer. Board Internal Med‐ Choosing Wisely campaign promotes clinician‐patient conversations about avoiding unnecessary care … like this example,
PLENARY (1) – CGM USE IN TYPE 2 DIABETES AND BEYOND
USE OF CGM WITH PEOPLE WITH DIABETES TYPE 2 TREATED WITH BASAL INSULIN ONLY
Manchester University NHS Foundation Trust, Manchester Diabetes Center, Manchester, United Kingdom
Glucose monitoring is central to safe and effective management for individuals with type 2 diabetes using insulin. It is estimated that approximately 30% of people living with type 2 diabetes in the USA are treated with insulin, with about two‐thirds using basal insulin without prandial insulin. However, only about one‐third of those individuals using insulin achieved HbA1c of less than 7.0%. Recent data also suggest there had not been much improvement in glycaemic outcomes in the USA between 2005 and 2016. Real‐time (rtCGM) and intermittently scanned continuous glucose monitoring (isCGM), by providing frequent glucose measurements, low and high glucose alerts, and glucose trend information can better inform diabetes management decisions compared with episodic self‐monitoring with fingerstick glucose. Studies have demonstrated that CGM improved glycaemic control in individuals with type 1 diabetes and with type 2 diabetes using insulin regimens with basal plus prandial insulin. However, the role of CGM in individuals with type 2 diabetes using less‐intensive insulin regimens is not well defined. Key Objectives of this lecture include: Understand the status of current glycaemic control in people with type 2 diabetes Glycaemic profiles of patients with type 2 diabetes using basal insulin HbA1c, sensor‐based and other outcomes from studies investigating the efficacy and safety of continuous glucose monitoring in people with T2DM only on basal insulin Impact of CGM on patient‐reported outcomes and quality of life Use of SGLT2 inhibitors and GLP‐1 in studies investigating CGM Mechanisms underpinning the improved outcomes Cost‐effectiveness Gaps in evidence‐based and future studies
PLENARY (1) – CGM USE IN TYPE 2 DIABETES AND BEYOND
USE OF CGM IN THE CYSTIC FIBROSIS POPULATION
Massachusetts General Hospital, Endocrinology, Boston, United States of America
Cystic fibrosis related diabetes (CFRD) affects up to 20% of adolescents and 50% of adults with cystic fibrosis (CF). Although CFRD shares some characteristics of type 1 and type 2 diabetes, it is a unique form of diabetes caused primarily by insulin deficiency from progressive islet cell dysfunction and destruction related to underlying pancreatic exocrine disease and fibrosis. At present, the oral glucose tolerance test (OGTT) is recommended annually in adolescents and adults with CF to screen for CFRD, but screening rates have historically been suboptimal, particularly among adults. Insulin is the only recommended treatment for CFRD, but this can add substantial treatment burden to an already medically complex patient population. Continuous glucose monitoring (CGM) has been validated in people with CF, and CGM measures have been correlated with important clinical outcomes such as pulmonary function and nutritional status. Emerging data suggest that CGM may identify people at risk for the future development of CFRD and may be a promising approach for the diagnosis of CFRD, but prospective longitudinal studies investigating this as a tool for CFRD screening are greatly needed. Although data are very limited, CGM may also have a beneficial effect on the management of CFRD, including in combination with hybrid closed loop insulin pumps, offering the potential for improved glycemic control and decreased diabetes treatment burden. In summary, CGM technology may be particularly useful for addressing current challenges unique to CF, but further studies are needed to investigate the use of this tool in the screening, diagnosis, and management of CFRD.
PLENARY (1) – CGM USE IN TYPE 2 DIABETES AND BEYOND
THE VISION OF THE FUTURE OF CGM IN TYPE 2 DIABETES
BDC, Pediatrics And Internal Medicine, aurora, United States of America
With the increasing number of people diagnosed with both type 1 and type 2 diabetes and related healthcare costs, it is imperative that we find easier ways to manage diabetes remotely and empower self‐diabetes management. Recently, the JDRF launched Type 1 Diabetes (T1D) Index where they revealed stark disparities in T1D life expectancy by countries. They also project a 66‐116% increase in the prevalence of T1D by 2040. Over the last three years, many new continuous glucose monitors (CGMs) have been approved in Western Europe and the USA. We have come a long way in the past 28 years from the first CGM being iPro, developed and launched by MiniMed (now Medtronic MiniMed, Northridge, CA, USA). Many CGM terminologies have been used, such as retrospective vs real‐time, real‐time vs isCGM, and adjunctive vs non‐adjunctive. Now most CGMs are standalone factory‐calibrated devices lasting for 10‐14 days. At the time of this writing, about 8 million people are using a CGM for their diabetes management, and this number is likely to exponentially grow to more than 15‐20 million in the next 5‐10 years. Also, in the near future, we might see another electrolyte or a ketone measurement being measured continuously through the same device (CGM + CKM, etc.). The majority of the available CGMs have a MARD of <10%; and thus, are pretty accurate for their interoperability with other devices like insulin pumps. Just like many years ago, Louis Monnier, et al. had shown that fasting blood glucose (FBG) values relate better in individuals with higher A1c and post‐prandial blood glucose (PPBG) values correlate better with individuals with lower A1c values. Similarly now, Time In Range (TIR) correlates to the contributions by FBG and PPBG. The research data has clearly documented that use of CGM improves glucose control, TIR, reduces hypo‐ and hyperglycemia, and a higher TIR reduces the risk of micro‐ and macrovascular complications. Since the insulin need in patients with T2D has continued to increase, it is likely that the use of CGM will become the standard of care for people with both T1D and T2D. It is also likely that many people with pre‐diabetes (T1D and T2D) could be detected before overt deterioration of glucose control and the risk of diabetic ketoacidosis (DKA). One wonders if glucose could be considered a vital sign just like blood pressure and heart rate.
PARALLEL SESSION ‐ DECISION SUPPORT SYSTEMS
INCORPORATING EXPLAINABILITY AND INTERPRETABILITY INTO AI‐ENABLED DECISION SUPPORT SYSTEMS
1Oregon Health & Science University, Department Of Biomedical Engineering, Portland, United States of America, 2Oregon Health & Science University, Harold Schnitzer Diabetes Health Center, SW Pavilion Loop, United States of America
Artificial intelligence (AI) and the sub‐field of machine learning (ML) are yielding powerful tools that are beginning to impact the field of diabetes in a number of ways. ML algorithms are being trained to forecast glucose, to predict meal and exercise events, and utilized in decision support systems to make insulin dose recommendations. Larger data sets are now becoming available because of the ubiquity of commercial sensors and these data sets are being used to train new ML algorithms. A challenge in the use of ML algorithms in healthcare, is that the algorithms are oftentimes not interpretable or explainable. An algorithm with high interpretability means that the algorithm is adept at indicating the cause and effect relationship between an input and an output of the algorithm. An algorithm with high explainability is designed in such a way that it is possible to easily understand how an algorithm works and therefore why it provides a specific forecast or recommendation. In this talk, I will discuss how we are incorporating interpretability and explainability into an AI‐driven app‐based decision support tool called DailyDose that is used to provide insulin dosing and behavioral suggestions to people with type 1 diabetes using multiple‐daily‐injection therapy. Specifically, I will review several decision support approaches: (1) a rule‐based system, (2) a k‐nearest‐neighbor approach and (3) a digital twin approach. I will discuss the strengths and weaknesses of each of these approaches as they relate to interpretability and explainability. I will show results from a recent clinical study on DailyDose that showed that glucose outcomes could be improved (6.3% increased time in range), but only when participants followed the recommendations provided by the app. A rule‐based and an AI‐based exercise decision support module within DailyDose will also be described with regards to interpretability and explainability. I will finally describe how the recommender engine in DailyDose compares with physician recommendations and how often the two agree.
PARALLEL SESSION ‐ DECISION SUPPORT SYSTEMS
ARTIFICIAL INTELLIGENCE AND DECISION SUPPORT SYSTEMS
1Oregon Health & Science University, School Of Medicine, Portland, United States of America, 2Oregon Health and Sciences University, Biomedical Engineering, Portland, United States of America, 3Oregon Health and Sciences University, School Of Medicine, Portland, United States of America, 4Oregon Health & Science University, Department Of Medicine, Division Of Endocrinology, Portland, United States of America
Designing an integrated, scalable decision support and coaching platform for multiple daily injection therapy Artificial Intelligence (AI) based decision support tools offer great promise to improve the care for people with type 1 diabetes who use multiple daily injections. We designed and tested in a clinical study a smartphone app decision support tool, DailyDose. This system makes insulin dose adjustment recommendations once weekly driven by an AI‐based algorithm. In the pilot clinical study, we found some participants did not accept recommendations even when clinically indicated based on glucose patterns. We conducted interviews with participants at the completion of the study which indicated involvement of clinical diabetes care and education specialists and behavioral health experts may improve uptake and interactions with the decision support system. However, this type of care is costly and resource intensive. In order to ensure scalability of the decision support system, we have designed a follow‐up study whereby those participants not achieving glycemic goals with decision support app use alone would receive diabetes education and behavioral health support tailored to their needs. This approach may allow for greater scalability and effectiveness. This presentation will include discussion of (1) qualitative results from post‐study interviews with participants, (2) incorporation of these findings into the decision support‐app to improve usability and explainability, (3) development of a web portal for interaction of diabetes educators, behavioral health and diabetes providers with app users. These system updates are important to ensure people with type 1 diabetes are able to benefit fully from AI‐based decision support systems. Lastly, the design of the next phase multi‐site clinical study with DailyDose will be presented.
PARALLEL SESSION ‐ DECISION SUPPORT SYSTEMS
PATIENT REPORTED OUTCOMES IN CLOSED LOOP STUDIES
Stanford University School of Medicine, Pediatric Endocrinology & Psychiatry Behavioral Sciences, Stanford, United States of America
Closed loop (CL) automated insulin delivery leads to glycemic improvements yet there are mixed findings with regard to patient reported outcomes (PROs). PROs refer to the subjective experience of the person using CL and often include topics such as quality of life, satisfaction, and diabetes distress. Common methods for obtaining PROs are validated surveys and structured interviews or focus groups. This presentation covers the results from CL studies and real‐world publications with regard to PROs, why there are mixed findings (e.g., some studies show PROs improvements while others show no change), and how we can improve methods for PROs data collection in clinics and future studies.
PARALLEL SESSION ‐ TECHNOLOGY USE IN PREGNANCY
AUTOMATED INSULIN DELIVERY IN TYPE 1 DIABETES PREGNANCY ‐ ARE WE NEARLY THERE YET?
1University of East Anglia, Department Of Medicine, Norfolk, United Kingdom, 2Cambridge University Hospitals NHS Foundation Trust, Norwich, Cambridge, United Kingdom, 3Norfolk and Norwich University Hospitals NHS Foundation Trust, Diabetes And Antenatal Care, Norwich, United Kingdom
Despite increasing use of continuous glucose monitoring (CGM) and insulin pumps, the pregnancy glucose targets of >70% time in range (TIR 3.5‐7.8 mmol/L, 63‐140mg/dl) ) and mean glucose 6.0‐6.5mmol/L (108‐117mg/dl) is often only reached in the third trimester, which is too late for optimal neonatal outcomes. Outside pregnancy, hybrid closed‐loop (HCL) insulin delivery systems are associated with improved glucose levels and early data for T1D pregancy suggest feasibility of use at home and in hospital settings, including after corticosteroids, and during labour/birth. The Cambridge adaptive MPC, is the first interoperable HCL android app (CamAPS® Fx) compatible with several insulin pumps (mylife YpsoPump®, DANA Diabecare RS® DANA‐i®) and with Dexcom G6 sensors (G6, G7). It offers customizable personal glucose targets which can be tightened to keep pace with gestational changes in insulin pharmacokinetics and variations in insulin sensitivity and/or resistance. Randomised controlled trials are underway evaluating this and the Tandem t:slim X2®, and Medtronic 780G® HCL systems. Case reports on other systems including Diabeloop® (Dexcom G6 with Kaleido® insulin pump) and do‐it‐yourself artificial pancreas systems (DIY‐APS) are available. This session will summarize the progress of ongoing HCL studies and translation into antenatal care.
PARALLEL SESSION ‐ EXERCISE IN DIABETES
EXERCISE WITH AUTOMATIC INSULIN DELIVERY
Steno Diabetes Center Copenhagen, Clinical Science, Diabetes Technology Research, Herlev, Denmark
Physical exercise with type 1 diabetes is a challenge, regardless of whether the insulin is given as multiple daily insulin injections or insulin pumps. Current consensus guidelines for avoiding hypo‐ and hyperglycemia during and after exercise are available. Recent advances in diabetes technology have led to the development of automated insulin delivery (AID) systems for glycemic management of people with type 1 diabetes. However, little is known about their safety and efficacy around exercise, which can cause significant and often worrisome disruptions in acute glycemic control. Although consensus recommendations exist for exercise management with AID, the guidance is based on first‐generation AID systems. Therefore, it is unknown how best to use the latest diabetes technologies around exercise.
In this presentation, data from new and ongoing studies that have investigated different glucose management strategies for training with the different generations of AID systems will be discussed. In addition, possible future management options for spontaneous exercise during AID treatment will be discussed.
PARALLEL SESSION ‐ EXERCISE IN DIABETES
IS TECHNOLOGY USEFUL FOR BREAKING DOWN BARRIERS TO EXERCISE IN DIABETES?
1Stanford University, Pediatrics, Stanford, United States of America, 2Stanford, Quantitative Sciences Unit, Stanford, United States of America, 3Stanford, Management Science And Engineering, Stanford, United States of America, 4York University, School Of Kinesiology And Health Science, Toronto, Canada, 5Stanford, Medicine, Stanford, United States of America
Clinical exercise guidelines recommend that children should aim to achieve at least 60 minutes of moderate‐to‐vigorous physical activity (MVPA) daily, but many youths with type 1 diabetes (T1D) are falling short of these recommendations. For individuals with T1D, exercise and physical activity can lead to disturbances in glycemia without proper preparation and implementation of these strategies. Some common strategies include insulin dose adjustments and/or carbohydrate feeding to reduce the risk of hypoglycemia during exercise. In addition to barriers such as lack of motivation to exercise and fear of hypoglycemia, exercise interventions in adults with T1D have been shown to be acceptable and feasible to deliver. Our team at Stanford is currently implementing a structured telehealth exercise education program in newly diagnosed youth with T1D. The exercise pilot study is part of the larger Teamwork, Targets, Technology, and Tight Control 4T Study that started youth with new‐onset T1D on continuous glucose monitoring (CGM) technology, physical activity trackers, and exercise education approximately 1‐month after diagnosis. This study also examined the potential association between physical activity and glycemia on active days in youth with T1D. We present data from focus groups aimed at understanding the parental and youth experiences in exercise education after T1D diagnosis and also benefits and challenges with real‐world use of physical activity trackers.
PARALLEL SESSION ‐ EXERCISE IN DIABETES
PHYSICAL ACTIVITY WITH LONG AND ULTRA‐LONG‐ACTING BASAL INSULINS
University Hospital Bern and University of Bern, Department Of Diabetes, Endocrinology, Nutritional Medicine And Metabolism, Bern, Switzerland
The use of long and ultra‐long acting basal insulins exposes people who are physically active to insulin levels that are entirely different from normal physiology. Consequences involve exercise‐induced hypo‐ and hyperglycaemic excursions, alterations in substrate utilization and post‐exercise metabolism. Large variations in individual clinical needs (e.g. purpose for engagement in exercise) introduces additional complexity. However, several pro‐active strategies, including variation of exercise intensity, use of digital tools, pharmacological agents and nutritional strategies can help people on long and ultra‐long acting basal insulins achieving maximal benefits from being physically active.
PARALLEL SESSION ‐ EXERCISE IN DIABETES
AUTOMATED DETECTION OF MEALS AND EXERCISE EVENTS IN PEOPLE WITH DIABETES
Illinois Institute of Technology, Chemical And Biological Engineering, Chicago, United States of America
PARALLEL SESSION ‐ RESOLVING HYPOGLYCEMIA
LEARNINGS FROM THE HYPO‐RESOLVE PROJECT
1Radboud University Medical Center, Internal Medicine, Nijmegen, Netherlands, 2Maastricht University, Carim School For Cardiovascular Diseases, Maastricht, Netherlands, 3Maastricht university medical center+, Internal Medicine, Maastricht, Netherlands
Therapeutic insulin is lifesaving for many people with diabetes. However, despite 100 years of experience and many innovations, its use is still associated with elevated risks of hypoglycaemia, the burden of which impacts considerably on many aspects of daily life with diabetes. Hypoglycaemia remains a major barrier to achieving optimal glucose control, reduces quality of life, increases health care demand and costs, and is associated with cardiovascular events, cognitive decline and death. The Hypoglycaemia REdefining SOLutions for better liVEs (Hypo‐RESOLVE) project is a public‐private partnership that aims to increase our understanding of hypoglycaemia through a comprehensive multilevel approach in order to reduce the burden of hypoglycaemia. One of the activities is the construction of the Hypo‐RESOLVE database, which contains data on hypoglycaemia from 98 clinical trials on insulin treatment among 60,000 participants with type 1 or type 2 diabetes, analysis of which will reveal better insight into the consequences of and risk factors for different levels of hypoglycaemia. In addition, the embedded 10‐week Hypo‐METRICS study will examine the psychological, clinical and health‐economic impact of sensor‐detected low interstitial glucose values and its relevance as compared to patient‐reported hypoglycaemia. A large hypoglycaemic glucose clamp study, conducted among over 100 people with type 1 or type 2 diabetes, aims to reveal potential mechanisms underlying the association between hypoglycaemia and cardiovascular disease, focussing on inflammatory parameters and cardiac function. Qualitative research and a quantitative survey examine the widespread impact of hypoglycaemia on various aspects of quality of life, diabetes distress and related aspects in people with or affected by diabetes Finally, the basic science component of the project will reveal novel pathways of hypoglycaemia sensing, so as to better understand the pathophysiology of impaired awareness of hypoglycaemia. Data from Hypo‐RESOLVE will provide the evidence needed to solidify the current and widely adopted International Hypoglycaemia Study Group (IHSG) 3‐level classification of hypoglycaemia. Collectively, the outcomes of Hypo‐RESOLVE will advance our understanding of hypoglycaemia, so as to alleviate its burden and improve the lives of people with diabetes.
PARALLEL SESSION ‐ EMERGING TREATMENT OPTIONS FOR OBESITY AND TYPE 2 DIABETES
GLP‐1 ANALOGS FOR THE TREATMENT OF OBESITY
1University Medical Centre Ljubljana, Department Of Endocrinology, Diabetes And Metabolic Disease, Ljubljana, Slovenia, 2University of Ljubljana, Faculty Of Medicine, Ljubljana, Slovenia
The classic approach to obesity treatment is a “treat to failure” model. If patients fail to lose weight or regain lost weight, they progressively escalate in a stepwise fashion to more intensive therapies, from lifestyle/behavioural therapy to pharmacotherapy and bariatric surgery.
Weight loss that is associated with clinically impactful outcomes for most adiposity based chronic disease (ABCD) is 10 to 20%. The marked increment in efficacy of modern anti‐obesity medications (AOMs) permits the weight loss within this range of magnitude as a new treatment target. The first AOM that fully enables such “treat to target” approach is GLP‐1 receptor agonist (RA) semaglutide 2.4 mg.
The safety and efficacy of semaglutide was evaluated in The Semaglutide Treatment Effect in People with Obesity (STEP) Phase 3a clinical development program. STEP 2 enrolled patients with overweight or obesity with type 2 diabetes (T2D), while the remaining STEP studies (STEP 1, 3 and 4) enrolled patients with overweight and at least one weight‐related comorbidity or obesity without T2D. Semaglutide safely produces ≥10% placebo subtracted weight loss in 69.1‐75.3% of patients without T2D and in 45.6% of patients with T2D. The estimated treatment differences for semaglutide versus placebo were 10.3 to 17.4%. More than half of patients lost ≥15%, up to 35% of patients were able to achieve ≥20% weight loss as an adjunct to lifestyle. In STEP 5 semaglutide led to clinically impactful and sustained weight loss of 15.2% at week 104 in adults with obesity without T2D, along with improvements in weight related cardiometabolic risk factors. The ongoing study SELECT was designed to see if semaglutide may reduce the risk of having cardiovascular events in patients with prior cardiovascular disease and will be completed in 2023.
Significantly more females than males in STEP 1, 3 and 4, while in STEP 2, a distribution between females and males was more even. Understanding some inter‐sex related difference in efficacy and some sex‐specific effects of GLP‐1 RAs is important to further improve therapeutic approaches in obesity care. GLP‐1 RAs is being studied in women with obesity and polycystic ovary syndrome (PCOS). The ovulation rate and menstrual frequency improved with GLP‐1 RAs exenatide and liraglutide. Short‐term preconception intervention with exenatide resulted in an increase of natural pregnancy rates in overweight and obese women with PCOS. A pilot study showed that preconception treatment with liraglutide increased pregnancy rates for patients with PCOS undergoing in vitro fertilization who responded poorly to first line reproductive treatment. In another pilot study liraglutide was superior to testosterone replacement therapy in improving an overall health benefit in men with obesity‐associated functional hypogonadism after lifestyle intervention failed.
The great advances are being seen in the use of GLP‐1 receptor agonists in combination with multi‐agonist unimolecular peptides, such as glucose dependent insulinotropic polypeptide (GIP), gastrin, amylin analogue, and others. GLP‐1 RAs and other developing therapeutic tools will change the way clinicians approach obesity and the prognosis of many ABCDs.
PARALLEL SESSION ‐ EMERGING TREATMENT OPTIONS FOR OBESITY AND TYPE 2 DIABETES
UNIMOLECULAR MULTIAGONISTS (DUAL AND TRIPLE) FOR THE MANAGEMENT OF OBESITY AND CARDIORENAL RISK – AN UPDATE
Velocity Clinical Research, Clinical Research, Los Angeles, United States of America
The selective glucagon‐like peptide‐1 (GLP‐1) receptor agonists (e.g., semaglutide, dulaglutide) have gained an ever‐increasing prominence in the management of type 2 diabetes (T2D) and obesity, and several agents in the class have proven cardiovascular (CV) benefits. We are now moving into an era of incretin‐based multiagonists. These so‐called unimolecular multiagonists are single molecules that bind to and agonize 2 or more receptors. Recently, tirzepatide, a dual agonist of the glucose‐dependent insulinotropic polypeptide (GIP) and GLP‐1 receptors was approved by regulatory agencies in the U.S. and Europe. In the T2D phase 3 SURPASS clinical development program, tirzepatide at 5, 10 and 15 mg once weekly demonstrated unprecedented glycemic and weight control across the spectrum of the disease, exceeding that seen with once‐weekly semaglutide 1.0 mg (SURPASS‐2) and titrated insulin degludec (SURPASS‐3). In these studies, tirzepatide‐treated patients exhibited a dose‐dependent reduction in body weight exceeding a mean reduction of 10% in each trial, with up to approximately 60% of patients achieving ≥10% weight loss. In the recently published SURMOUNT‐1 study, assessing tirzepatide for weight management in persons with overweight or obesity (without T2D), participants lost an average of over 20% body weight at 72 weeks, with approximately 40% achieving greater than 25% weight reduction. The tirzepatide CV outcomes trial (SURPASS‐CVOT) is underway and scheduled to complete in 2024. A prespecified meta‐analysis of 7 randomized‐controlled trials assessing tirzepatide CV safety was recently published, demonstrating a 20% reduction in 4‐point MACE for tirzepatide versus comparators thereby indicating its cardiovascular safety. Other GIP/GLP‐1 receptor dual agonists as well as GLP‐1/glucagon receptor agonists are currently in clinical development for T2D, obesity and/or non‐alcoholic fatty liver disease. Additionally, a triple agonist (GIP/GLP‐1/glucagon receptor) recently reported encouraging 12‐week data in patients with T2D, with significant improvement in glycemic control and a mean reduction in body weight of approximately 10% at 12 weeks. It will be entering late stages of clinical development. As we look into the future, these important agents that address obesity and its many complications (including dysglycemia) should gain increasing prominence in our management of metabolic diseases. We await further weight loss data as well as data demonstrating potential cardiorenal benefits.
PARALLEL SESSION ‐ EMERGING TREATMENT OPTIONS FOR OBESITY AND TYPE 2 DIABETES
BARIATRIC SURGERY: AN UPDATE
FACS, Md, Colonel, United States of America
Instead of full utilization of glucose to produce 32 ATP, the signal limits energy production to 4 ATP with conversion of the remainder to lactate, glucose and fat.
While this mechanism that limits the conversion of glucose to ATP by mitochondria may be injurious today at a time of ready access to food, it could be an ancient adaptation that facilitated storage of fat in times of plenty. It could also be an underlying process in hibernation.
Based on these observations, our approach to T2D, obesity and the other expressions of the metabolic syndrome deserve review. Insulin resistance is not the cause of T2D.
PARALLEL SESSION ‐ JDRF SESSION ‐ CONTINUOUS KETONE MONITORING FOR TYPE 1 DIABETES
CLINICAL NEED FOR CONTINUOUS KETONE MONITORING INTEGRATED INTO CGM DEVICES (I.E. CGM‐CKM)
University of Melbourne, Dept. Of Medicine St. Vincent's Hospital, Fitzroy, Australia
In people with type 1 diabetes, diabetic ketoacidosis (DKA) is a medical emergency and a major threat to life. While a failure in insulin delivery would also be signalled by increasing glucose levels, there are other causes of elevated glucose levels which are less likely to be associated with ketosis e.g. carbohydrate ingestion covered by an inadequate bolus, or emotional stress. In addition, DKA can also present without hyperglycaemia. Therefore, ketone levels should be checked in the face of significantly elevated glucose levels or if the person has nausea or vomiting. The current standard of care is measurement of capillary blood ketones using a ketone‐capable meter. However, some people with type 1 diabetes may not check their ketones in a timely manner as not all meters have a ketone measuring function, and many people do not have in date ketone testing strips with them. Continuous glucose monitors are now the standard of care for glucose monitoring in people with type 1 diabetes in advantaged countries. These devices currently sense a single analyte only (interstitial glucose). Evidence indicates that interstitial ketone levels closely mirror those in blood. Incorporating a ketone sensor as part of a multianalyte platform which also senses glucose would overcome some of the limitations associated with our current approach to the early detection and management of ketosis. An ideal device would not increase the user's physical, emotional, or financial burden. The ketone component of the multianalyte sensor should act analogously to a car's airbag, sitting unobtrusively in the background for most of the time, and becoming evident and lifesaving under those (hopefully exceptional) circumstances when needed. A continuous glucose/ ketone sensor would be relevant to the general type 1 diabetes population and of particular importance for those with recurrent DKA; during an acute illness; those who are pregnant; those on an SGLT2 inhibitor; those on a low carbohydrate diet; or those undertaking high intensity exercise.
PARALLEL SESSION ‐ JDRF SESSION ‐ CONTINUOUS KETONE MONITORING FOR TYPE 1 DIABETES
SGLT INHIBITORS IN T1D: DKA RISK AND DKA RISK MITIGATION STRATEGIES, ESPECIALLY CGM‐CKM, TO ENABLE THERAPY USE FOR HEART AND KIDNEY HEALTH
University of Michigan, Internal Medicine/endocrinology, Ann Arbor, United States of America
Despite continous progress in the development and implementation of diabetes technologies (including automated insulin delivery systmes) into the clinical care for people with type 1 diabetes (T1D), only 20% T1D individuals meet the evidence‐based A1c target shown to prevent chronic complications such as renal and cardiovascular disease (CVD). Additionally, a solley intensive insulin management regimen is associated with residual challenges such as overweight/ obesity, high disease self‐management burden, substantial diabetes‐related emotional distress, fear of hypoglycemia. Diabetic kidney disease (DKD) remains the leading cause of end‐stage kidney disease (ESKD) in the USA and developed world despite improvements in glycemia management and the use of renin‐angiotensin system blockade, with incidence rates of 30‐40% in T1D. Also, heart failure has emerged as the most prevalent CVD complication in people with T1D, while DKD markedly increases the risk of CVD and heart failure, leading causes of increased mortality in T1D. For people with type 2 diabetes, sodium‐glucose cotransporter‐2 inhibitors (SGLT2i) have emmerged to effectively prevent CVD and DKD progression and associated severe outcomes including death. Whether similar results can be achieved in T1D remains unknown because traditionally people with T1D were excluded from the larger CVD and CKD outcome trials. Add‐on to insulin SGLTi therapy was shown to associate with significant glycemic, weight loss, and blood pressure benefits in several randomized clinical trials , and have been approved in Europe and in Japan for use in T1D. However, there are concerns about a causally increase in risk of diabetic ketoacidosis (DKA) with SGLT2i therapy in T1D. Background risk of DKA in the contemporary T1D population remains high, estimated at 5‐7%
PARALLEL SESSION ‐ JDRF SESSION ‐ CONTINUOUS KETONE MONITORING FOR TYPE 1 DIABETES
PRE‐CLINICAL DEVELOPMENT OF CGM‐CKM
QuLab Medical, R&d, Herzliya, Israel
The unmet need for ketone monitoring in the diabetic population is underscored by the rising incidence of Diabetic Ketoacidosis (DKA). Current solutions for ketone monitoring are expensive and cumbersome, mostly involving serial measurements of capillary blood. These solutions are insufficient for real‐time monitoring of ketone levels, which is required for reducing DKA incidence. Continuous Ketone Monitoring (CKM) wearable patches are under development by several groups, employing different approaches to measure levels of the most prominent ketone ‐ beta‐hydroxybutyrate (BHB) ‐ in the interstitial fluid (ISF). Most of these approaches rely on the enzyme BHB‐dehydrogenase to specifically oxidize BHB to pyruvate while generating the co‐factor NADH in the process, which is then electrochemically sensed. QuLab Medical has developed a novel minimally‐invasive intradermal patch platform for continuously monitoring multiple metabolites in parallel. We are in the process of developing a novel non‐enzymatic sensor for BHB sensing. Combining this sensor with a CGM in a single patch device is expected to greatly benefit T1D patients, providing them with multiple additional treatment options and empowering them to better monitor their condition and improve their overall health and well‐being.
PARALLEL SESSION ‐ JDRF SESSION ‐ CONTINUOUS KETONE MONITORING FOR TYPE 1 DIABETES
FEASIBILITY AND PERFORMANCE OF A CONTINUOUS KETONE MONITORING SENSOR
Abbott, Abbott Diabetes Care, Alameda, United States of America
Current methods of ketone measurement using urine or blood ketones do not indicate the onset of ketosis or ketoacidosis, rather confirm if it is already in progress. In the case of diabetes ketoacidosis (DKA), alerting the patient about an impending DKA would reduce the complications of DKA, including hospitalization or even prevent it.
Feasibility of a subcutaneous continuous ketone monitoring (CKM) sensor was demonstrated using β‐hydroxybutyrate dehydrogenase enzyme with a proprietary mediation chemistry in a FreeStyle Libre 2 sensor form factor. The in vitro performance of the sensor has been demonstrated up to 8 mmol/L, showing that the sensor responds linearly to the change in the concentration of ketone and with minimal variation between sensors. The first human study with participants on low carbohydrate diet demonstrated that subcutaneous ketone can be measured with these sensors, which tracks the capillary ketone levels over a 14‐day period with a single retrospective calibration. The ketone levels generated with low carbohydrate diet were limited to 2mmol/L.
For the CKM sensor to be viable, the sensor needs factory calibration, as the fingerstick calibration is impractical. Performance of a factory calibrated sensor in the FreeStyle Libre 2 form factor was evaluated in a clinical setting where the study participants (without diabetes) consumed exogenous ketone to generate elevated ketone levels. The sensor results were compared to venous blood ketone using Precision Xtra ketone test strips. The sensor responded quicky to the changing ketone concentrations and the lag time was about 4 minutes. The mean absolute difference between the sensor and the reference results was 0.3 mmol/L.
The integrated continuous glucose – ketone monitoring will leverage the FreeStyle Libre 3 form factor. This dual analyte sensor system is designed to continuously monitor glucose and ketones levels every minute, in one sensor.
Funding: This study was funded by Abbott Diabetes Care, Alameda, CA.
PARALLEL SESSION ‐ AID EXPERIENCE IN THE REAL‐WORLD
– IN FRENCH CHILDREN
Montpellier University Hospital, University of Montpellier, Department Of Endocrinology And Diabetes, Montpellier, France
A cohort of 120 pre‐pubertal French children, aged 6‐12 years, 45%F/55%M, with Type 1 diabetes since 5.1+/‐2.2 years treated by continuous subcutaneous insulin infusion since 4.4+/‐2.4 years, with HbA1c of 7.8+/‐0.6%, were included in a 18‐week randomized control study to assess 24/7 vs. evening and night Automated Insulin Delivery (AID) using Control‐IQ algorithm and were further followed‐up with free‐life 24/7 AID for up to 83 to 101 weeks. Under 24/7 AID with a mean of 94.1% of time in closed‐loop, the time in 70‐180 mg/dl target range (TIR) reached 67.5+/‐5.6% after 18 weeks (+12.5% vs. baseline), while time below 70 mg/dl (TBR) was 2.6% (95%CI 1.9‐3.6) (reduction by 41% from baseline) and mean HbA1c was reduced by 0.5%. TIR at night‐time was 77.2 +/‐7.7% and TBR 2.2% (95%CI 1.0‐3.7). During the follow‐up after 36 and 83‐101 weeks, similar glucose metrics were sustained with no occurrence of severe hypoglycemia or ketoacidosis. Meanwhile, 45% of children entered in puberty with no impact on glucose control. During a 13‐week period of closed‐loop interruption, TIR and TBR went back to baseline levels, whereas a later 13‐week confinement due to the COVID‐19 pandemic while Control‐IQ was active had no impact on TIR and TBR levels. Our data shows that AID using active Control‐IQ in free‐life provides sustained improvement of glucose control with no safety concern in children, including during the pubertal period.
PARALLEL SESSION ‐ AID EXPERIENCE IN THE REAL‐WORLD
AUTOMATED INSULIN DELIVERY IN BELGIUM IN REAL WORLD: A SPECIFIC MODEL COMING FROM THE PARADOX COUNTRY
CHU Liège, Liège University, Diabetes, Nutrition And Metabolic Disorders, Liège, Belgium
In Belgium, we have 3 Automated Insulin Delivery (AID) systems: the Minimed Metronic 780G system, the Tandem Basal or Control IQ and the Diabeloop.
The Belgian federal health authorities have allocated a specific fixed budget for new technologies in the management of diabetes.
This budget covers the reimbursement of some of these technologies but only through specific centers recognized as experts in the field and designated by these same authorities.
Reimbursement of these AID devices is only available to people with type 1 diabetes and according to certain criteria.
These devices are only implemented in the hospital setting (either during hospitalization or as an outpatient). Education is also provided by these hospital teams and the equipment is provided by the hospital.
There is therefore no intermediary service provider, as in some countries. The budget allocated by the authorities therefore covers the purchase of the equipment by the hospital, as well as the multidisciplinary staff required for education, according to a fixed reimbursement.
Finally, the reimbursement of these technologies by the authorities is conditioned by the obligation to carry out a real‐life study in order to judge the relevance of this type of treatment and to ensure the sustainability of the reimbursement in the future.
The objective of these studies will be briefly discussed.
Translated with
PARALLEL SESSION ‐ AID EXPERIENCE IN THE REAL‐WORLD
– IN THE UNITED KINGDOM
1University Hospitals of Derby and Burton NHS Trust, Department Of Diabetes & Endocrinology, Derby, United Kingdom, 2University of Nottingham, Translational Medical Sciences, Nottingham, United Kingdom, 3University Hospital of Leicester NHS Trust, Leicester Diabetes Centres, Leicester, United Kingdom, 4Liverpool University Hospitals NHS Trust, Department Of Diabetes & Endocrinology, Liverpool, United Kingdom, 5University Hospitals Birmingham NHS Trust, Department Of Diabetes, Birmingham, United Kingdom, 6King's College London, Diabetes, London, United Kingdom, 7St Helen's Hospital, Diabetes, Merseyside, United Kingdom, 8Portsmouth Hospitals University NHS Trust, Diabetes, Portsmouth, United Kingdom, 9University Hospitals Sussex, Diabetes, Brighton, United Kingdom, 10Harrogate and District NHS Trust, Diabetes, Harrogate, United Kingdom, 11Heartlands Hospital, Diabetes, Birmingham, United Kingdom, 12Good Hope Hospital, Diabetes, Birmingham, United Kingdom, 13County Durham and Darlington Foundation Trust, Diabetes, Durham, United Kingdom, 14Cambridge University Hospitals NHS Trust, Diabetes, Cambridge, United Kingdom, 15Manchester University Hospitals NHS Trust, Department Of Diabetes, Manchester, United Kingdom, 16Sheffield Teaching Hospitals NHS Trust, Department Of Diabetes, Sheffield, United Kingdom, 17Sandwell and West Birmingham Hospitals NHS Trust, Department Of Diabetes & Endocrinology, Birmingham, United Kingdom, 18Harrogate and District NHS trust, Department Of Diabetes, Harrogate, United Kingdom, 19Oxford University Hospitals, Department Of Diabetes & Endocrinology, Oxford, United Kingdom, 20University of Leicester, Leicester Diabetes Centre, Leicester, United Kingdom
AID experience in the real‐world in the United Kingdom Background Hybrid closed loop (HCL) technology automates insulin delivery and improves outcomes in people living with Type 1 diabetes. We report real‐world outcomes from adults with Type 1 diabetes with raised HbA1c despite insulin pump therapy and flash glucose monitoring. Methods A national clinical audit programme collected routine, anonymised clinical data submitted to a secure online tool. Reported outcomes include HbA1c, key glucose sensor metrics; Diabetes Distress Score; Gold Score; event rates (hospital admissions, paramedic callouts and severe hypoglycaemia) and user opinion of HCL. Results Follow up data were available from 520 HCL users; median age 40 (IQR 29‐50) years, 67% female, mean diabetes duration of 21 (IQR 15‐30) years, 85% white British. Baseline HbA1c 78.9 ± 9.1mmol/mol [9.4 ± 0.8%] reduced to 62.1 ± 9.1mmol/mol [7.8 ± 0.8%] at 5.1 (IQR 3.9‐6.6) months median follow up. Mean adjusted HbA1c reduced by ‐18.1mmol/mol (95% CI ‐16.5, ‐19.6; P < 0.001) [1.7% (95% CI 1.5, 1.8, P < 0.001)]. Time in range (3.9‐10mmol/l) increased from 34.2% to 61.8% (P < 0.001), time below range (<3.9mmol/l) reduced from 2.1% to 1.6% (P < 0.001). The proportion reporting diabetes‐related distress reduced from 69.0% to 22.5%(P = 0.001). Gold score reduced from 2.2 to 1.6 (P < 0.001). Almost all (96.3%, 549/570) would recommend HCL to others with diabetes; 94.7% (540/570) reported that the system had a positive impact on their quality of life. No significant increases in hospital admissions/paramedic callouts were found. Conclusion The NHS England pilot of HCL therapy led to substantial improvements in HbA1c, time in range and time below range over 5 months of follow up. The prevalence of diabetes related distress improved. Almost all reported a positive impact on quality of life and would recommend the use of HCL system to other people living with diabetes.
PARALLEL SESSION ‐ AID EXPERIENCE IN THE REAL‐WORLD
– REAL-WORLD ROLLOUT OF AUTOMATED INSULIN DELIVERY IN TYPE 1 DIABETES PREGNANCY
1Cambridge University Hospitals NHS Foundation Trust, Norwich, Cambridge, United Kingdom, 2Norfolk and Norwich University Hospitals NHS Foundation Trust, Diabetes And Antenatal Care, Norwich, United Kingdom, 3University of East Anglia, Norwich Medical School, Bob Champion Research And Education Building, Norwich, United Kingdom
Over the past 5 years there has been an unprecedented acceleration of diabetes technology use before and during pregnancies complicated by type 1 diabetes (T1D). The CONCEPTT trial established the benefit of Continuous Glucose Monitoring (CGM) for improving maternal glucose and reducing neonatal morbidity in T1D pregnancy. However, many women struggled to achieve and maintain the mean CGM glucose and time in range (TIR) targets required for optimal obstetric and neonatal outcomes. Automated insulin delivery systems have the potential to support women to safely achieve the pregnancy glucose targets from early pregnancy. This session will review the experimental and real‐world experience of using hybrid closed‐loop systems during T1D pregnancy. It will examine which commercially available systems are suitable for use during pregnancy and explore the opportunities and barriers to rolling out closed‐loop in routine antenatal care. It will report on healthcare professionals' views about the training needed for hospital teams to support effective rollout of closed‐loop systems including management of diabetic ketoacidosis (DKA) by emergency and maternity department staff, as well as inpatient hospital use following corticosteroids, and continuing closed‐loop during and after labour/birth. We will report on how pregnant women engage with closed‐loop and how its use during pregnancy affects their diabetes self‐management, pregnancy experiences, interactions with healthcare teams and quality‐of‐life.
PARALLEL SESSION ‐ CLOSED‐LOOP IN ACTION
CLOSED‐LOOP WITH ADJUNCT THERAPIES
Research Institute of the McGill University Health Centre, Experimental Medicine, Montreal, Canada
Automated insulin delivery systems improve glycemia in type 1 diabetes but daytime control remains suboptimal and carbohydrate counting is still needed. Glucose control could be improved and carbohydrate counting burden could be reduced with the addition of adjunct therapies such as pramlintide, SGLT2i, and GLP‐1. The amylin analogue pramlintide delays gastric emptying, suppresses nutrient‐stimulated glucagon secretion, and increases satiety in people with type 1 diabetes. Adjunct use of pramlintide with closed‐loop therapy improves glucose control during the day and has the potential to alleviate carbohydrate counting. SGLT2i inhibits glucose reabsorption in the kidney, which allows more glucose to be excreted in the urine and thus lowers blood glucose levels in an insulin‐independent manner. Adjunct use of SGLT2i with closed‐loop therapy improves glucose control during the day and night but increases ketone concentration and ketosis compared to placebo. Data on the adjunct use of GLP‐1 with closed‐loop therapy is lacking.
PARALLEL SESSION ‐ CLOSED‐LOOP IN ACTION
LEVERAGING BEHAVIORAL AND PHYSIOLOGIC PATTERNS COLLECTED FROM WEARABLE SENSORS AND A SMART‐HOME TO AUGMENT NEXT‐GENERATION CLOSED‐LOOP ALGORITHMS
1Oregon Health & Science University, Department Of Biomedical Engineering, Portland, United States of America, 2Oregon Health & Science University, Harold Schnitzer Diabetes Health Center, SW Pavilion Loop, United States of America
Wearable sensors and smart‐home based sensors are becoming ubiquitous but they have not yet been integrated into automated insulin delivery (AID) or decision support systems (DSSs). We present a new algorithm called BlockRQA that is used to identify patterns from multi‐variate, multi‐modal data collected from both wearable sensors and smart‐home sensors to identify behavioral patterns that can lead to negative health outcomes or other events important for glucose management. A total of 30 people with type 1 diabetes were recruited to be monitored for 4 weeks while wearing a CGM, an insulin pump, an Apple Smart Watch, and using a custom food and exercise tracking app while having their movement tracked with a beacon‐based smart home monitoring system called MotioWear (MotioSens, Portland OR). Twenty‐four participants had data that were usable for analysis of patterns. We found that BlockRQA was able to identify patterns that led to 61% of low glucose (<70 mg/dL) events on average. We found that meals could be anticipated 30‐60 minutes in the future when utilizing movement data from the smart home along with other physiologic data within the BlockRQA algorithm whereby an average of 46.2% of meals could be anticipated. We derived a hypoglycemia risk score that is defined as a prior‐conditioned ratio of likelihood of a pattern leading to low glucose (<70 mg/dL) relative to likelihood of a pattern leading to high glucose (>180 mg/dL). We also derived a meal anticipation score that is defined as a prior‐conditioned ratio of likelihood of a pattern leading to a meal relative to likelihood of the pattern leading to low glucose. The hypoglycemia risk score and the meal anticipation score may ultimately be used to increase the aggressiveness of insulin delivery for an AID algorithm in anticipation of a meal, or may be used to decrease insulin aggressiveness in anticipation of a behavioral pattern that has led to a problem event like hypoglycemia.
PARALLEL SESSION ‐ DERMATOLOGY IN DIABETES
SKIN AND THE INSULIN PUMP: NEW FINDINGS
University of Washington, Diabetes Institute, Seattle, United States of America
Insulin pump infusion set failure is a common problem in all age groups, yet many with long‐standing pump use have more problems with insulin flow. Skin pathology with insulin infusion has been studied with short‐term animal models, but never with long term human use. Our group assessed non‐invasive optical coherence tomography (OCT) and skin biopsy of 30 subjects using insulin pump therapy. OCT showed both dermal inflammatory changes and increased vascularity. Pump sites where the infusion set was removed immediately prior to biopsy, and 3‐day old infusion sets showed no differences compared to control skin with inflammation, fibrosis, vascularity, fat necrosis, and IGF1, and TGFβ binding. Eosinophilic inflammation was also common with pump sites, but not seen with the controls. We also saw a positive relationship between inflammation and insulin dose, and a negative correlation between inflammation and time‐in‐range on continuous glucose monitoring. This single study has numerous implications. First, we have shown it is possible to study skin pathology both non‐invasively and invasively in type 1 diabetes. Next, the implications of the findings need further study, particularly as they pertain to site failures and skin health after years of insulin infusion. The etiology of these findings need study, as insulin, the infusion set, or both could be involved. More skin data are needed for pediatric patients, multiple injections, and sensors.
PARALLEL SESSION ‐ DERMATOLOGY IN DIABETES
SKIN INTEGRITY, TIPS, TRICKS AND HACKS FOR SUSTAINED DEVICE USE
University of Colorado School of Medicine, Barbara Davis Center For Diabetes, Aurora, United States of America
Diabetes device components including insulin infusion sets, patch pumps, and continuous glucose sensors all involve adhesive patches adhered to the user's skin. Wear is often for an extended amount of time (3‐14 days), and requires continued, repeated exposure to chemical and mechanical agents. As a result, exposure to adhesives lead to acute and chronic skin problems that may impede comfortable use of diabetes devices. Skin complications from device use can range from contact irritation to contact allergy. Contact irritation causes direct damage to skin via chemical or mechanical agents, and results in a non‐immune inflammatory response. Contact allergy is a hypersensitization of the immune response to a chemical agent in the adhesive. It can be immediate (Type 1 hypersensitivity, IgE mediated) or delayed (Type 4 sensitivity T‐cell mediated). Contact allergy has been documented in response to isobornyl acrylate, colophonium, ethyl cyanoacrylate and N,N‐dimethylacrylamide. Different devices contain different agents in their adhesives. To minimize complications from contact dermatitis, clinicians should discuss prophylactic strategies with users, including site rotation, skin preparation and chemical/physical barriers. Insufficient adhesion can be addressed with overpatches and tackifiers. Sensor removal agents and techniques are important for healing. For contact allergy, it may be possible to use a skin protecting layer between the native adhesive and skin, however complete avoidance of the offending agent is often indicated. Overall careful skin care before, during, and after device application may reduce incidence of complications.
PARALLEL SESSION ‐ DERMATOLOGY IN DIABETES
LONG TERM SOLUTIONS FOR IMPROVING INFUSION SITE CHALLENGES FOR INSULIN PUMPS
American Institute for Medical and Biological Engineering, University Of California, Santa Barbara, United States of America
Background: Phenolic compounds that are used for stabilizing or preserving insulin formulations may cause harmful side effects within the human body. Specifically, it may explain “site loss” or unexplained hypoglycemia for type 1 diabetes patients using continuous subcutaneous insulin infusion (CSII). In this work, I will present our research to date on a bioinspired polyelectrolyte‐modified carbon electrode for effective electrooxidative removal of phenol from insulin and eventual incorporations into an infusion set of a CSII device.
Methods: To electrooxidize the phenol, we used a screen printed carbon electrode (SPE) that we modified with poly‐L‐lysine (PLL) to avoid passivation due to polyphenol deposition while still removing phenolic compounds from insulin injections. We characterized these electrodes using scanning electron microscopy (SEM) and electrochemical impedance spectroscopy (EIS) and compared their data with data from bare SPEs. We performed electrochemical measurements to determine the extent of passivation, and high‐performance liquid chromatography (HPLC) measurements to confirm both the removal of phenol and the integrity of insulin after phenol removal.
Results: Voltammetry measurements show that electrode passivation due to polyphenol deposition is reduced by a factor of 2X. HPLC measurements confirm a 10x greater removal of phenol by our modified electrodes relative to bare electrodes.
Conclusion: Using bioinspired polyelectrolytes to modify a carbon electrode surface aids in the electrooxidation of phenolic compounds from insulin and is a step toward integration within an infusion set for mitigating site loss.
PARALLEL SESSION ‐ WEEKLY INSULINS
ONCE WEEKLY INSULINS IN TYPE 1 DIABETES: SAFETY, EFFICACY AND DOES IT ADDRESS AN UNMET NEED?
University of California, Division Of Endocrinology And Metabolism, San Diego, United States of America
Once weekly basal insulin is being developed for both type 1 and type 2 diabetes. Although most of the studies with weekly basal insulin are looking at type 2 diabetes , there are a few studies looking at the effects of weekly basal insulin in type 1 diabetes treated with a multiple daily injection regimens. Both Lilly's BIF and NovoNordisk's Icodec appear to be safe and effective comparted to insulin glargine and/or insulin degludec in clinic trials (details will be shown and discussed at the presentation). The promise of once weekly basal insulin comparted to once daily basal insulin includes greater convenience, better adherence, improved quality of life, reduced burden of self‐management and easier for individuals in need of self‐care assistance. Is there an unmet need in type 1 diabetes? For individuals on “dumb” pumps and hybrid closed loop systems there Is no obvious need unless an individual feels the technology is burdensome and stressful. For many on multiple daily injections regiments currently doing well on the second generation basal insulins supported by a CGM may or may not enjoy the need for one less injection per day. There will be a subset of T1Ds were adherence with their current daily basal insulin is poor leading to an elevated A1c and TIR in which weekly basal insulin may help. The successful use of weekly basal insulin in T1D, as well as T2D will require an intensive education program to the people living with diabetes and their HCPs for the proper switching, initiation, titration and long term monitoring. Education and protocols will also be needed for acute situations where the dose of basal insulin must be significantly reduced or increased.
PARALLEL SESSION ‐ WEEKLY INSULINS
ONCE WEEKLY BASAL INSULIN FC (BIF): AN UPDATE ON THE TYPE 2 DIABETES CLINICAL DEVELOPMENT PROGRAM
Velocity Clinical Research, Clinical Research, Los Angeles, United States of America
Basal insulin Fc (BIF) is a once‐weekly basal insulin currently in phase 3 of clinical development. BIF is a fusion protein that combines a novel single‐chain variant of insulin with a human IgG2 Fc domain and is designed for once weekly administration. It has a half‐life of approximately 17 days due to slow absorption from the subcutaneous space, Fc‐Rn‐mediated recycling, reduced renal clearance, and reduced insulin receptor affinity (reducing receptor‐medicated endocytosis). Importantly, it has low mitogenicity potential and low immunogenicity risk. In phase 1 pharmacokinetic (PK) and pharmacodynamic (PD) studies, BIF demonstrated dose‐proportional PK with low between‐day and ‐subject variability and a flat peak‐to‐trough ratio (1.14) throughout the week after administration. PD results in these studies demonstrated dose‐dependent reductions in fasting glucose concentrations, consistent with PK results and with a once‐weekly dosing regimen. Two phase 2 studies in patients with type 2 diabetes (T2D) have been reported to date; one in patients previously treated with basal insulin (32‐week study) and one in in insulin naïve patients previously treated with oral agents (26‐week study). Both studies compared once‐weekly BIF to once daily insulin degludec, with change in HbA1c as the primary endpoint (non‐inferiority). Both trials demonstrated significant HbA1c reductions from baseline for both insulins, which were non‐inferior between BIF and insulin degludec. Rates of hypoglycemia were lower or similar with BIF versus insulin degludec and both insulin formulations were well tolerated with similar incidences of treatment‐emergent adverse events. Based on these positive data, BIF has entered phase 3 of clinical development. The phase 3 program, called QWINT, is assessing BIF in patients with T2D who are insulin naïve as well as patients previously treated with insulin. It is anticipated that initial phase 3 results will be available in late 2023 or early 2024.
PARALLEL SESSION ‐ CARDIOVASCULAR DIABETES
CARDIOVASCULAR OUTCOME TRIALS (CVOT): WHICH ROLE FOR RISK FACTORS CONTROL?
IRCCS MultiMedica, Diabetes Dept, Milan, Italy
Several studies suggest that, together with glucose variability, the variability of other risk factors, as blood pressure, plasma lipids, heart rate, body weight, and serum uric acid, might play a role in the development of diabetes complications. Moreover, the variability of each risk factor, when contemporarily present, may have additive effects. However, the question is whether variability is causal or a marker. Evidence shows that the quality of care and the attainment of the target impact on the variability of all risk factors. On the other hand, for some of them causality may be considered. Although specific studies are still lacking, it should be useful checking the variability of a risk factor, together with its magnitude out of the normal range, in clinical practice. This can lead to an improvement of the quality of care, which, in turn, could further hesitate in an improvement of risk factors variability.
PARALLEL SESSION ‐ CARDIOVASCULAR DIABETES
CAUSES OF CARDIOVASCULAR DISEASE AND INSULIN RESISTANCE IN TYPE 1 DIABETES
University of Colorado Anschutz Medical Campus, Barbara Davis Center For Diabetes, Aurora, United States of America
Despite remarkable progress in newer therapeutics and diabetes technologies for the management of type 1 diabetes (T1D), mortality in people with T1D still remains elevated. On average, the life‐span of people with T1D is reduced by 10 years in developed nations (unfortunately, life‐span is much shorter for people with T1D in developing nations); this is largely attributed to a higher burden of cardiovascular diseases (CVD). A number of large prospective studies have highlighted the importance of optimal glycemic control to reduce CVD risk in people with T1D. Besides glycemic control, optimal blood pressure and lipid management are paramount to CVD risk reduction in T1D. Studies have also shown that despite optimal control of three major factors (A1C, blood pressure, and lipids), CVD risk is still elevated in people with T1D, especially in overweight and obese individuals. The prevalence of overweight and obesity is increasing among T1D and it is associated with insulin resistance and heightened risk for cardio‐renal complications. Moreover, insulin resistance in normal‐weight individuals with T1D has been shown to have a higher prevalence of coronary artery calcification (CAC) score and progression of that CAC score. Mounting evidence is now implicating insulin resistance as an important and independent risk factor for CVD and CVD mortality in people with T1D. How can we reduce CVD risk in people with T1D? Clinicians must encourage all people with T1D to optimize A1C, blood pressure, lipids, and other CVD risk factors (e.g. smoking). Studies have documented clinical inertia in treating blood pressure and lipids, especially in adolescent and young adults with T1D. For example, in a recent study from the T1D Exchange Clinic Registry (USA) and German/Austrian Registry (DPV), only 4‐6% of young adults with T1D were receiving anti‐hypertensive or lipid‐lowering treatment despite elevated blood pressure and lipids. Recent clinical trials with GLP‐1RAs (glucagon‐like peptide‐1 receptor agonists) and SGLT‐2 (Sodium glucose transporter‐2) inhibitors have been shown to reduce CVD events and mortality in people with type 2 diabetes. Moreover, GLP‐1RA and a newer dual incretin agonist (Tirzepatide) has been shown to reduce weight significantly. However, these agents are currently not approved by the US FDA (Food and Drug Adminisration) and EMA (European Medical Agency) for managing T1D and reducing CVD risk in people with T1D. Hopefully in future, these agents will be evaluated in randomized controlled trials to establish their glycemic and non‐glycemic effects in people with T1D.
PARALLEL SESSION ‐ NON‐INVASIVE GLUCOSE MONITORING: REALITY VS. HYPE
NON‐INVASIVE GLUCOSE MONITORING: BREATH, A REALISTIC OPTION?
BOYDSense, R&d, Toulouse, France
Non‐invasive glucose monitoring (NIGM) refers to the measurement of glucose levels in the human body without puncturing the skin, drawing blood, causing trauma or pain. NIGM has become a desire of patients with diabetes ever since the search for a successful technique began about 1980 and has continued to the present time. Approaches that have been tried includes spectroscopic technologies like fluorescence, near‐infrared, mid‐infrared, stimulated emission/stimulated Raman, bio impedance, terahertz, and photoacoustic. It also includes other technologies like microwave/Radio frequency sensing, reverse iontophoresis and ultrasound. However, most of these NIGM approaches try to measure glucose levels in the skin and not in other compartments of the body. Measurement of Volatile Organic Compounds (VOCs) in breath has gained interest as an alternative approach. VOCs in exhaled breath are correlated to many disease areas, like diabetes (e.g. glucose, ketones), gastrointestinal related diseases (e.g. lactose intolerance), COPD and oncology. For the first product, we correlate VOCs concentrations in breath with glucose levels. The number of publications for diabetes and breath has grown throughout the years from 2 in 1995 to 342 in 2022. An accurate VOC breath analyzer would be a breakthrough in glucose monitoring as this solution would be non‐invasive, gentle, affordable, user‐friendly, less stigmatizing and would produce significantly less waste compared to existing finger‐stick blood glucose or continuous glucose monitoring systems. Several challenges must be overcome for a successful development of a miniaturized VOC breath analyzer to enable glucose monitoring. For instance, VOCs that are highly correlated to glucose levels/changes must be properly identified, measured with sufficient accuracy and precision and their biological relevance must be verified. The cost and size of such a VOC analyzer must be optimized since GC‐MS, the golden standard for chemical detection of VOCs, is a too costly and cumbersome method for a personal use. Additionally, an effective noise reduction system, working at the levels of the sampling module, the sensor and the algorithms must be developed to separate exogenous VOCs from endogenous VOCs. For regulatory agencies, measurement of VOCs in breath is a novel approach as well. Verification and validation of the breath analyzer that we have developed must include not only research studies, but also validation and longitudinal clinical studies. In view of these challenges, it is not surprising that no breath analyzer to monitor glucose has come to market yet. Nevertheless, a new and promising development of a breath analyzer is in clinical trials right now, most recently in a clinical study in patients with type 2 diabetes. The presentation will give an overview on breath VOC sensing, analyzer development and will discuss potentials and challenges.
PARALLEL SESSION ‐ NON‐INVASIVE GLUCOSE MONITORING: REALITY VS. HYPE
CONTINUOUS KETONE MONITORING
University Hospital Antwerp ‐ University of Antwerp, Endocrinology‐diabetology‐metabolism, Antwerp, Belgium
Continuous glucose monitoring has improved diabetes care, showing beneficial effects on time in range (70‐180 mg/dl), time below range, HbA1c, hospitalisations for acute complications, and quality of life. Monitoring of additional biomarkers such as ketones may offer further advantages. Ketones are being produced in conditions of insulin deficiency (e.g. in case of inadequate bolus/basal dosing, pen or pump failure), starvation or insufficient intake of carbohydrates (very low calorie diet), increased alcohol intake, during sick days, or when using sodium‐glucose co‐transporter‐2 inhibitors (SGLT2‐i). Treatment with SGLT2‐i has shown cardiorenal benefits in people with type 2 diabetes and in those without diabetes. However, in people with type 1 diabetes (T1D) increased ketone levels appear in up to 3‐4%. Even in people with T1D using a hybrid closed loop system, the danger of DKA is present, probably related to a reduction in total insulin delivery. Monitoring ketones is advised in these conditions, but in reality many at‐risk patients do not have ketone test strips at home. Continuous ketone monitoring (CKM) may facilitate earlier detection of ketones, thereby possibly reducing hospitalisations for diabetic ketoacidosis (DKA) in high‐risk people. In those developing DKA, CKM may potentially help to resolve this condition faster, and reduce in‐hospital length of stay. However, this remains to be proven. In people using hybrid closed loop (HCL) systems, successful integration of a CKM into an automated insulin delivery device will require novel algorithms also integrating data on ketone levels. Ketone‐specific alarms for high ketone levels, and predictive alarms and trend information will be useful features. The first‐in‐human results obtained in 12 volunteers of a CKM device were published in 2021 by Alva et al. The electrochemical sensor used wired enzyme to measure β‐hydroxybutyrate (BHB), the major pathologic analyte. This sensor delivered a linear response over the 0‐8 mM range with good accuracy and stability, both in vitro and in vivo, for 14 days. With a single retrospective calibration the mean absolute difference (MAD) for BHB concentrations <1.5 mM was 0.129 mM and 91.7% of the sensor results were within ±0.3 mM of the reference. For BHB ≥1.5mM the mean absolute relative difference (MARD) was 14.4%. Teymouran et al. reported data of a new real‐time CKM microneedle platform based on the electrochemical monitoring of BHB alongside with glucose. This sensor detects BHB based on the NAD‐dependent dehydrogenase enzyme and a selective low‐potential fouling‐free anodic detection of NADH using an ionic liquid‐based carbon paste transducer electrode. In vitro data showed that the sensor had a high sensitivity (with low detection limit, 50 μM), high selectivity in the presence of potential interferences, along with good stability. We performed an early feasibility study (NCT04782934), including 4 participants with T1D and 3 healthy volunteers investigating the safety of the YANG near‐infrared (NIR) spectroscopy multimetabolite sensor (developed by Indigo Diabetes nv, Belgium) which was implanted for 28 days. Exploratory data on accuracy were collected. Different protocols were performed to induce a broad range of glucose levels (glucose drink, from 40‐400 mg/dL, 2.2‐22.2 mmol/L) and ketones (ketone drink, up to 3.5 mM). NIR spectra for glucose and BHB levels analyzed with partial least squares regression were compared with blood values for glucose (Biosen EKF) and BHB (GlucoMen LX Plus). The implanted YANG sensor proved to be safe, well tolerated, and did not cause any infectious or wound healing complications. Six out seven sensors remained fully operational over the entire study period. Glucose measurements were sufficiently accurate (overall mean absolute (relative) difference MARD of 7.4%, MAD 8.8 mg/dL). MAD values were 0.12 mM for BHB levels. In summary, there is a compelling need for a device that can continuously monitor not only glucose, but also ketones. However, so far only limited data (in vitro, in healthy volunteers, but also in T1D) is available. In the future, CKM exerts great potential to reduce the risk of DKA, and potentially also allow people with T1D to be able to use SGLT2‐i for cardiorenal benefits.
PARALLEL SESSION ‐ NON‐INVASIVE GLUCOSE MONITORING: REALITY VS. HYPE
RAMAN NI‐BGM FROM CONCEPTION TO REAL WORLD CLINICAL DEVICE; ARE WE THERE YET?
RSP Systems, Administration, Odense C, Denmark
The quest for non‐invasive Blood Glucose Monitoring (NI‐BGM) intensifies: people/patients and their caterers want improved convenience, lower expense and less waste and in order to grow the industry needs ways of expanding the use of Glucose Monitoring. Years of failed attempts have disappointed stakeholders and thereby raised the barriers for realising a genuine solution.
To be successful, a NI‐BGM solution must now offer clear advantages over CGM, which use is currently expanding, both as a stand‐alone device, and increasingly integrated with insulin delivery.
Most attempts, broadly characterized as non‐invasive BGM, does not promise such advantage, and when applying more stringent criteria (good accuracy, stable calibration, touch only and no waste), only a few approaches appear relevant as depicted below:
Of those, Raman spectroscopy clearly leads. An often overlooked feature is, that a practical device must be be stably calibrated. To surpass CGM in that respect, calibration should be stable for two weeks or more. Stable calibration has recently been demonstrated by NI‐BGM devices using Raman spectroscopy. While Raman based devices have demonstrated the required performance and calibration stability, the associated low photon yield necessitates high yielding photonics, which currently limits applications due to cost. High power integrated VCSEL lasers and microspectrometers are about to change that, and next generation Raman devices are poised to rival CGM in performance, and surpass them in affordability, while delivering the ultimate convenience.
PARALLEL SESSION ‐ ADVANCES IN FULLY AUTOMATED CLOSED‐LOOP
AUTOMATED CONTROL MEETS BEHAVIOR: HUMAN‐MACHINE CO‐ADAPTATION OF THE ARTIFICIAL PANCREAS
University of Virginia, Center For Diabetes Technology, Charlottesville, United States of America
The first attempts to automatically regulate blood glucose levels in type 1 diabetes (T1D) via exogenous insulin were made between the 1960s and 1970s. Due to technological limitations of the time, applicability of all these pioneering efforts was rather limited, confining their use to inpatient settings. With the advent of less invasive and more accurate glucose sensors and insulin delivery methods, wearable automated insulin delivery (AID) systems were possible. After years of gathering clinical evidence and testing of system components and algorithms, the idea of a commercial product became a tangible reality. Now, more than 6 years after the U.S. Food and Drug Administration approved the first commercial AID system — the Medtronic MinimedTM 670G, we can claim that AID solutions are becoming standard of care. However, all systems on the market still represent hybrid closed‐loop (HCL) solutions that work best with premeal insulin doses. To understand why full closed‐loop (FCL) designs remain in early stages, we should consider that in health, glucose metabolism is tightly controlled by a hormonal network that rapidly compensates for any physiological and behavioral disturbance. This fast response cannot be achieved with a minimally invasive AID system where glucose measurement and insulin infusion are both performed subcutaneously. In such setup, there are significant delays in insulin absorption and action that make impossible to match physiological plasma insulin profiles. Under these conditions, an aggressive design can increase the risk for hypoglycemia due to controller‐induced insulin stacking. In control‐engineering terms, a way to circumvent these structural limitations is through feedforward actions, or in other words, to anticipate the effect of important disturbances and take preventive actions. Off‐the‐shelf AID systems recognize the need for anticipation, but the burden falls on the users who are expected to assess the total amount of carbohydrates for every meal and prompt prandial boluses. This naturally links system's performance to user's behavior since the achieved glycemic control will rely on user adherence to manual doses. This presentation will revolve around two approaches that aim to compensate for the current slow response to fast biobehavioral disturbances. We will introduce an Adaptive Biobehavioral Control (ABC) strategy that recognizes the need for bi‐directional human‐machine co‐adaptation. In this regard, ABC assists the person's adaptation to the AID system via information and risk assessment provided to the user while it adapts the AID system to the co‐dynamics of physiological and behavioral disturbances. Also, focusing on FLC, we will discuss how we can design an AID system that can anticipate user's behaviors by injecting data‐driven patterns of glycemic disturbances into its formulation. Insulin timing is the key, and timely biobehavioral adaptation can represent a viable means to take another step forward into the next generation of more effective AID solutions.
PARALLEL SESSION ‐ ADVANCES IN FULLY AUTOMATED CLOSED‐LOOP
CLOSING THE LOOP ON EXERCISE
York University, School Of Kinesiology And Health Science At York, Toronto, Canada
Many individuals living with diabetes are now on automated insulin delivery (AID) systems for the maintenance of their glycemia. While AID can effectively improve overall time in range and reduce hypoglycemia exposure relative to multiple daily insulin injections or standard pump therapy, management during times of increased physical activity remains a challenge with the current AID systems. This session will highlight current research programs that evaluate the efficacy and safety of current AID systems during and after exercise and will also provide evidence‐informed strategies to help maximize AID control during exercise. The evolution of possible future “exercise smart” AID systems, including the use of glucagon, will also be discussed.
PARALLEL SESSION ‐ ADVANCES IN FULLY AUTOMATED CLOSED‐LOOP
ULTRA‐RAPID INSULIN AND ANTICIPATORY ALGORITHMS
UMC ‐ University Children's Hospital Ljubljana, Department For Pediatric Endocrinology, Diabetes And Metabolic Diseases, Ljubljana, Slovenia
Glucose‐responsive automated insulin delivery has improved the management of type 1 diabetes. Several clinical studies evaluating different automated insulin delivery systems have demonstrated safety and efficacy of these devices in individuals with type 1 diabetes in all age groups. Users of automated insulin delivery systems, however, still experience the everyday burden of constant engagement with the device, including meal or exercise announcement. Premeal insulin dosing bolus is required to prevent postprandial glycemic excursion due to the pharmacokinetic and pharmacodynamic delay and comparatively slow insulin absorption from subcutaneous administration of insulin. Ultra‐rapid insulin formulations are continuously being developed and have the potential to further improve the efficacy and safety of automated insulin delivery systems, with the aim towards fully glucose‐responsive insulin delivery. Data from clinical trials have demonstrated encouraging glycemic outcomes with ultra‐rapid insulin formulations using various hybrid automated insulin delivery systems in adults and less in youth with type 1 diabetes. In this presentation, we will present contemporary data regarding ultra‐rapid insulin formulations use with automated insulin delivery systems in individuals with type 1 diabetes.
PARALLEL SESSION ‐ ADVANCES IN FULLY AUTOMATED CLOSED‐LOOP
FULLY‐AUTOMATED CLOSED‐LOOP CONTROL: CHALLENGES AND POTENTIAL SOLUTIONS
University of Virginia, Center For Diabetes Technology, Charlottesville, United States of America
All contemporary automated insulin delivery (AID) systems are “hybrid,” in that the user is expected to announce meals and initiate meal boluses, or anticipate physical activity and attenuate insulin delivery in advance. Fully‐automated Closed Loop is defined as an AID system that does
PARALLEL SESSION ‐ TIMES IN RANGES
TIR AND OTHER TIMES IN RANGES ARE BETTER THAN HBA1C AS METRICS FOR QUALITY OF GLYCEMIC CONTROL
Biomedical Informatics Consulting, Llc, Potomac, United States of America
PARALLEL SESSION ‐ FEAR OF HYPERGLYCEMIA
UPDATE ON GLYCEMIC TARGETS IN THE ICU
University of Washington, Diabetes Institute, Seattle, United States of America
We don't have evidenced‐based glycemic targets in the ICU for as the data to date has been unclear (except for cardiac surgery). Society recommendations suggest all patients, with or without diabetes, maintain glucose in the 140‐180 mg/dL range. These pragmatic targets consider glycemic safety (hypoglycemia) and concerns for hyperglycemia resulting in worse infection, wound healing, and inflammation. It has been noted repeatedly since 2002 (including with COVID patients) that those without diabetes have higher ICU mortality rates with hyperglycemia than those with diabetes. The reason for this is unclear. In 2020, Krinsley et al published a retrospective study where HbA1c levels were measured on each ICU patient starting in 2011. With over 5500 patients, it was shown those with HbA1c levels benefited from glucose levels 80‐140 mg/dL and had a 3.5X higher mortality with glucose levels above 180 mg/dL. However, for those with admission HbA1c levels >8%, those with glucose levels 80‐140 mg/dL had a 3X greater mortality than those with glucose levels above 180 mg/dL. A future study reported the exception to these observations were those with HbA1c levels less than 6.5% receiving insulin. For this population, mortality was almost twice as high with the well‐controlled glucose levels of 80‐140 mg/dL. While the etiology of these findings is not known, there are several speculatory hypotheses, including brain adaptation to pre‐admission glycemia. This also explains why those with, compared to without diabetes appear to be protected in the hospital from hyperglycemia. While the impact of these findings could result in “precision glycemia” for those admitted to the ICU, it is time to consider an appropriate randomized controlled trial to test these findings.
PARALLEL SESSION ‐ FEAR OF HYPERGLYCEMIA
AVOIDING HYPERGLYCEMIA FROM DIABETES ONSET
UMC, University Children's hospital Ljubljana, Endocrinology, Diabetes And Metabolic Disorders, Ljubljana, Slovenia
It is well known that children are more sensitive to dysglycemia and that hyperglycaemia in youth is associated with decreased executive functions, possibility to learn and remember. But even nowadays talking about diabetes is discussing hypo/hyperglycaemia. Usually more time is spent discussing hypoglyceamia and the danger of severe hypoglycamia and the use of glucagon already at diagnose, but also later in out patient clinic or other opportunityies We have many questionnaires discussing the fear of hypoglycamia, but almost never ask patients how much they know about the danger of hyperglyceamia. But on the other hand ‐ goals for good metabolic control are clear for all age groups ‐ we discuss time in range above 70% (glucose values from 3,9 ‐ 10 mmol/l) next to HbA1c of less than 7% (53 mmol/mol) or bellow also for children and adolescents, emphasising that glucose values should not exceed 14 mmol/l frequently (less than 5% daily), next to less than 1% of time spent bellow 3 mmol/l. Can we reach these goals? With modern therapy ‐ newest insulins, insulin pumps, continuous glucose monitoring, the percentage of children and adolecents reaching those goals is increasing in the last 20 years. Times of frequent severe hypoglycaemia belong to history. But still ‐ we know that parents carry the decisions about diabetes management for their children and this on short or long term can be related to anxiety, stress and depression and severe fear from hypoglyecemia and lead to metabolic detoriatation. Education is a powerfull tool for families of newly diagnosed childrenand later in life, so we must change the setting ‐ more and earlier in the first days should be discussed about hyperglycaemia. Putting the hypo on the second place can also reduce the fear of hyperglycaemia.
PARALLEL SESSION ‐ FEAR OF HYPERGLYCEMIA
FEAR OF HYPERGLYCEMIA IN PARENTS OF CHILDREN WITH TYPE 1 DIABETES
Schneider Children's Medical Center of Israel, Jesse Z. And Sara Lea Shafer Institute Of Endocrinology And Diabetes, National Center For Childhood Diabetes, Petah Tikva, Israel
Abstract OBJECTIVE: This study aimed to: (1) develop and validate a novel questionnaire for measuring fear of hyperglycemia among parents of children with type 1 diabetes (T1D) – the Hyperglycemia Fear Survey – Parent version (FoHyper‐P); (2) investigate correlations between parental fear of hyperglycemia and objective measures of glycemic control. RESEARCH DESIGN AND METHODS: A multi‐center, multi‐national study of 152 parents of children with T1D was conducted in three large diabetes clinics from Israel, Poland, and Greece. Inclusion criteria were parents of children aged 6‐16 years, at least 6 months from diagnosis, at least 3 months of CGM use and parental involvement in care. Parents filled the FoHyper‐P and the Hypoglycemia Fear Survey ‐ Parent Version (HFS‐P). Patient data were obtained via electronic medical records and informative questionnaires. RESULTS: Significant correlations were found between our new FoHyper‐P and the HFS‐P including total questionnaires scoring (r = 0.747, p<0.001), worries subscales (r = 0.735, p<0.001), and behavior subscales (r = 0.532, p<0.001). Significant correlations were also found between time in range (TIR) (r = ‐0.18, p = 0.029) and time above range and parental fear of hyperglycemia (r = 0.192, p = 0.02). Correlations were found between worry subscales, and HbA1C in the past year (r = 0.198, p = 0.014) and percent of hyperglycemia (r = 0.236, p = 0.004). A negative correlation was found between the worry subscale and TIR (r = ‐0.219, p = 0.008). CONCLUSIONS: The FoHyper‐P is a novel, validated tool for assessing parental fear of hyperglycemia which also correlates with objective measures. Integrating it into clinical practice can address an underestimated aspect of parental diabetes management, thus enabling better care for children with T1D.
PARALLEL SESSION ‐ TECHNOLOGY FOR THE HEALTHY AGING OF OLDER PEOPLE WITH DIABETES
WHY HEALTHY AGING WITH DIABETES IS A CHALLENGE?
Beth Israel Deaconess Medical Center, Medicine, Brookline, United States of America
There has been a significant evolution in technology to improve diabetes management over the past decade. Studies have shown that when used in appropriate patients, technology can ease the burden of self‐care and provide a sense of security in all adults with diabetes. With successful aging in both type 1 and type 2 diabetes, there is an increased opportunity and need to use technology for better and safer diabetes management in the older population. However, there are several age‐related factors that can act as barriers to the successful use of technology in older adults. Multiple medical comorbid conditions, including cognitive and physical decline, can make technology use challenging in this population. In addition, older adults with diabetes are a heterogeneous group with varying degrees of clinical, functional, and psychosocial characteristics that require individualized considerations. Finally, older adults at different stages of their life have different overall goals for health and quality of life, and require input from, not only patients, but also caregivers, to choose the appropriate technology. Thus, the barriers to successful technology use in older adults might be related to a combination of factors, including the patients themselves, their caregivers, their clinicians, and their healthcare system as a whole. To achieve optimal benefits while using technology and avoiding harm, it is imperative to choose the right technology with a targeted, but holistic, approach for older patients with diabetes.
PARALLEL SESSION ‐ TECHNOLOGY FOR THE HEALTHY AGING OF OLDER PEOPLE WITH DIABETES
ADVERSE OUTCOMES OF HYPERGLYCEMIA & HYPOGLYCEMIA IN OLDER PEOPLE WITH DIABETES‐ HOW SHOULD THE RECOMMENDED TARGET % TIR BE DETERMINED?
Sheba Medical Center, Divison Of Endocrinology & Metabolism, Tel‐Aviv, Israel
The prevalence of diabetes increase with age. It is estimated that 25‐30% of the population over the age of 65 have diabetes. As in the younger age group diabetes treatment aims at preventing the short and long term complications of the disease. However, in older age maintenance of cognitive function, physical capacity and prevention of dementia and disability become important treatment targets also. The lecture will present the data regarding the relationship between hyperglycemia, hypoglycemia and these diabetes complications that are important in older age. It will discuss the heterogeneity that exists in functional state and present current guidelines that recommend treatment targets be determined according to the health status of the individual. Using this data a framework for determining TIR in older individuals will be proposed.
PARALLEL SESSION ‐ TECHNOLOGY FOR THE HEALTHY AGING OF OLDER PEOPLE WITH DIABETES
THE USE OF HYBRID CLOSED SYSTEMS IN OLDER PEOPLE WITH DIABETES
University of Melbourne, Dept. Of Medicine St. Vincent's Hospital, Fitzroy, Australia
The number of older people living with type 1 diabetes is increasing. In an advantaged country such as Australia there are three times as many people aged 60 years or older living with this condition than aged 20 years or less. Older people are faced with unique challenges in managing their glucose levels. They vary widely in their functional state and have a higher prevalence of impaired hypoglycaemia awareness. Regardless, glucose control remains important because the adverse impact of both hypoglycaemia and hyperglycaemia are particularly pertinent to the older person living with type 1 diabetes. While Automated Insulin Dosing (AID) systems have been shown to improve glucose control in the general diabetes population and age has not impacted outcomes there have been few randomised‐control trials targeting older people with type 1 diabetes. Data from available studies indicate that use of AID systems results in a significant increase in time in range in older adults without an increase in hypoglycaemia risk, with real world observational data providing supporting evidence. However, these data are derived largely from older high‐functioning adults. Further AID trials are needed, including studies with more advanced systems that are tailored to address the needs of older people across the full spectrum of health including reduced vision, reduced manual dexterity, and impaired cognition.
SOME MORE HIGHLIGHTS IN DIABETES
WHY DO CGM PERFORMANCE ASSESSMENTS NEED MORE STANDARDIZATION?
Institut für Diabetes‐Technologie Forschungs‐ und Entwicklungsgesellschaft mbH an der Universität Ulm, Management, Ulm, Germany
Systems for continuous glucose monitoring (CGM) have become an essential tool in the therapy of people with diabetes that provides information to the patent and allows to calculate CGM derived parameters like TiR etc. Accuracy of CGM systems has since become a topic of discussion, promoting the mean absolute relative difference (MARD) as one of the key characteristics of a CGM system. Experiences from practice as well as from structured head‐to‐head studies, however, have shown that different CGM systems may exhibit considerable variations in clinically relevant accuracy parameters even if they claim similar MARD values. On the one hand, this shows that the MARD alone is inadequate to fully characterize the accuracy of CGM system, which has led to the proposal of several alternatives. On the other hand, it shows that the apparent accuracy of a CGM system can be highly dependent on various factors related to the design and evaluation of the respective performance study. Among these factors are the selection of study participants as well as the characteristics of the comparator measurements (e.g. range and rate of change) and their traceability. This leads to a broad range of reported accuracy and makes comparison of different systems indeed difficult because all aspects of the respective study design have to be taken into consideration which requires extensive background knowledge that cannot be expected from practitioners or users.
Given the importance of CGM in current diabetes therapy, reliable and transparent performance declaration is essential which can be reached by standardized and scientifically reasonable study procedures. In his talk, Dr. Freckmann will provide an overview on past and current efforts, including the work of the IFCC working group on CGM, to achieve this goal of standardization.
SOME MORE HIGHLIGHTS IN DIABETES
CAN HYBRID CLOSED LOOP AND/OR VERAPAMIL PROLONG ISLET SURVIVAL IN NEW ONSET TYPE 1 DIABETES? RESULTS FROM THE JDRF CLVER TRIAL
1Barbara Davis Center, Pediatric Endocrinology, Aurora, United States of America, 2University of Minnesota, Pediatric Endocrinology, Minneapolis, United States of America, 3Jaeb Center for Health Research, Diabetes, Tampa, United States of America, 4Jaeb Center for Health Research, Jaeb Center For Health Research, Tampa, United States of America, 5Stanford University, Division Of Pediatric Endocrinology, Department Of Pediatrics, Stanford, United States of America, 6Indiana University, Pediatric Endocrinology, Indianapolis, United States of America, 7Yale School of Medicine, Pediatrics, New Haven, United States of America, 8Children's Mercy Hospital, Endocrinology And Diabetes, Kansas City, United States of America, 9University of Colorado School of Medicine, Barbara Davis Center For Diabetes, Aurora, United States of America, 10University of California San Francisco, Pediatrics, San Francisco, United States of America, 11Children's Mercy‐Kansas City, Endocrinology, Kansas City, United States of America
SOME MORE HIGHLIGHTS IN DIABETES
THE USE OF SAP IN PREGNANCY: COMPARISON OF THE INITIATION OF THE TREATMENT BEFORE AND AFTER CONCEPTION
1University of Belgrade, Clinic For Endocrinology, Diabetes And Metabolic Diseases, Belgrad, Serbia, 2Faculty of Medicine, Clinic For Endocrinology, Diabetes And Metabolic Diseases, Belgrad, Serbia
Previous studies showed that achieving and maintaining optimal metabolic control remain a challenge during pregnancy complicated with type 1 diabetes (T1D). The International Consensus on Time in Range recommends increasing time in range (TIR) in pregnancy with T1D promptly and safely with a target glycaemia range of 3.5‐7.8 mmol/L and TIR >70%.
However, CONCEPTT, a large multicenter randomized controlled trial of real‐time continuous glucose monitoring (CGM) before and during pregnancy with T1D, indicate that women only achieved these targets towards the end of the third trimester.. However, it is suggested that even a 5% increase in TIR is associated with clinically relevant improvements in neonatal health.
Previous trials demonstrated the safety of sensor‐augmented insulin pump therapy (SAP), and its potential to improve glucose control in pregnancy, without increasing maternal hypoglycemia. Also, recent trials conducted in women with T1D who started SAP before pregnancy, including our study, showed an earlier significant reduction of HbA1c and improvement in TIR all over from prepregnancy to third trimesters.
Hybrid closed‐loop insulin pumps with automated insulin delivery based on CGM readings have not been approved for use in pregnancy. However, many women use it in preconception and during pregnancy, after discussion of risks and benefits. Recently, use of artificial pancreas (AP) with a model‐predictive control algorithm, in pregnancy with T1D, was associated with comparable glucose control and significantly less hypoglycemia than SAP therapy. Further trials are needed to identify suitable candidates for CGM, SAP and AP technology in pregnancy.
PARALLEL SESSION ‐ REMOTE TREATMENT OF DIABETES
POPULATION HEALTH MANAGEMENT IN THE DIGITAL DIABETES ERA
Children's Mercy Hospital, Endocrinology And Diabetes, Kansas City, United States of America
Few diabetes centers have implemented risk‐based approaches to differentiating care in the clinic. Yet clinicians and researchers recognize that individuals with diabetes do not all respond equally to various medical therapies (e.g., glucometer or CGM use; insulin delivery via pen, smart pen, insulin pump or automated insulin delivery system; non‐insulin medications for type 2 or type 1 diabetes, etc.). Psychologists also recognize that individuals with diabetes do not all respond equally to behavioral interventions designed to promote engagement with medications, glucose monitoring, physical activity or dietary interventions. Remote monitoring and mHealth tools for the first time allow clinicians to interact with patients' data and with the patients themselves between scheduled clinic visits. mHealth tools further allow clinicians to deliver behavioral interventions that are less time consuming, less expensive, and‐ in many cases‐ just as effective as in‐person interventions. Thus clinics have the opportunity to create an entire toolkit of interventions that, along with population health management software, can be used to deliver personalized care that drives precision engagement at population scale. The speaker will review the essential ingredients for delivering a scalable, flexible population health management program in the clinic: quality improvement methods, software that integrates data from many sources and highlights patients at risk, clinic‐owned mHealth app(s) that serve as a “clinic in the pocket” or a digital front door to the clinic, advanced predictive analytics, and a care delivery model that supports clinical micro‐encounters via telehealth. Examples of such systems for diabetes and neighboring chronic will be provided from the research literature.
PARALLEL SESSION ‐ REMOTE TREATMENT OF DIABETES
ECHO STUDY ‐ DELIVERING TELE‐EDUCATION ON DIABETES TO PRIMARY CARE PHYSICIANS IN UNDERSERVED AREAS
1Stanford, Pediatrics, stanford, United States of America, 2Stanford, Pediatrics, Palo Alto, United States of America, 3University of Florida, Pediatrics, stanford, United States of America, 4University of Florida, Pediatrics, Palo Alto, United States of America, 5University of Florida, Pediatrics, Gainesville, United States of America
Many people with diabetes do not receive care at a diabetes center. The ECHO model is a tele‐education program developed at the University of New Mexico. Teams at Stanford University and the University of Florida developed an ECHO Diabetes program to improve care provided by primary care providers to people with T1D and intensively managed T2D in California and Florida. Goals included addressing disparities and the urgent needs of complex patients across the lifespan. In this presentation, data will be reviewed on experiences with recruitment (with a focus on reaching those people with diabetes under‐represented in research), disparities experienced by people with diabetes in our study, primary care provider perspectives, use of diabetes coaches, and financial considerations of starting an ECHO Diabetes program.
PARALLEL SESSION ‐ REMOTE TREATMENT OF DIABETES
OUTCOMES OF CONTINUOUS REMOTE CARE IN PRE‐DIABETES AND TYPE 2 DIABETES
Georgetown University, Division Of Endocrinology, Metabolism And Diabetes, Washington, United States of America
Although telehealth existed and was widely implemented in the past, its potential and necessity was amplified in 2020 with the onset of the COVID pandemic. Many providers and patients shifted from periodic office visits to the use of episodic computer and telephone‐based contacts. Although somewhat useful, these interactions cannot take the place of data and laboratory assessments, and always preclude direct patient examination. The true potential for telehealth requires data inputs from the patient to the provider, including anthropometrics and personal device data, with subsequent provider assessment, support, and follow‐up therapeutic recommendations. Telehealth provides the opportunity to shift from an episodic encounter system (every 3‐6 months) for chronic disease management to a continuous remote care paradigm in which data are continuously collected and reviewed, and patient support and education, together with therapeutic interventions may be instituted as rapidly and as often as needed. Diabetes serves as an ideal intervention to be managed by this continuous remote care telehealth model. This presentation will describe one model of diabetes care delivery using nutrition as the primary intervention through telehealth with data on sustainability, durability and patient outcomes, in a research setting over 5 years and in a real world setting over 2 years.
PARALLEL SESSION ‐ NUTRITION AND FOOD TECHNOLOGIES
STRATEGIES FOR MITIGATING GLYCEMIC EXCURSIONS FOLLOWING UNANNOUNCED MEALS WITH EXISTING TECHNOLOGIES
ASST Cremona, Division Of Pediatrics, Pediatric Diabetes, Endocrinology And Nutrition, Cremona, Italy
Post‐prandial hyperglycemia can occur frequently, and it is still a challenge also in (advanced) hybrid closed loop (A‐HCL) systems users, both children and adults. Usually, meal announcements are manual inputs to the A‐HCL system, and to date no system in the market is able to manage unannounced boluses autonomously.
Some experiences exist using either larger or smaller amounts of carbohydrates. For example, a recent study from Italy found that children and adolescents with type 1 diabetes using an A‐HCL (MiniMed 780G) system can tolerate an unannounced snack containing 20 g of carbohydrate without excessive blood sugar fluctuations. This may be particularly helpful for young children who are still not autonomous in providing insulin boluses through the pump when not assisted by an A‐HCL skilled caregiver (e.g., grandparents, schoolteachers, and babysitters).
We investigated the efficacy of another A‐HCL system (Control IQ) to contain post‐snack glycemia in 42 children and adolescents with type 1 diabetes. Participants underwent two 2‐h interventions involving midafternoon snacks (15 gr CHO and 25 gr CHO, respectively) eaten in random sequence without bolusing. Glucose values were significantly lower after 15 gr CHO snack (baseline vs. 2‐h, 161 ± 52 mg/dl vs. 136 ± 45 mg/dl, p = 0.008 with one‐sample Kolmogorov‐Smirnov test), while after the 20 gr CHO snack a significant increase was observed (152 ± 57 mg/dl vs. 172 ± 73 mg/dl, p = 0.028). Correction boluses were used in 21/43 patients (15 gr CHO snack) and in 30/42 patients (25 gr CHO snack). Although there is always the need to reinforce the importance of bolusing before meals, in selected circumstances, actual A‐HCL systems pediatric users can eat unannounced snacks up to 15‐20 g of carbohydrate, without causing a glycemic excursion.
Further studies are needed in larger cohorts for bigger unannounced meals, with any starting glycemia, and with other A‐HCL systems.
PARALLEL SESSION ‐ NUTRITION AND FOOD TECHNOLOGIES
COMPLEX MEAL HANDLING WITH ADVANCED HYBRID CLOSED‐LOOP SYSTEM
Sheba Medical Center, Division Of Endocrinology, Tel‐Hashomer, Israel
PARALLEL SESSION ‐ NUTRITION AND FOOD TECHNOLOGIES
WHAT HEALTHCARE PROFESSIONALS AND END‐USERS NEED IN IMAGE‐BASED NUTRITION APPS
University of Bern, Artorg Center For Biomedical Engineering Research, Bern, Switzerland
Digital technologies have advanced rapidly in recent years and smartphones apps are increasingly used for different purposes, including health monitoring. Dietary assessment is critical for the prevention and treatment of nutrition‐related diseases, including diabetes. There are numerous nutrition and diet apps available, and some of them are very popular in terms of user downloads, indicating a trend towards digital diet monitoring and assessment. However, the opinions of users and healthcare professionals (HCPs) who recommend nutrition apps have not been studied in detail. Preferences for nutrition app features, current use, and predictors of adoption from the perspectives of HCPs and end‐users will be shared in this presentation. One interesting finding is that nearly a quarter of HCPs who have not yet recommended a nutrition app to their clients/patients are unaware of such apps' existence. Easy to use, free, validated apps that can automatically estimate calorie and macronutrient content are highlighted as important factors for recommending an app for HCPs. The use of inaccurate food composition databases, lack of local food composition database, and tech‐savviness. were significant barriers for HCPs. The respective barriers for end‐users were incorrectly estimated portion size, and nutrient content and the usage of database that does not include local foods. Although smartphone penetration is increasing and mobile health research is progressing, there is still room for improvement in HCP's recommendations and end‐users' selection criteria of nutrition apps. Understanding user needs will assist researchers in the fields of digital dietary assessment and nutrition‐related behavioral change, as well as computer scientists and AI experts who design, develop, or optimize such apps.
PARALLEL SESSION ‐ NUTRITION AND FOOD TECHNOLOGIES
MOBILE APPLICATIONS FOR PERSONALIZED NUTRITION
Schneider Children's Medical Center of Israel, The Jesse Z. And Sara Lea Shafer Institute For Endocrinology And Diabetes, National Center For Childhood Diabetes, Petah Tikva, Israel
Preventing postprandial glucose elevations in people with diabetes (PWD) is critical in achieving tight glucose control. Treatment with an insulin pump and continuous glucose monitoring (CGM) or with automated insulin delivery systems enables tighter glucose control, trying to reach the goal of 70% of the time in the range of 70‐180 mg/dl. However, both treatment modalities demand pre‐meal bolusing by the patient according to the meal content.
Since carbohydrates are the macronutrient that primarily affects blood glucose, guidelines for diabetes treatment recommend carbohydrate counting as an effective strategy for achieving glucose control. Therefore, nutritional education given to PWD focuses on counting carbohydrates and dosing insulin accordingly. Nonetheless, most people with diabetes underestimate the amount of carbohydrates they eat and do not account for other food composites such as protein, fat, and fibers. Therefore, prandial insulin doses are usually inaccurate.
Smartphone apps can aid with meal insulin dosing. Nutritional apps for PWD are mainly food journals and food composition databases. More advanced apps use artificial intelligence to analyze a meal photo to collect information about its composition, while others suggest personalizing nutrition according to the person's microbiome. This data and data from insulin pumps and CGM devices may help understand the effects of different food and meals on blood glucose levels. Such applications are needed and should be an integral part of nutritional consultation, helping to personalize the diet for PWD.
PARALLEL SESSION ‐ DESPAIR AND SELF‐HARM & DIABETES TECHNOLOGY
UNDERSTANDING CLINICAL RELEVANCE ON A PRO
Spotlight‐AQ Ltd, R&d, Fareham, United Kingdom
It has long been recognized that a biopsychosocial approach to diabetes management is required for optimal health outcomes. In 1948, the World Health Organization (WHO) defined health beyond the absence of disease or infirmity to include ‘a state of complete physical, mental and social well‐being’ [who‐definition‐of‐health‐1.jpg (888 × 665) (publichealth.com.ng)]. Furthermore, the WHO constitution states the enjoyment of the highest attainable standard of health is one of the fundamental rights of every human being without distinction of race, religion, political belief, economic or social condition [Constitution of the World Health Organization (who.int)] The challenge lies in the delivery of healthcare that achieve these goals. Many patient‐reported outcome measures have been developed for use in diabetes, however their quality is variable. Furthermore, some measures are designed for use in clinical trials rather than in clinical practice and it is often not possible to determine what represents a meaningful difference of improvement or otherwise. In 2020, the FDA qualified the first PRO for use specifically in diabetes. This milestone represented an achievement of parity of esteem between standardized, rigorous assessment of a physical health outcome and a mental health outcome. Translating that standard into routine clinical care is necessary to enable healthcare professionals to effectively support their patients in optimal self‐management of their diabetes. This presentation will provide clarity on what represents a patient‐reported outcome, why that is important, what the underpinning science is pertaining to PROs, including mechanism of action and improvements in physical or mental health outcomes.
PARALLEL SESSION ‐ PRACTICAL ISSUES IN DAILY LIFE OF PEOPLE WITH DIABETES
THE ISSUES WITH USING CE MARK AS A VALID PROXY FOR CONTINUOUS GLUCOSE MONITORING SYSTEMS ACCURACY FOR PEOPLE WITH TYPE 1 DIABETES
Birmingham Children's Hospital, Department Of Endocrinology And Diabetes, Birmingham, United Kingdom
In March 2022, the National Institute for Clinical Excellence (NICE) updated the United Kingdom (UK) diabetes guidance for adults (1) and paediatrics (2). The updates will make real‐time continuous glucose monitoring and intermittently scanned continuous glucose monitoring (defined as CGM forthwith) accessible for all people living with type 1 diabetes. The NICE guidance lays out multiple factors when choosing a CGM device. Prudently, accuracy is the first factor; however, there are no internationally recognised design or performance criteria for accuracy studies, and NICE has called for a review (2). The updates also state, 'if multiple devices meet their needs and preferences, offer the device with the lowest cost' (1,2), making the UK market attractive to manufacturers with existing or prospective CGM devices with a range of prices and varied sensor accuracy, reliability and user‐friendliness. The UK Medical Device Regulations (MDR) 2002 (3) brought into effect the Conformité Européenne (CE) marking regulations from the 1993 European Union (EU) Directive 93/42/EEC (4). Therefore, CE marking indicates the medical device is fit for purpose and no pre‐market assessment is required by the UK patient safety regulator, the Medicines and Healthcare products Regulatory Agency (MHRA) (3). Therefore, evaluating the robustness of the CE marking process is essential. This lecture starts by outlining the landscape of CGM accuracy detailing the available CGM performance metrics and explaining the rationale for focusing on manufacturer sponsored studies submitted for CE marking. The lecture goes on to describe the current regulatory framework for CE marking in the EU, in contrast to the United States of America (USA) Food and Drug Administration (FDA) and Australian Therapeutic Goods Administration (TGA) processes. Furthermore, the review highlights the limitations of the CE marking system compared to the FDA and TGA approval processes by appraising the clinical data requirements. The lecture discusses the acceptance of a representative sample of clinical data and studies using a device of equivalence for CE marking, resulting in wide‐ranging indications for use that stretch beyond the available clinical data. Also, the review identifies the challenge in verifying if data 'on file' submitted by manufacturers is robust, considering the data misses the peer‐review process required for a scientific journal publication. In contrast, the lecture references FDA and TGA approval documents showing the approved indications for use mirror the available clinical data. The FDA published the integrated CGM (iCGM) study design and performance criteria to speed up approval and allow interoperability in 2018. The iCGM criteria define the standards required for CGM approval with manually operated digital technologies and automated insulin delivery systems (AID). Evaluating the CGM devices available in the UK against the iCGM criteria identifies several studies that used protocols that minimise glucose variability. Performance in the hypoglycaemic range did not meet the standards or was missing for many devices. The lecture discusses the inadequacy of using full glucose range accuracy metrics such as mean absolute relative difference (MARD. Finally, the lecture discusses opportunities and risks for UK medical device safety from 2025 because of leaving the EU. The main finding is the USA FDA and Australian TGA approvals are valid proxies of CGM device accuracy for the indicated populations due to the using the highest risk classification with specific assessment criteria that requires comprehensive product‐specific clinical data. The CDRH of the FDA and Advisory Committee of the TGA complete conformity assessments against regulatory requirements as governmental entities, ensuring consistency without conflicts of interest. However, the time taken to receive FDA and TGA approval risks hindering innovation. In contrast, CE marking does not appear to be a valid proxy for the accuracy and performance of CGM devices. Multiple Notified Bodies perform conformity assessments against EU regulations without standardised criteria risking inconsistency of assessment, and their employment by the manufacturer introduces a potential conflict of interest. Publication and transparency in the clinical data that justified the CE marking would be welcome. CE marking for AID requires an urgent appraisal. The CGM‐specific study design and accuracy criteria for iCGM approval offer a starting point for the standardisation of CGM Accuracy. However, more work is required to develop clear design verification standards and performance metrics depending on the CGM device category, for which the IFCC working group on CGM may provide. In the absence of standardised assessment criteria, the lecture offers an overview of considerations for the critical appraisal of study design, reporting, and performance of a CGM accuracy study to support stakeholders involved in the decision‐making process. If unable to complete a full critical appraisal, when presented with a MARD of ∼10%, one could enquire, 'Did the study include participants in sufficient numbers with demographics similar to those I look after, did the protocol induce glycaemic variability on the test days, and what is the percentage of readings withing 15/15 agreement rates in the different glucose ranges?' If the UKCA marking system tightens regulation, there are justified grounds to suggest more stringent standards will hinder innovation and slow access to the latest technologies. Fro example, in the USA and Australia, where regulation is more robust than CE marking, the time required to gain approval is estimated to be double that of CE marking. Also, the CE marking system supports commercialisation of markets that can drive down prices and increase optionality for users and health care professionals. A solution could be for the UKCA marking system to implement study design criteria that apply to all CGM devices, with varying accuracy performance standards for different CGM device categories.
References
1. NICE (National Institute for Health and Care Excellence). Recommendations | Type 1 diabetes in adults: diagnosis and management | Guidance | NICE [Internet]. NICE. 2015 [cited 2022 Jun 8]. Available from:
PARALLEL SESSION ‐ PRACTICAL ISSUES IN DAILY LIFE OF PEOPLE WITH DIABETES
HELPING ADULTS CHOOSE A SAFE AND EFFECTIVE CGM IN LIGHT OF NEW NATIONAL GUIDANCE
1University Hospitals of Derby and Burton NHS Trust, Department Of Diabetes & Endocrinology, Derby, United Kingdom, 2University of Nottingham, Translational Medical Sciences, Nottingham, United Kingdom
There is growing evidence to support the use of continuous glucose monitoring in people living with diabetes. As such, in the UK the National Institute for Clinical Excellence (NICE) now recommend that all people living with Type 1 diabetes, and sub‐groups of people with insulin treated Type 2 diabetes should have access to interstitial glucose monitoring. In those with Type 1 diabetes the choice should ideally be based on individual preferences, considering the needs, characteristics, and functionality of the devices. Some of the factors to be considered for individuals with Type 1 diabetes include device accuracy, predictive alerts/alarms, dexterity, hypoglycaemia fear, psychosocial factors, need for use as part of a closed loop, calibration, data sharing, hypoglycemia awareness and cost. This lecture will consider the practical considerations above, alongside available evidence to support informed, collaborative decision making when deciding on the optimal continuous glucose monitoring solution.
PARALLEL SESSION ‐ PRACTICAL ISSUES IN DAILY LIFE OF PEOPLE WITH DIABETES
IS IT EASY TO USE TIME IN RANGE (TIR) FOR DAILY MANAGEMENT OF DIABETES?
University of Leeds, Leeds Institute Of Cardiovascular And Metabolic Medicine, Leeds, United Kingdom
This presentation attempts to compare and contrast two key glycaemic markers: the old and the new, represented by glycated haemoglobin (HbA1c) and time in range (TIR), respectively.
HbA1c has been used as the gold standard for assessing glycaemic control in diabetes, given the wealth of data linking this glycaemic marker to future vascular complications. While HbA1c served us well, this glycaemic marker is not without limitations as accuracy can be affected by factors that influence red blood cell lifespan. Moreover, HbA1c fails to provide information on hypoglycaemic exposure and glycaemic variability, which are both associated with adverse outcome. HbA1c is also slow at assessing response to new glycaemic therapies, which can delay optimisation of glucose levels.
These were accepted limitations for HbA1c in the absence of a credible alternative. However, with the increased use of continuous glucose monitoring, additional glycaemic markers have surfaced that can address the shortcomings of HbA1c.
One of these modern glycaemic markers is TIR, depicting the time spent in glucose levels between 3.9 and 10 mmol/l (70‐180 mg/dl) each day, and showing an inverse relationship with the risk of diabetes complications. While use of TIR is not as widespread as HbA1c, it is easy to understand and well accepted by health care professionals as well as individuals with diabetes. Importantly, TIR gives an unbiased account of glucose levels, and, unlike HbA1c, is not affected by red blood cells changes, while rapidly assessing response to the introduction of new glycaemic therapies.
Despite the clear advantages of TIR, HbA1c will continue to serve the diabetes community for a while. However, this old friend will need help from the new generation of glycaemic markers for optimal glucose management in people with diabetes.
PARALLEL SESSION ‐ FIGHTING DISPARITIES
FIGHTING DISPARITIES: DIFFERENT WORLDS
Portsmouth NHS Trust, Diabetes, Southsea, United Kingdom
Diabetes care has continued to progress around the world‐ with an explosion of new drugs, insulin, technology etc. Yet a big challenge continues to be access to those who need it most‐ whether it be based on deprivation or ethnicity In this session‐w e will look at 2 different funded health systems‐ namely UK and India‐ and see where the similarities end‐ yet also are quite close to each other. we will explore means to try and bridge gaps, methods being used‐as well as ideas about taking things forward.
PARALLEL SESSION ‐ FIGHTING DISPARITIES
BRIDGING DISPARITIES IN TYPE 1 CARE IN THE US
1Stanford University, Pediatrics, Palo Alto, United States of America, 2Stanford University, Pediatrics, stanford, United States of America
The American Diabetes Association and the International Society of Pediatric and Adolescent Diabetes advocate (evidence grade level A) for the use of automated insulin delivery for all people with type 1 diabetes and other types of insulin‐deficient diabetes who are capable of using the device safely. Data from programs designed to identify and reduce disparities in care for people with type 1 diabetes in the US will be reviewed.
PARALLEL SESSION ‐ FIGHTING DISPARITIES
ADVOCACY AND ADOPTION OF TECHNOLOGY AND DISPARITIES IN INDIA
Madras Diabetes Research Foundation & Dr. Mohan's Diabetes Specialties Centre, Diabetology, Chennai, India
The number of people with diabetes globally, is rising at an alarming rate. South Asia is one of the hot spots of the diabetes epidemic. In India alone, there are over 74 million people with diabetes today. Unfortunately, 70% of the doctors in India practice in urban areas while 70% of India's population lives in rural areas. This mismatch between the availability of health care professionals and the rapid spread of diabetes in rural areas, provides an opportunity to use technology to deliver the diabetes care to remote rural areas. The first part of this presentation will talk about a model of successful delivery of diabetes health care in rural India. The Chunampet Rural Diabetes Program was carried out in a group of 42 villages in Kancheepuram District in Tamilnadu. Using a Mobile van, a population of 27,014 individuals (86.5% of the adult population) were screened for diabetes. All those detected with diabetes were offered a follow up care at a rural diabetes centre which was set up during the project. The results were very impressive and led to good improvement in A1c levels using low cost generic drugs. The second use of technology was during the COVID – 19 pandemic and the lock down which was enforced in India and many other countries. Thankfully, Telemedicine was also legalized in India at that time. Using technology, a system was created whereby the doctor and the patient stayed at home but blood tests were arranged at home for the patient. With the results, teleconsultation was done by doctors using the Electronic Medical Records which were made available on their mobile phones. Thus, despite the lockdown, patients managed to get their tests and diabetes consultations done remotely. The third use of technology is through our network of diabetes clinics across India. Even at centres where there was no ophthalmologist, retinal photographs were obtained using a low‐cost retinal camera and were uploaded for centralized diabetic retinopathy grading unit where the images were read by trained retina specialists. The eye reports were sent back to the peripheral clinics in real time. Over one year period, 25,316 individuals with diabetes could have their eyes screened for diabetic retinopathy. Only 11.4 % needed referral to an ophthalmologist for further management. Finally, the use of mobile Apps has revolutionized diabetes treatment. Recently, we have developed three diabetes related tools.
PARALLEL SESSION ‐ JDRF SESSION ‐ MONITORING PRE‐SYMPTOMATIC TYPE 1 DIABETES: WHICH TECHNOLOGY FOLLOWS AUTOANTIBODY DETECTION?
THE STRENGTHS AND LIMITATIONS OF MONITORING VIA OGTT
University of Florida, Pediatrics, Gainesville, United States of America
We will review the strengths and limitatoins of monitoring progression to Stage 3 T1D via OGTT.
PARALLEL SESSION ‐ JDRF SESSION ‐ MONITORING PRE‐SYMPTOMATIC TYPE 1 DIABETES: WHICH TECHNOLOGY FOLLOWS AUTOANTIBODY DETECTION?
THE EVIDENCE FOR ALTERNATIVE MONITORING TECHNOLOGIES
University Hospitals Leuven ‐ KU Leuven, Endocrinology, Leuven, Belgium
When it comes to monitoring glucose, the last 50 years have seen a revolution, in particular when it comes to allowing people living with diabetes to measure their glucose levels themselves. It was only in the 1980's that self‐monitoring of blood glucose became available broadly with the home blood glucose meters. These have seen increasing accuracy, but most importantly increasing user friendliness, with reduction of the size of the capillary blood volume needed, with the improved lancets for sampling, and with greater affordability, increasing thus the accessibility of the technology and allowing people with diabetes around the world to perform capillary blood glucose measurements. This is of the utmost importance those using insulin, as they need the guidance of blood glucose levels in their day to day decisions on insulin doses, but also for those not on insulin, having the data on blood glucose levels is important for guidance and motivation, provided the data are part of educational programs. The field was revolutionized with the arrival of systems allowing continuous glucose measurements, measuring glucose levels in subcutaneous tissue and recalculating them to blood glucose values. But it was only when the Abbott system LibreTM became available that this technology truly revolutionized the way glucose is measured in those living with diabetes. This system with intermittent scanning allowed a 14day sensor use, with great accuracy and most importantly greater affordability compared to the previous continuous glucose monitoring systems was a key moment in diabetes care. Democratization of sensor use lead to worldwide uptake and importantly pushed the field forward on thinking about new ways to express overall glucose control. The availability of 24h glucose curves, with many more data, drove to new concepts: times in ranges, coefficients of variability etc.. These turn out to be important additional inputs on top of the traditional concepts like HbA1c and are even overtaking HbA1c in daily practice and clinical trials. It will hopefully not be long before also regulators will embrace these new parameters. The good news is that the field does not stand still! For a revolution to become a persisting reality, evolution is needed: linking sensor and pen data, linking sensors to pumps with artificial intelligence, creating (hybrid) closed loop systems, apps assisting those using sensors for glucose measures in decision making on insulin doses, food intake, exercise etc. and most importantly, increase affordability and user friendliness. But new revolutions are on the horizon: not only glucose monitoring is being targeted, but also other metabolites come into the picture: lactate, ketones and others. And integration of all these values together with information on exercise, heart rate, food intake and even geography (person in the kitchen versus the bathroom…) will lead to completely different sets of information allowing artificial intelligence systems to assist those living with diabetes even better in therapeutic decision making, but particularly guiding them how to integrate diabetes better in their life, with less disruption and improved quality of life.
PARALLEL SESSION ‐ JDRF SESSION ‐ MONITORING PRE‐SYMPTOMATIC TYPE 1 DIABETES: WHICH TECHNOLOGY FOLLOWS AUTOANTIBODY DETECTION?
WHAT'S FEASIBLE IN THE CLINICAL CARE SETTING?
University of Colorado, Barbara Davis Center For Diabetes, Aurora, United States of America
The natural history of type 1 diabetes is well defined, and early stages of type 1 diabetes are defined by the presence of multiple type 1 diabetes associated autoantibodies (glutamic decarboxylase antibody, insulinoma antigen‐2 antibody, insulin antibody, and zinc transporter 8 antibody). Internationally, the number of programs screening for early stages of type 1 diabetes through the measurement of type 1 diabetes associated autoantibodies continues to increase. In addition, all four major type 1 diabetes associated autoantibodies can be measured commercially in many countries. With increased screening and detection of individuals who are in early stages of type 1 diabetes, there is a need to provide routine clinical follow up to keep patients medically safe, reduce the risk of diabetic ketoacidosis, counsel on risk of disease progression and identify individuals who may be eligible for interventions. In a research setting, oral glucose tolerance tests, metabolic risk scores, HbA1c measurement, blood glucose testing and continuous glucose monitors may be used to monitor disease progression. It is unknown whether these tools used during follow‐up visits in a research setting will be technically, economically or operationally feasible in a clinical setting. Whether our current monitoring strategies are feasible in a clinical setting is also dependent on the demand for such follow‐up, which hinges on the successful implementation of screening individuals for early stages of type 1 diabetes. We will review experience from our general population screening program in Colorado, the Autoimmunity Study in Kids, as well as our Early Type 1 Diabetes Clinic to examine the acceptability, implementation, practicality and integration of the use of various monitoring tools in the clinical setting. We will also discuss how current monitoring strategies may be adapted to improve feasibility in a clinical setting.
PARALLEL SESSION ‐ DATA SCIENCE IN DIABETES
IMPROVED DATA COLLECTION: MOVING CGM REPORTS FROM THE PATIENT DIRECTLY TO THE EMR
International Diabetes Center, Pediatric Endocrine, Minneapolis, United States of America
International Diabetes Center (IDC) has spent the last 15 years working with the diabetes technology community to establish an effective way to present glucose data that allows both the patient and the clinician a way to identify actionable patterns of hyperglycemia and hypoglycemia to increase time in range. The result of the multiple consensus meetings and publications has been the refinement of the ambulatory glucose profile (AGP) report to display continuous glucose monitoring (CGM) and blood glucose monitoring (BGM) data in a standardized and clinically meaningful way. The AGP Report has been accepted by the International Consensus Committee on Time in Range and the American Diabetes Association's (ADA) Standard of Medical Care for Diabetes as a recommended example of a standardized method to represent CGM data. With increasing options for virtual diabetes care, the ability to integrate CGM data directly into the Electronic Health Record (EHR) efficiently for clinical care has become more necessary. The Integration of Continuous Glucose Monitoring Data into the Electronic Health Record (iCoDE) Project, completed in 2022, recognized the importance of consistent and secure data integration and has developed guidelines for loading and integration of continuous glucose monitor (CGM) data into the EHR. Since May 2020, our healthcare organization (IDC, HealthPartners Institute & HealthPartners Care Group) has been able to integrate Abbott's CGM data from LibreView directly into our Epic EHR using the application programming interface (API) supplied by Redox. We have created a patient flowsheet in Epic to review CGM glucose metrics over time to identify areas of improvement for the individual patient with diabetes providing the foundation to track diabetes population health metrics in the future. Patients with diabetes use different CGM devices and are seen for diabetes in primary and specialty care with a need to establish scalable processes to bring discrete CGM data from all devices directly into the EHR wherever patients receive diabetes care. Work has now begun to integrate data from other CGM systems, connected insulin delivery devices and on standardizing the associated data reports needed for efficient and effective use of these technologies that are transforming diabetes care. Automated access to CGM data, fully integrated into the EHR, is a key step toward precision diabetes care.
PARALLEL SESSION ‐ DATA SCIENCE IN DIABETES
ALGORITHM‐ENABLED RPM FOR T1D; LESSONS FROM THE 4T STUDY FOR A CONTINUOUSLY LEARNING HEALTH SYSTEM
Stanford University, Pediatrics, Stanford, United States of America
The 4T (Teamwork, Targets, and Technology for Tight Control) Study seeks to set and maintain tighter glucose management targets in patients with newly diagnosed T1D, with the use of continuous glucose monitoring and telemedicine. Timely Interventions for Diabetes Excellence (TIDE) facilitates this new technology‐enabled, telemedicine‐based care model. TIDE identifies patients with deteriorating glucose management and the data most relevant for providers to help patients reastablish control, through the analysis of CGM data. The 4T program and TIDE are a continuously learning, algorithm‐enabled model for personalized care at population scale. The algorithms that identify patient needs are continuously improved based on historical CGM data collected through TIDE, the messages sent to patients by the care team, and the resulting changes in patient glucose management. The TIDE interface is continuously improved based on feedback from the care providers who use it. The initiation of TIDE reduced provider screen time by 47%. A year later, the first major round of improvements to the algorithms and interface reduced provider screen time by 86%. Within the 4T study, patients monitored with TIDE saw greater reductions in HbA1c and higher TIR than patients not monitored with TIDE. We present the development of TIDE, lessons learned from its iterative improvements, and ongoing projects to further expand its functionality.
PARALLEL SESSION ‐ DATA SCIENCE IN DIABETES
DATA OWNERSHIP AND USE OF DATA AGGREGATORS IN CLINICAL CARE
Barbara Davis Center for Diabetes, Pediatrics, Aurora, United States of America
Type 1 diabetes (T1D) is increasingly becoming a “digital disease” for which persons with diabetes (PwD) generate hundreds of continuous glucose monitoring (CGM), insulin dosing, carbohydrate intake, exercise, sleep, and other physiological datapoints per day. These data have value to the PwD themselves in addition to the providers giving medical advice, companies looking to improve algorithms and products, and third‐party researchers aiming to better understand T1D care. The desire to share data and grow knowledge is counter‐balanced against the need to secure protected health information, prevent breeches in privacy, and obstruct malicious actors. Digital data is generally available to PwD and their providers via manufacturer‐based websites and uploaders. These platforms, however, require individual providers to learn and maintain a growing number of accounts, a process which is prohibitive outside specialty diabetes centers. Third‐party data aggregators hold the potential to homogenize data sharing and visualization among PwD, providers, and researchers, but move data control outside of the companies creating and improving the devices. In this talk we will discuss data ownership and the pros and cons of data aggregators getting at the question, “Whose Data Is It Anyway?”
PARALLEL SESSION ‐ DATA SCIENCE IN DIABETES
DEEP LEARNING TO PREDICT DIABETES OUTCOME
Children's Mercy Hospital, Endocrinology And Diabetes, Kansas City, United States of America
Deep learning is a special form of machine learning that uses algorithms inspired by the structure and function of the human brain. Deep learning methodologies are now being tested in automated insulin delivery systems, where other machine learning methodologies are already widely used. But deep learning also has the potential to positively impact the structure diabetes care delivery overall. Deep learning models can be used to classify patients and to predict clinical outcomes like hospital admission for diabetic ketoacidosis, rising hemoglobin A1c, falling time in range for sensor glucose, predicting the onset of type 1 and type 2 diabetes, nudging individuals toward increased physical activity or other behaviors, and even the optimal choice of the next therapy for type 2 diabetes. Deep learning models have even been applied to voice recordings to predict health outcomes. In this presentation the speaker will survey the literature on the applications of deep learning in diabetes care, and he will review some of the concepts critical to evaluating this literature. The speaker will specific applications of deep learning in detail, as well as issues of concern that are raised by deep learning models such as barriers to implementation across healthcare institutions, disparities in model performance across patient subgroups, lack of explainability impacting clinician trust in Artificial Intelligence, model overfitting, and the need to tune or retrain models over time or across different populations. The presenter discuss potential a potential model for accelerating the implementation of machine learning and deep learning in diabetes care.
PARALLEL SESSION ‐ GLUCOSE MANAGEMENT IN THE PEDIATRIC AND ADULT FEMALE POPULATION
TECHNOLOGICAL GADGETS: WHAT IS AVAILABLE TO GIRLS AND WOMEN WITH DIABETES
University of California San Francisco, Pediatrics, Division Of Endocrinology, San Francisco, United States of America
Women have unique diabetes care needs that encompass a range of areas. Technology plays a fundamental role in delivering progress on personalized diabetes care. And now, new technologies are making it possible for women to manage their diabetes care and well‐being on a more precise and personalized level than ever before. Female Technology (“Femtech”) is a term applied to a category of software, diagnostics, products, and services that use technology often to focus on women's health. As gender becomes more widely addressed in conversations about health disparities, so does Femtech's power to advance women's health. During this talk, we dive into women's health and the technologies disrupting the space, with a specific focus on diabetes technology. We will present evidence based diabetes technology gadgets that have transformed treatment of diabetes in women and discuss potential diabetes technology tools and systems to enhance lives of women with diabetes.
PARALLEL SESSION ‐ GLUCOSE MANAGEMENT IN THE PEDIATRIC AND ADULT FEMALE POPULATION
GENDER DIFFERENCES IN CARDIOVASCULAR RISK MARKERS IN YOUNG POPULATION WITH TYPE 1 DIABETES
1University Medical Center‐University Children's Hospital Ljubljana, Department Of Pediatric And Adolescent Endocrinology, Ljubljana, Slovenia, 2University of Ljubljana, Faculty Of Medicine, Ljubljana, Slovenia
Overall, life expectancy is shorter in people with type 1 diabetes (T1D) compared to the general population, and cardiovascular diseases (CVD) are the leading cause of morbidity and mortality in persons with T1D. Hyperglycemia is considered the primary mediator of atherosclerosis in T1D. However, it cannot explain all CVD risks associated with T1D. Even individuals who achieved HbA1c levels of 6.9% or lower still had a chance of death from CVD twice as high as the risk in the general population. Several other modifiable risk factors such as hypertension, dyslipidemia, obesity, insulin resistance, lack of exercise, smoking and psycho‐social factors further influence the CVD risk. Women with T1D have a greater excess risk of all‐cause mortality and fatal and nonfatal vascular events compared with men with T1D. Therefore, sex‐related protection from CVD seems to be lost in women with diabetes. Why diabetes confers a higher risk for CVD in women than men is not entirely understood. It is also unclear when excess CVD risk in women with T1D begins. Intima‐media thickness of the carotids and aorta shows that vascular damage and atherosclerosis start from the first years after the onset of T1D. A higher prevalence of multiple CVD risk factors in adolescent girls than in boys may contribute to a more atherogenic risk profile and, thus, less favourable CVD morbidity and mortality outcomes in women with T1D compared to men. Early identification of CVD risk factors and possibly also sex‐specific intervention would potentially reduce later CVD morbidity and excess mortality in women with T1D. Further studies and more precise clinical guidelines are needed to address the role of sex‐related differences in CVD risk profiles in the pediatric population.
PARALLEL SESSION ‐ GLUCOSE MANAGEMENT IN THE PEDIATRIC AND ADULT FEMALE POPULATION
SEX DIFFERENCES IN THE MANAGEMENT OF EXERCISE IN THE PEDIATRIC AND ADULT POPULATION
1Stanford University, Pediatrics, Stanford, United States of America, 2Stanford, Quantitative Sciences Unit, Stanford, United States of America, 3Stanford, Management Science And Engineering, Stanford, United States of America, 4Stanford, Medicine, Stanford, United States of America
Regular physical activity and exercise can lead to numerous health benefits for individuals with type 1 diabetes (T1D). However, glycemic disturbances in individuals with T1D can also occur depending on various factors including, but not limited to, time of day, type, intensity, and duration of exercise. There is well‐established literature suggesting differences in sex‐related responses to exercise in individuals without T1D, but fewer studies have focused on this topic related to T1D. A large percentage of the existing literature around exercise and glycemic management with T1D has been conducted on mostly male participants. This presentation will be reviewing the current literature on sex‐related differences that may impact glycemic responses to exercise in youth and adults with T1D. In addition, we will be exploring the potential sex‐differences of newly diagnosed youth with T1D in 4T Exercise Study that have been started on physical activity trackers and involved in exercise education within the first month of diabetes diagnosis. This work in new‐onset T1D youth will highlight potential sex‐differences and acute glycemic responses to exercise (24 hours post‐exercise) in the 4T Exercise cohort.
PARALLEL SESSION ‐ GLUCOSE MANAGEMENT IN THE PEDIATRIC AND ADULT FEMALE POPULATION
INSULIN DOSING IN WOMEN WITH T1D: IS THERE A NEED FOR TAILORED SOLUTIONS?
University of Virginia, Center For Diabetes Technology, Charlottesville, United States of America
Correctly tuning insulin replacement therapy in type 1 diabetes (T1D) is a challenging task because insulin requirements are modified by multiple metabolic and psycho‐behavioral factors – eg, meals, physical activity, psychological stress. In women, hormonal changes happening over the life span further influence insulin needs, making the task of dosing insulin even more strenuous for the female population. Among the factors complicating insulin replacement for women, a relevant role has been documented to be played by the menstrual cycle. According to several studies, women with T1D may experience a decrease in insulin sensitivity during the second half of their menstrual cycle (ie, the luteal phase), which is oftentimes accompanied by an increased exposure to hyperglycemia. Also, increased occurrence of hypoglycemia has been documented during the initial days of the menstrual cycle, as women transition from luteal to follicular phase. During this talk, we will review aspects unique to the physiology of women that impact insulin needs and complicate insulin dosing – with particular attention to the menstrual cycle. Further, the talk will discuss how technology in the form of open‐loop decision support systems or closed‐loop automated insulin delivery systems can be tailored to support challenges in the management of T1D specific to women.
PARALLEL SESSION ‐ GLUCOSE MANAGEMENT IN THE PEDIATRIC AND ADULT FEMALE POPULATION
PSYCHO‐BEHAVIORAL BARRIERS TO OPTIMAL GLUCOSE MANAGEMENT IN WOMEN WITH T1D ACROSS THE AGES
Spotlight‐AQ Ltd, R&d, Fareham, United Kingdom
There are many factors affecting glucose management at different stages in life for girls and women with type 1 diabetes. From puberty through to menopause, sexual health and reproductive function present considerable challenges for many with both physical and mental health consequences. Female sexual health remains a much‐neglected area in diabetes clinical medicine, however it is important for psychological and social well‐being. Sexual health issues for women go beyond pre‐conception care and pregnancy. The risk of sexual dysfunction is 2.5 times higher for women with type 1 diabetes with contributing factors spanning interpersonal, social, psycholgoical and biological issues. Given the complex nature of type 1 diabetes, its management and its complications it is unsurprising that female sexual health is markedly affected by the condition. This presentation will explore some of the factors affecting women with type 1 diabetes across the ages in the context of barriers to optimal glucose management and how these can be overcome.
PARALLEL SESSION ‐ DIABETES INDIA
PERSONALIZED MEDICINE TO PRECISION MEDICINE: INDIA IS CHANGING
Centre for Diabetes Care, Diabetology, Greater Noida, India
Personalized medicine is well established in management of diabetes and patient centric approach is now at the core of all the global recommendations and guidelines . Person centered approcah takes into consideration the choices and preferences of the person living with diabetes but it still does not considers the fact that each individual is genetically and metabolically unique. Advent of Big Data, omics and Artificial intelligence looks promising but precision medicine is yet to find its way into routine clinical practice in case of diabetes. India is uniquely placed as it represents the second largest population of persons living with diabetes. Asian Indian Phenotype is characterized by earlier onset of diabetes, higher insulin levels and greater degree of insulin resistance , abdominal obesity, higher visceral fat despite lower BMI along with certain other unique biochemical and clinical markers in comparison to Caucasian counterparts. Precision diabetes is still in its infancy and most of the work is with monogenic diabetes however it has a great potential for use in prevention, diagnosis and treatment of other forms of diabetes. Researchers in India are now looking at the unique population characteristics through genome wide studies, DNA sequencing, application of microRNA techniques, Big DATA and omics measurements.
PARALLEL SESSION ‐ DIABETES INDIA
DIABETES TECHNOLOGY ‐ THE MAKE IN INDIA STORY
CKS Hospital, Diabetology, Jaipur, India
The latest IDF atlas puts India at second position in the list of top 10 countries with highest prevalence of type 2 diabetes in adults (20‐79 years) and also of those with undiagnosed diabetes. India also has highest estimated prevalence of type 1 diabetes. With such huge burden, India spends only 3% of its GDP on healthcare. There have been several developments in the field of diabetes technology like insulin pumps and continuous glucose monitoring. Although majority of Indians cannot afford these. India is also known for frugal innovations like the Mars Orbiter Mission. Govt of India launched make in India initiative in the 2014 to transform India into global design and manufacturing hub. In the year 2015 Startup India was launched by Govt of India. Over last one decade India has given over 100 unicorns. Currently there are 11738 startups in the field of healthcare registered with startup India, out of which 2632 are working in the field of healthcare IT and healthcare technology. Several startups have launched their diabetes care products incorporating flash glucose monitoring system and wearable devices into their mhealth apps. Ultrahuman is dedicated to improving metabolic health of people at risk of diabetes. Startups like SugarFit and TwinHealth are focused on reversal of diabetes. Efforts are on to launch indigenous continuous glucose monitoring system. BeatO has launched a connected glucose meter along with diabetes care programs. Some startups also claim to have developed non‐invasive glucose monitoring systems, although they are yet to establish their validity. Sensing self is working on saliva based non‐invasive glucose monitoring. 7Sugar has launched a program that helps people get calorie distribution of their meal by uploading a photo of their meal plate on the app. A cost‐effective insulin pump is being developed that can potentially bring down the cost of insulin pumps by 90%. Various startups are working on improving metabolic health by the use of wearable devices and machine learning programs. Artificial intelligence is being used in fundus cameras. Artificial intelligence based multispectral wound imaging cameras that can differentiate between gram positive and gram‐negative bacteria have been developed. The story of India's progress in the field of Diabetes Technology with special reference to few frugal innovations that can potentially change the lives of millions of Indians with diabetes will be presented.
PARALLEL SESSION ‐ DIABETES INDIA
DIABETES MANAGEMENT IN INDIA ‐ CHALLENGES & TECH SOLUTIONS
Lina Diabetes Care Mumbai Diabetes Resaerch Centre, Diabetology, Mumbai, India
India has the second largest number of people with diabetes in the world today (74.2 million). There are many a challenges faced – delayed diagnosis, presence of complications at time of diagnosis, lack of insurance support for ambulatory care, lack of support programmes to engage and motivate individuals with chronic diseases, high cost of monitoring, poor availability of test strips, many unvalidated monitoring devices , social and cultural beliefs like religious fastings and walking bare feet, early onset of typ2 DM, poor screening of complications and follow up visits, poor adherence to lifestyle modification and poor medications compliance. Some of the other challenges in Diabetes management are the high patient burden with poor physician to patient ratio and geographical barriers to accessing optimum care. Technology has come to the forefront in overcoming some of these challenges. Use of locally available connected meters, training on structured smbg including 3/21 concept, mobile apps and applications for diet adherence, exercise regularity, insulin dosing algorithms, carbohydrate counting etc are gradually being used regularly by patients with diabetes especially the younger individuals. The use of cgm too has gradually improved especially in type 1 DM , but largely continues to be intermittent with smbg used in between. Use of telemedicine and teleconsultation too has improved the access to diabetes specialists especially for patients in rural areas. Technology advancements in point of care testing are resulting in better monitoring of metabolic parameters and screening for complications including those for retinopathy and neuropathy and some of these devices make use of artificial intelligence for correct interpretation and reporting. Technology is helping in development of digital tools to identify at risk foot, grades of foot ulceration along with technology driven solutions for protective footwear and insoles. Though there continue to be many a challenges in diabetes management in India, technology is slowly and steadily helping physicians, patients and health care companies find simple solutions towards earlier diagnosis , monitoring , treatment and screening and prevention of complications. The wide use and availability of mobile phones with cheap data and internet availability have been a boon towards this.
PARALLEL SESSION ‐ DIABETES INDIA
OPPORTUNITIES AND THREATS ANALYSIS (SWOT) OF BLOOD SUGAR MONITORING IN INDIA
Association of Clinical Endocrinologists, Diabetes And Endocrinology, Dispur, Assam, India
“ Is the Glass half full or half empty? “ It depends on your outlook.
India is a diverse country with wide geographical variation in knowledge, financial capability and exposure .In Diversity lies its strength and the opportunities to thwart the weakness and threats.
Fasting and PP system
Screening camps
Pharmacies
Cheap health packages
Diabetes centre packages
Pharma driven CME
Intermingling of Tier system
Pharma dependent training
Johari window unawareness
Converting the strength into Assets
Neutralising weakness by online modules, credit points, new NMC rules
encouraging startups
Streamlining fasting and PP concepts
“Who moved my cheese”
Funding
Half training
Chinese whispers
Blood sugar monitoring has evolved enormously. As Robert Frost said “ I have miles to go ,miles to go before I sleep “, there is still a lot of work to do .
PARALLEL SESSION ‐ DIABETES INDIA
DO IT YOURSELF ARTIFICIAL PANCREAS: THE AFFORDABLE INDIAN EXPERIENCES
Jothydev's Diabetes Research Centre, Diabetes, Trivandrum, India
In spite of a robust decline from 55.1% to 16.4% over the past 15 years, India still has the highest number of poor people in the world. Though poverty among children has declined at a faster rate, India still has the highest number of poor children (97 million, 21.8%) implying that one in every three children lives in poverty, while one in seven adults lives in poverty. It is a frustrating experience for adolescents with TID in India, to live a miserable life, despite technological advances in diabetes. In India, currently, there is no established support for T1D with either CGM, insulin pumps, or automated insulin delivery (AID) devices. The Do‐it‐yourself artificial pancreas (DIYAP), which is popular in developed countries is slowly catching up in the Indian T1D community and is experimenting with affordable options. DIYAP combines the sensor data from a CGM together with other specifications such as basal rate, insulin sensitivity factor, & carb ratio and subsequently, calculates the insulin dose required to maintain the blood glucose level within the target range. Unfortunately, in India, we don't have many options either for insulin pumps or CGMs, including integrated CGM (CGM that can be used as part of an integrated system with other compatible medical devices and electronic interfaces, which may include automated insulin dosing systems, insulin pumps, blood glucose meters or other electronic devices used for diabetes management). In a country like India, where 16.4% of the population, as mentioned earlier, is in multidimensional poverty, the cost is the major limitation to the use of technologies. A commercial hybrid‐close loop costs around 600,000 INR (73467 USD) followed by a monthly cost of approximately 30,000 INR (370 USD) making it difficult to afford by self‐funding. The only commercial hybrid‐close loop AID available in India is MiniMed 780G. Though in some states like Kerala, the government provides free insulin pumps and AIDs to some patients, these are only very few numbers compared to the magnitude of the T1D population in the country. In India, currently, there are approximately 860,423 people living with T1D who are finding every option to optimize the use of currently available affordable devices. This reiterates the need for DIYAPs. As per the users, the initial investment is around 250,000 INR (3061 USD) followed by a monthly cost of approximately 10,000 INR (122 USD). The majority of DIY users in India use AndroidAPS (Android users) and loop (for IOS) as open‐source closed‐loop systems. Out of limited options, the compatible insulin pumps they mostly use are old Medtronic pumps including, Medtronic Minimed 712,715,&722. Unlike many other developed nations insulin pumps are not reimbursable and no insurance policies are available in India. Since the options are limited the users are re‐engineering the available pumps so as to work with the open‐source closed‐loop system. For old Medtronic pumps, an additional communication device is needed to “translate” the radio signal from the pump to Bluetooth. The additional communication devices that are used by most of the users here in India are Rileylink and Orange link. The Orange link is preferred by the larger population as it is compact, lightweight, and designed using an all‐new nRF52810 Bluetooth 5.2 System on Chip. With regard to CGM, we have Guardian Sensor 3 and Guardian Sensor 4 (factory calibrated) currently available as real‐time CGM in India that provide predictive alerts up to 60 minutes in advance of high and low glucose events. Guardian sensor though available as a real‐time device in India is not affordable to a majority of the tT1D population and is also not an integrated CGM. Freestyle Libre is the isCGM available in India. The professional CGM (Libre Pro) is comparatively affordable to the majority of the T1D population. One of the alternate affordable ways to get real‐time data is to use a professional CGM like Libre Pro with a transmitter like Miao Miao or NightRider. Consequently, the users also reuse the sensor with the help of certain apps and restart the sensor. As per the users, the readings are quite accurate. To display the glucose data, they use the xDrip+ app or the Nightscout website[Table 1]. That is the one side of the story where people are trying to convert available devices into user‐friendly and affordable options. The average time‐in‐range [TIR] with a commercial hybrid close loop system is 70‐80%. When using DIYAP, the average TIR is about 85‐90%, as per the users. Though there are more than two dozen known users successfully using DIYAP, one of the difficulties that the users face is regarding troubleshooting, which can be difficult at times. Patient stories also suggest that the procurement of compatible devices to initiate the DIYAP is the most difficult part. The setting up of algorithms can be time‐consuming and requires technical know‐how. The configuration can be tricky and complicated which can discourage the patients from choosing these systems. The users need to be engaged, activated, and committed to allocating time to understand and set up the system. The chance of insulin pump failure is very low when used by tech‐savvy users, but it can be the other way when less‐expert individuals start managing the older pumps. A very small percentage (<1%) of users get support from their clinics to use DIYAP. The patient community themselves support each other in every step. They research and train each other. The loopers community is equipped with 24‐h, global online support and makes consistent efforts to increase its radical transparency and accountability. Like in other parts of the world, DIYAP is not approved for use in India as well. But, as American Diabetes Association (ADA) states, we never discourage users. These affordable options are providing them with profound quality of life (QoL). Diabetes technology advancements in the past years made possible the development of DIYAPs that lead to “closing the loop” integrating CGM, insulin pumps, and smartphone technology to run openly shared algorithms to achieve appreciable glycemic control and QoL without burning their pockets too much!
PARALLEL SESSION ‐ EMERGING TECHNOLOGIES FOR DIABETES
REPLACING PUMPS WITH LIGHT CONTROLLED INSULIN DELIVERY
University of Missouri‐Kansas City, Pharmacology And Pharmaceutical Sciences, Kansas City, United States of America
We have developed a new way of delivering insulin that uses light to stimulate insulin release from a patient‐injectable depot. We call this approach the Photoactivated Depot (PAD) approach. This allows for the continuous variability of an insulin pump, without the numerous problems associated with the physical connection that a pump requires. These problems include infection, cannula crimping, bio‐fouling and occlusion. We designed and synthesized a range of materials that are injectable into the skin using a standard insulin syringe and have shown that these release insulin both in‐vitro and in‐vivo. The amount of insulin released is proportional to the amount of light applied to the skin using a compact LED light source. In addition, we have demonstrated that these materials release fully bioactive insulin which results in blood glucose reductions that are also proportional to the amount of irradiation. In this presentation we will discuss the strategies that we have used to achieve multiple design aims. One of these design aims is Efficiency. This refers to the proportion of the material that is insulin, as opposed to carrier or polymer. First generation materials were based on polymers to achieve depot insolubility. This resulted in materials that contained <10% insulin by dry weight. Our second generation design strategies allow materials that are ∼90% dry weight insulin, allowing for greater duration of action and ease of release. In addition, we will describe potential therapeutic advantages of photoactivated insulin over pump delivered insulin. The principal of these advantages is speed. We observe insulin in the blood 5 minutes after irradiation of a PAD, which makes photoactivated insulin as fast or faster than the fastest insulins commercially available. This begins to rival the performance of the pancreas itself, and can lead to better control of post‐prandial blood glucose excursions, with attendant health benefits. Finally we will discuss some of the operational issues associated with the PAD approach. The skin based light source used to stimulate insulin release is entirely solid state with significantly lower energy requirements compared to a pump. This can lead to a smaller, lighter form factor. In addition, the lack of moving parts compared to an insulin pump should lead to greater physical robustness, and lower costs to manufacture and purchase. This latter factor can potentially expand the range of patients who are able to access and use an artificial pancreas.
PARALLEL SESSION ‐ EMERGING TECHNOLOGIES FOR DIABETES
CONTINUOUS LACTATE MONITORING (CLM) – A NEW PARADIGM FOR MONITORING HIGH‐RISK DIABETIC PATIENTS
QuLab Medical, R&d, Herzliya, Israel
As wearable healthcare monitoring systems advance, there is great potential for multi‐metabolite sensing to enhance the management of type 1 diabetes (T1D). Improvements in wearable sensor technology, specifically the introduction of additional analyte monitoring capabilities, are believed to ultimately lead to improved glycemic control. Defective glucose metabolism and low tissue oxygenation have been linked to enhanced lactate levels in diabetic patients with acute myocardial infarction (AMI). High lactate levels indicate increased risk for poor outcome in this population. The rise in blood lactate concentration in diabetics with AMI was previously shown to be associated with increased incidence of heart failure, severe arrhythmias, cardiogenic shock, and high mortality rate. QuLab Medical has developed a novel minimally‐invasive intradermal patch platform for continuously monitoring multiple metabolites in parallel. Specifically, we have focused on the development of a Continuous Lactate Monitor (CLM) and combining it with a CGM in a single path device. We believe that the combined CGLM solution will help T1D patients better manage their life, on both wellness and disease fronts.
PARALLEL SESSION ‐ EMERGING TECHNOLOGIES FOR DIABETES
BEYOND THE GLUCOSE‐CENTRIC DIABETES MANAGEMENT: THE PATH TO MULTIPLE SENSING
1University of Padova, Department Of Women's And Children's Health, Padova, Italy, 2Yale University, Department Of Pediatrics, New Haven, United States of America
Whereas continuous monitoring of glucose has served as the main guidance for the development and use of automated insulin delivery systems (AID) in type 1 diabetes (T1D), major limits still halt the achievement of a fully AID able to self‐adaption to different daily life activities as well as to optimize glycemic control in other conditions than T1D. Glucose is part of a complex energetic cellular balance that involves other two key metabolites – lactate and ketones (beta‐hydroxybutyrate[BHB]) – that have been recently the target of continuous sensing. Recently, investigational devices that allow continuous lactate and BHB sensing have shown promising results in small clinical studies. The clinical relevance of lactate and BHB stands in their ability to early detect metabolic shifts during unannounced activities (e.g. physical activities, fasting) as well as in their role as alternative metabolic fuels for critical organs as brain and hearth. The ability to real time monitor lactate and BHB paves the way to a new generation of AID able to minimize the need for user announced activities and to broaden the use of AID to other clinical settings. We will propose three experimental case studies: the use of lactate during physical activity in those living with T1D ; the potential benefit of monitoring lactate and BHB in preterm neonates to adjust glucose and insulin infusion in order to meet the metabolic need of neonatal; the enhanced safety of BHB continuous monitoring as a tool for early detection of ketosis in those with T1D.
PARALLEL SESSION ‐ ISPAD SESSION
GLOBAL REGISTRY DATA: DIABETES TECHNOLOGY IMPACT
Hospital Sant Joan de Déu, Pediatric Endocrinology, Barcelona, Spain
Global registries reflect the current status of type 1 diabetes and the real‐life impact that the adoption of diverse diabetes therapies have beyond randomized clinical trials. With the rapid adoption of diabetes related‐technology worldwide, firstly continuous glucose monitoring and more recently closed‐loop systems, it is expected a drastic change in the achievement of recommended targets for children and adolescents with type 1 diabetes. International registries such as SWEET (Better control in Pediatric and Adolescent diabetes: Working to create centers of reference) or large national prospective registries like DPV (Diabetes‐Patienten‐Verlaufsdokumentation; Germany and Austria), T1D Exchange (US), ADDN (Australasian Diabetes Database Network (Australia) or the SWEDIABKIDS (Swedish Childhood Diabetes) registries have reported a drastic increase in technology use. The SWEET registry has shown a significant decrease in HbA1c on a background of increasing pump and sensor use for 10 years within this registry. Nevertheless, there is still room for improvement as only 21% of children and adolescents with type 1 diabetes achieved the current ISPAD/ADA HbA1c<7.0% (53 mmol/L) target in another description within the SWEET Registry from 2021 (with data for the period 2017‐2019) while 37% achieved the former ISPAD/ADA HbA1c<7.5% (58 mmol/L) target. This percentage is however significantly higher that the one described by the T1D Exchange for the period 2017‐2018, where only 17% of youth with diabetes achieved the ISPAD/ADA HbA1c<7.5% (58 mmol/L). Reports from the different registries associate a positive effect of technology use (pump and/or sensor) on HbA1c. The decrease in HbA1c in large registries have been accompanied by a lower number of diabetes ketoacidosis and severe hypoglycemic episodes. During the next few years a potential improvement in diabetes outcomes is also expected in parallel with an increasing use of closed loop systems. On the other hand, there is still a paucity of registry‐derived data regarding other parameters such as time in range, economic measurements/analyses and quality of life/patient‐reported outcomes (PROs) which might help to visibilize the impact of technology in pediatric diabetes.
PARALLEL SESSION ‐ ISPAD SESSION
PSYCHOLOGICAL IMPACT OF TECHNOLOGY: WHAT IS MOST RELEVANT?
1University of Edinburgh, Usher Institute, Edinburgh, United Kingdom, 2University of East Anglia, Department Of Medicine, Norfolk, United Kingdom, 3Wellcome‐MRC Institute of Metabolic Science, University Of Cambridge, Cambridge, United Kingdom
The clinical benefits to using diabetes technology are now well established. However, to optimise these clinical gains, user ‘buy in’ and engagement are essential. The psychological impacts of using diabetes technology can both motivate and hinder effective and sustained technology use. Hence, alongside clinical trials to assess efficacy, it is vital to draw upon users' perspectives to better understand acceptability. With a focus on closed‐loop technology, this presentation will showcase how qualitative methodology can help illuminate and understand how diabetes technology can impact users' quality of life in ways which cannot be captured through questionnaire designs. Drawing upon findings from interviews with a diversity of user groups (young people, parents of children with type 1 diabetes and pregnant women), the presentation will highlight the transformative impact closed‐loop technology can have on users' sense of self and their confidence and ability to undertake everyday activities and engage in meaningful relationships with others. The presentation will also highlight how qualitative methods can capture unintended psychological consequences to technology use, such as excessive anxiety leading to over interaction with the system in certain individuals. Hence, it will be argued that consulting users and learning about their lived experience can help inform decisions about technology rollout and support identification of those who might benefit from bespoke input and psychological support to make optimal use of technology in everyday life.
PARALLEL SESSION ‐ ISPAD SESSION
DIABETES TECHNOLOGY ACCESS IN LOW AND MIDDLE LOW‐INCOME COUNTRIES: NOW OR LATER
Rainbow Babies and Children's Hospital, Pediatric Endocrinology, Cleveland, United States of America
Emerging diabetes technologies, including insulin pumps, continuous glucose monitors, and automated insulin delivery systems, can improve the quality of life and glycemia of people living with diabetes mellitus. Diabetes technologies are not available to all people living with diabetes across the globe secondary to issues with manufacturing, marketing and registration, pricing and reimbursement, procurement and supply, prescribing, dispensing, and patient use. There are also well described socioeconomic and racial disparities in the use of diabetes technology even in high income countries with access to these devices. Given the continued challenge of access to blood glucose meters, blood glucose strips and insulin in low and middle income countires, is now the time to make access to diabetes technologies in low and middle income countries a priority? Benefits of using diabetes technology on quality of life and glycemia (HbA1c and time in range) and current access to and use of diabetes technology in low and middle income countires will be reviewed in the context of the 2022 ISPAD Clinical Practice Consensus Guideline Chapter on the management of the child, adolescent, and young adult with diabetes in limited resource settings.
PARALLEL SESSION ‐ MIND THE FOOT!
MONITORING OF DIABETIC FOOT DISEASE
Division of Endocrinology and Diabetology, Department Of Internal Medicine, Medical University Of Graz, Graz, Austria
Diabetic foot syndrome is a complication of diabetes mellitus and is defined as the infection, ulceration, or destruction of the deep tissues of the foot. Diabetic neuropathy and/or peripheral vascular disease in the lower extremities are factors that contribute to the occurrence of diabetic foot syndrome. To diagnose diabetic neuropathy other causes of neuropathy have to be ruled out; neuropathies not associated with diabetes mellitus may be present in patients with diabetes and may be treatable. In up to 50% diabetic peripheral neuropathy can be asymptomatic. If diabetic peripheral neuropathy is not recognized and preventive foot care thus is not implemented, patients are at risk to develop diabetic foot syndrome due to their insensate feet. Loss of protective sensation (LOPS) is a sign of distal sensorimotor polyneuropathy and is a risk factor to develop foot syndrome. The following diagnostic tests are useful to assess small‐ and large fiber function and protective sensation: pinprick and temperature sensation for small‐fiber function, vibration perception and 10‐g monofilament for large‐fiber function and 10‐g monofilament for assessment of protective sensation. The incidence of diabetic foot syndrome lies between 15 and 25%. Diabetic foot syndrome is a frequent cause of hospitalization and could lead to major complications, like lower limb amputations, sepsis and death. In up to 80‐85% of cases with diabetes who have to undergo lower limb amputations have previously had a foot ulcer. The mortality rate in patients with diabetic foot syndrome is more than double than in the general population. As diabetic neuropathy with loss of protective sensation is associated with a high risk to develop (recurrent) ulcerations, different methods to (continuously) monitor diabetic feet and diabetic foot disease are in development. (Remote) monitoring of diabetic foot syndrome can be applied in three domains using technologies to support triaging high‐risk patients, technologies to support care at the site of the care provider, and technologies an enabling self‐management. These technologies include digital health solutions, smart wearables, telehealth technologies, and “hospital‐at‐home” care delivery model. Minor injuries are often unnoticed and may result in subsequent infection and ulceration may end in a foot amputation. Some studies have shown an association between increased skin temperature and asymmetries between the same regions of both feet. A smart device to assess the temperature patterns might indicate the risk to develop diabetic foot syndrome. Pressure sensors could compensate for the loss of pain sensation and enable the early detection of inadequate pressure patterns and thus prevent diabetic foot syndrome. These sensors can be incorporated in socks or insoles. Apps to recognize infections and wounds, and to empower self‐care might also be useful in the management of diabetic foot syndrome. Most of these innovative technologies are still in early phases of development and have not been widely adopted in routine care. However, they do have the potential to revolutionize management of diabetic foot syndrome in the near future.
PARALLEL SESSION ‐ DIABETES TECHNOLOGY AND WASTE: HOW TO TURN GREENER?
THE GREENING OF DIABETES CARE IN AMERICA
William Sansum Diabetes Center, Research And Innovation At Sansum Diabetes Research Institute, California, United States of America
In the United States (U.S.), 7 million Americans use insulin each day and an increasing number are being prescribed other types of injectable therapies. Similarly, a growing proportion of the 1.6 million individuals living with type 1 diabetes, change their continuous glucose monitoring sensor every 2 weeks including the majority of the 350, 000 insulin pump users. Going forward, the market for CGM among adults with non‐insulin treated diabetes and pre‐diabetes is set to increase, perhaps exponentially. From an environmental perspective, the downside of the ubiquitous use of technologies, in addition to established therapies such as insulin, is the negative impact that this can have from the associated waste. The waste associated with diabetes care can be stratified according to the type of materials (e.g., recyclable versus non‐recyclable) and the risk to human health (e.g., from needlestick injuries or exposure to bodily fluids). In the U.S., reducing risk from hazardous waste varies by State – for example in California, the Dept of Public Health regulates medical waste. The cost of dealing with medical waste in the U.S., has been estimated to contribute to 5 to 16% of healthcare spending. With the increasing concerns about environmental change, stakeholders in diabetes care have a vested interest in developing creative new approaches to reducing the impact of all forms of waste. These stakeholders include the pharmaceutical and medical device industries, health professionals, payers, policy makers and people living with diabetes. To be successful, this may also require new thinking on the transparency of the environmental burden of different approaches to diabetes care and ensuring that environmental progress comes with meaningful rewards and not additional costs to the end‐user.
PARALLEL SESSION ‐ DIABETES TECHNOLOGY AND WASTE: HOW TO TURN GREENER?
DIABETES TECHNOLOGY AND WASTE: HOW TO TURN GREENER? ‐ THE EU POINT OF VIEW
Science Consulting in Diabetes GmbH, Management, Kaarst, Germany
Diabetes Technology (DT) is widely used in the EU, with rapid increases in the usage of certain medical products, e.g. systems for continuous glucose monitoring. The well‐known pros of doing so, concerning improvements in glucose control, are associated with cons like an increase in the economic burden for the healthcare systems, but also with a lot of (plastic) waste. In the EU it is the political will to reduce plastic waste drastically. The EU Commission has issued several respective regulations, which currently mainly address plastic disposable items like bags; however, in the end also the situation with medical products (which are often exempt from such considerations) has to be improved. For example, in the Medical Device Regulation (MDR) it is clearly stated that the design of medical products should be so, that waste production is reduced or even avoided. One can envisage that such conditions will become even more rigid soon, the MDR is in constant “development”. Many manufacturers of products used for diabetes therapy are located outside the EU; however, also these will have to fulfill EU requirements. Given the long periods needed to change production lines/develop new products, it is obvious that the manufacturers are already considering and implementing such changes. In addition, patients with diabetes and diabetologists are becoming rapidly more sensitive to environmental aspects of diabetes therapy, with certain differences between countries in the EU. This also exerts pressure on manufacturers to reduce waste and recycle wherever this is meaningful and possible. Several country‐specific aspects/activities do exist at the country level, driven by regional diabetes associations; however, to my knowledge, there is no EU‐wide initiative in the diabetes arena. A coordinative activity by, e.g., by the EASD is missing. This also holds true for patient associations like the IDF‐Europe. What can be done immediately on a practical level is to provide appropriate information/training to patients with diabetes about how to handle the (plastic) waste generated. Because of the activities in the US (i.e. the Green Diabetes Declaration, in which specific tasks are given to all parties involved), we should adjust this declaration to the EU situation and establish a Task Force which pushes a change in the EU ahead.
PARALLEL SESSION ‐ DIABETES TECHNOLOGY AND WASTE: HOW TO TURN GREENER?
DIABETES TECHNOLOGY AND WASTE: HOW TO TURN GREENER? ‐ THE MANUFACTURERS' POINT OF VIEW
Novo Nordisk, Clinical Medical Regulatory, Mainz, Germany
Take the question “How to turn greener” and split it in several “sub‐questions”, the resulting sub‐questions might be: “Is there a problem?” … “Is the problem understood and addressed?” … “Are there strategies for how to turn greener?” … “Are there any tangible and concrete goals set and even controlled?” and finally … “Is progress being made and what expectation can we have for the future?” The good message at the beginning, the topic is deeply understood and broadly addressed. Today, there isn´t barely any pharmaceutical or medical device company that doesn´t understand and agree that sustainability and the ecological footprint is important, change is necessary and actions need to be taken, holistically, to improve current situation and transform organizations into greener and ecologically more responsible parts of society.
In January 2020 at Davos, “Sustainable Market Initiative” (SMI) (1) was launched by his Royal Highness the Prince of Wales. His Majesty King Charles III, hosted several task forces which invite and involve companies to develop strategies and collaborations for more sustainability. In SMI, there is a “Health Systems Task Force” which is also a partner of the WHO's Alliance on Transformative Action on Climate and Health (ATACH), a platform that over 60 countries have committed to at the Minister of Health level to strengthen climate resilience and lower the emissions of health systems. To fill this Task Force with life and focusing on environmental strategies and actions of the health industry , CEOs of 7 major global pharmaceutical companies (AstraZeneca, GSK, Roche, Merck Germany, Novo Nordisk, Sanofi and Samsung Biologics) released ‐ at the recent COP27 at Sharm el‐Sheikh ‐ collaborations and goals for the future to achieve a near‐term emission reduction and accelerate the delivery of net zero health systems (2).
Even if recently published studies (3) relativize ‐ to a certain extent‐ the quantity of negative ecological footprints of the pharmaceutical and medical device industry in comparison to other industries, e. g. in regard to waste production, they are by far not heading the list but are rather midlevel in ranking.
Pharmaceutical and medical device companies are aware of the need to act on the ecological footprint and do aim at getting greener and more sustainable. Clear strategies have been developed and are in implementation, about what to do, how and until when. The core of sustainability strategies focuses on CO2 emission, on (renewable) energy consumption, on resource consumption (e.g. [toxicity and amount of] raw materials or water) and for sure specifically also on waste. Avoiding waste and mange waste as end‐of‐life challenge of medical products. One example for a broad environmental strategy is Novo Nordisk´s strategy “Circular for Zero”. It has been developed and implemented soon after the turn into the new millennium with the ambition: zero environmental impact. It addresses “circular supply”, including supplier footprint and circular procurement, “circular company”, including operations and transport, elimination of energy‐, water‐ and material waste and green affiliates and “circular products”, including circular product design and solving end‐of‐life challenges. This strategy includes near, mid‐ and long‐term goals, e.g. for the year 2030: “all supply based on 100% renewable power”, “zero CO2 from operations and transportation” and “50% of Novo Nordisk products to be reused or recycled until that year”. An already existing result of “Circular for Zero” is the use of 100% renewable energy in all Novo Nordisk production facilities worldwide since 2020. (4)
Getting greener is understood, agreed on and actions are taken. However, the way to full sustainability is multifaceted, challenging and quite complex. To cover a zero environmental impact and, in particular, to reduce waste, it requests sustainable mindset from the beginning of the value chain until the end‐of‐life solution. It requests innovation, creativity and many partners to collaborate with, such as regulators, interdisciplinary scientists and experts from many areas and industries, also politicians. Prolonged half‐lives of medications changing from daily to weekly use or from injections to tablets can reduce waste significantly. Political support for an innovation friendly environment is needed for materialisation of related benefits. More robust and compatible medical devices, smaller, next generation medical devices and those made of less different materials, like plastics, can reduce waste in many ways. Packaging and blister materials based on organic, recyclable, even eatable materials are other examples to change and reduce waste from designing products to the use at patient´s level. Collecting back medical products in a systematic manner to reuse or recycle, in “take‐back” programs, request learning, partnering and intelligently covering evolving details, in order not to solve problems at one end and create new environmental problems at another end. Pilots are needed and strategies how to deal and proceed with the acquired knowledge. Piloting take‐back programs in one country by one company, then scaling up to different countries, then collaborating with different companies in different countries on the same (medical) product and finally scaling up to a global approach across different countries and industries involving different medical products could be one strategy that indeed already exists for insulin pen devices (5). Innovation is often the key to solving problems. Even so to reduce waste and improve the quality of treatment in the same approach. Injection devices and needles can be collected in internet linked boxes not only to prepare products for recycling but also addressing poor adherence and reminding patients on missed injections. While counting the number of devices put into the boxes and assessing the treatment adherence level, the internet connection of the boxes allows messages to patients in case therapies have been missed. Getting greener and reducing the ecological footprint is understood and being addressed. Strategies are in place and deliver results. There is no doubt that it is still a long way to become entirely green with zero footprints and, unfortunately, such improvements need time. Nothing shall be glossed over. However, pharmaceutical and medical device companies must and will continue to lead this development.
Preventing diseases, providing better treatments or even cures that avoid acute and chronic care also in hospitals are the core and finest tasks of pharmaceutical and medical device companies. This by itself leaves a greener footprint in society as all medical care that is needed significantly contributes to waste, energy and material consumption. (1) White Paper, “ACCELERATING THE DELIVERY OF NET ZERO HEALTH SYSTEMS”. Sustainable Markets Initiative Health Systems Task Force, in collaboration with BCG, November 2022.
PLENARY (3) SCREENING AND PREVENTION OF TYPE 1 DIABETES
SCREENING OF GENERAL POPULATION – CURRENT STATUS AROUND THE WORLD
Oxford University Hospitals NHS, Wellcome Centre For Human Genetics, Nihr Oxford Biomedical Research Centre, Oxford, United Kingdom
Until recently, screening programs identifying children and adults at risk of type 1 diabetes (T1D) focussed on those with first degree relatives with the disease. This has been an efficient approach for recruitment into prevention trials, as individuals with a first degree relative have a ∼15‐fold increased relative lifetime risk of T1D compared to the general population. However, this approach identifies only the minority (10‐15%) of individuals who will progress to T1D. The success of improved technology to identify at‐risk individuals, and the development of drugs aiming to delay onset of the disease, has seen a rise in programmes around the world which target screening in the general population. These programmes utilise diabetes autoantibody testing at various different ages, alone, or in conjunction with genetic testing. The benefit of general population screening is in identifying a wider group of individuals suitable for drugs or trials to delay T1D onset, such as the anti‐CD3 monoclonal antibody, teplizumab, which has recently been approved by the USA's Food and Drug Administration, and to prevent life‐threatening diabetic ketoacidosis, and to prepare individuals for a smoother transition to insulin therapy. Here we will discuss the breadth of programmes around the world and highlight their achievements and potential hurdles, which will need consideration, before rollout into clinical practice is possible.
PLENARY (3) SCREENING AND PREVENTION OF TYPE 1 DIABETES
EMERGING BIOMARKERS OF RESPONSES TO IMMUNOTHERAPIES
University of Florida, Department Of Pediatrics, Division Of Endocrinology, Gainsville, United States of America
Therapies to augment the immune response in type 1 diabetes will become an important part of the early management of the disease. Several therapies have been successful, albeit transiently, in preserving C‐peptide in clinical trials. This often manifests clinically as a significant and prolonged ‘honeymoon’, or partial relapse, period. Such times of lower glycemic variability and increased time‐in‐range reduce diabetes‐related complications, both acute and chronic. However, challenges remain regarding the use of immunotherapies as a future standard of care in type 1 diabetes. Firstly, there is vast heterogeneity in regard to the immunopathogenesis of type 1 diabetes, affecting multiple cell types including T cells (effector and regulatory), B cells, dendritic cells, and more; thus, a myriad of immunotherapeutics with differing mechanisms of action have been studied to interdict in this destructive process. Next, the role of the beta cell in its own demise requires further elucidation, and measures of beta cell function, used as clinical trial endpoints, continue to evolve. Chief among the challenges of implementing precision medicine‐directed immunotherapy is the variability in response and how it is defined. While a specific therapy may work for some individuals, termed “responders”, it may not work for all who receive it. This phenomenon is seen in other autoimmune diseases. Even among successful clinical trials, meeting their primary endpoint, there can be a subpopulation that has a C‐peptide response similar to that of the placebo group (i.e., “non‐responders”). Therefore, a one‐size‐fits‐all mentality will not be sufficient for the use of immunotherapeutics in early type 1 diabetes. Determination of the appropriate time to treat with the appropriate immunotherapy is a major hurdle. Statistical modelling could aid in this query and has been used to determine immune signatures of slow and fast progressors from multiple autoantibody positivity to type 1 diabetes. To identify biomarkers of immune therapy “responders”, in‐depth characterization is required, not only via immunophenotyping but also transcriptomics, genomics, epigenomics, and demographics. Currently, we have limited knowledge of markers that differentiate “responders” from “non‐responders”. Study teams are performing mechanistic and responder‐based analyses and are uncovering exhausted T cell subsets, HLA frequency variation, and the presence of specific autoantibodies in association with responders. In the future we hope to identify a biomarker for the prediction of these “responders”. Other autoimmune diseases can be a powerful example for the field of type 1 diabetes in their use of precision medicine‐directed treatment pathways. We are working towards increasing the number of immune therapies receiving regulatory approval. This, along with the validation of current and future biomarkers, would allow a personalized, biomarker‐driven recommendation for the treatment of the underlying pathology in type 1 diabetes.
ATTD 2023 Oral Abstract Presentations
Topic:
AS01 Big data and artificial intelligence‐based decision support systems
SEPSIS‐ASSOCIATED HYPOGLYCEMIA ON ADMISSION IS ASSOCIATED WITH INCREASED MORTALITY IN CRITICALLY ILL PATIENTS, BASED ON REAL‐WORLD EVIDENCE
G. Dafoulas1, I. Kalamaras2, K. Votis2, A. Bargiota
1
1Faculty Of Medicine, University Of Thessaly, Endocrinology ‐ Diabetes, Larisa, Greece, 2Centre for Research and Technology‐Hellas, Information Technologies Institute, Thermi, Thessaloniki, Greece
Background and Aims: The frequency and cause of hypoglycemia in various categories of septic patients have not been adequately explored. In this study, we focused on sepsis‐associated hypoglycemia in the early phase of critically ill patents and evaluated the impact of hypoglycemia on mortality.
Methods: We performed a retrospective cohort study using the Medical Information Mart for Intensive Care IV, anonymised database (MIMIC‐IV), based on the data of Intensive Care Unit (ICU) admissions between 2008 and 2012 at Beth Israel Deaconess Medical Center, USA. The study protocol was approved by the respective Institutional Review Boards.
We selected 31461 patients with Sepsis‐3 criteria from the MIMIC‐IV database for the analysis. Figure 1 depicts the stages of patient inclusion in the study.
Results: We performed survival analysis with ICU mortality as the target, stratified per glucose level. Figure 2 shows the Kaplan‐Meier curves.
Figure 2. Kaplan‐Meier survival curves for patients with sepsis in the five categories of blood glucose levels. The following table shows the results of a Cox proportional hazards model, per glucose category, after adjusting for age, sex, OASIS (Oxford Acute Severity of Illness Score) and SOFA (Sequential Organ Failure Assessment) scores.
Conclusions: The survival curves for severe and mild hypoglycemia seem to be quite below the curve for euglycemia, indicating lower survival rates, as is also the case for severe hyperglycemia, although less prominently. The proportional hazards model suggests that severe and mild hypoglycemia are significantly (p < 0.05) associated with increased mortality rates for septic patients, as is also severe hyperglycemia.
Topic:
AS01 Big data and artificial intelligence‐based decision support systems
G. Dafoulas1, I. Kalamaras2, K. Votis2,
1Faculty Of Medicine, University Of Thessaly, Endocrinology ‐ Diabetes, Larisa, Greece, 2Centre for Research and Technology‐Hellas, Information Technologies Institute, Thermi, Thessaloniki, Greece
We selected 31461 patients with Sepsis‐3 criteria from the MIMIC‐IV database for the analysis. Figure 1 depicts the stages of patient inclusion in the study.
Figure 2. Kaplan‐Meier survival curves for patients with sepsis in the five categories of blood glucose levels. The following table shows the results of a Cox proportional hazards model, per glucose category, after adjusting for age, sex, OASIS (Oxford Acute Severity of Illness Score) and SOFA (Sequential Organ Failure Assessment) scores.
Topic:
AS01 Big data and artificial intelligence‐based decision support systems
A.S. Negus1, H. Hill2,
1Digital Diabetes Analytics, R&d, Solna, Sweden, 2Uppsala University, Women's And Children's Health, Uppsala, Sweden, 3Uppsala University, Medical Cell Biology Research Group Per‐ola Carlsson, Uppsala, Sweden, 4Uppsala University, Transplantation and regenerative medicine, Medical Sciences, Uppsala, Sweden, 5Uppsala University, Medical Cell Biology, Research Group Daniel Espes, Uppsala, Sweden, 6Uppsala University, Medical Sciences, Transplantation And Regenerative Medicine, Uppsala, Sweden
Topic:
AS01 Big data and artificial intelligence‐based decision support systems
M. Jaloli,
University of Houston, Mechanical Engineering, Houston, United States of America
Topic:
AS01 Big data and artificial intelligence‐based decision support systems
1ege university, Pediatric Endocrinology, bornova, Turkey, 2Ulm University, Institute For Epidemiology And Medicval Biometry/zibmt, Ulm, Germany, 3Auf der Bult‐Zentrum für Kinder und Jugendliche, Pediatric Endocrinology, hannover, Germany, 4Motol University Hospital, Department Of Pediatrics, Prague, Czech Republic, 5Azienda Ospedaliera Universitaria Anna Meyer, Pediatric Endocrinology, Florence, Italy, 6Hospital Sant Joan de Déu, Pediatric Endocrinology, Barcelona, Spain, 7Medical University of Vienna, Pediatric Endocrinology, Vienna, Australia, 8Leiden University Medical Centre, Pediatric Endocrinology, Rotterdam, Netherlands, 9Université Mohammed V Souissi, Pediatric Endocrinology, rabat, Morocco, 10Cincinnati Children's Hospital Medical Center, Pediatric Endocrinology, Cincinnati, United States of America, 11ege university, Pediatric Endocrinology, çiğli, Turkey
Topic:
AS01 Big data and artificial intelligence‐based decision support systems
1Motol University Hospital and 2nd Faculty of Medicine, Department Of Pediatrics, Prague, Czech Republic, 21st Faculty of Medicine, Charles University, Prague, Czech Republic, 3Faculty of Mathematics and Physics, Charles University, Department Of Probability And Mathematical Statistics, Prague, Czech Republic
Topic:
AS01 Big data and artificial intelligence‐based decision support systems
1Bangalore Diabetes Centre, Diabetes, Bangalore, India, 2Twin Health Inc, Diabetes, MOUNTAIN VIEW, United States of America, 3Twin Health, Diabetes, Bangalore, India, 4MS Ramaiah Medical College, Endocrinology, Bangalore, India, 5Diabetes Care & Hormone Clinic, Diabetes, Ahmedabad, India, 6Ramakrishna Hospital and Harvey Speciality Clinic, Endocrinology, Coimbatore, India, 7Sudha Prevention Center, Diabetes, Bangalore, India, 8Lilavati Hospital and Research Centre, Diabetes, Mumbai, India
Topic:
AS02 Clinical Decision Support Systems/Advisors
1Klinikum Karlsburg, Heart and Diabetes Center, Department For Diabetology, Karlsburg, Germany, 2Institute of Diabetes “Gerhardt Katsch”, R&d, Karlsburg, Germany, 3Diabetes Service Centre, R&d, Karlsburg, Germany
Topic:
AS02 Clinical Decision Support Systems/Advisors
W. Pearson1,
1DreaMed Diabetes, Customer Success, Richmond, United States of America, 2Bridgeport Hospital, Endocriniology, Trumbull, United States of America
Topic:
AS02 Clinical Decision Support Systems/Advisors
1Eden Health Plus, Diabetology, Kolkata West Bengal, India, 2OneGlance Software Services Pvt Ltd, Ceo, chennai, India, 3OneGlance Software Services Pvt Ltd, Statistician, chennai, India
Topic:
AS02 Clinical Decision Support Systems/Advisors
M. Ganji,
University of Virginia, Center For Diabetes Technology, Charlottesville, United States of America
Topic:
AS02 Clinical Decision Support Systems/Advisors
1Oviva AG, Science, Potsdam, Germany, 2University Hospital and University of Bern, Department Of Diabetes, Endocrinology, Nutritional Medicine And Metabolism, Bern, Switzerland
Topic:
AS02 Clinical Decision Support Systems/Advisors
CHU RENNES, Endocrinologie, Diabétologie, Nutrition, RENNES, France
Topic:
AS02 Clinical Decision Support Systems/Advisors
1University of Padova, Department Of Information Engineering, Padova, Italy, 2Mayo Clinic College of Medicine, Division Of Endocrinology, Diabetes, Metabolism, Nutrition, Department Of Internal Medicine, Rochester, United States of America
Topic:
AS02 Clinical Decision Support Systems/Advisors
1Barbara Davis Center for Diabetes, Adult Clinic, Aurora, United States of America, 2Tandem Diabetes Care, Clinical Affairs, San Diego, United States of America, 3Tandem Diabetes Care, Behavioral Sciences, San Diego, United States of America, 4Tandem Diabetes Care, Data Science & Analytics, San Diego, United States of America
Topic:
AS02 Clinical Decision Support Systems/Advisors
1embecta, Medical Affairs International, Eysins, Switzerland, 2embecta, Medical Affairs Emea, Heidelberg, Germany, 3embecta, Clinical & Scientific Affairs, Franklin Lakes, United States of America, 4Diabetes Center, Diabetes Clinic, Bad Mergentheim, Germany
Topic:
AS03 Closed‐loop System and Algorithm
1Diabeloop, Research And Development, Grenoble, France, 2Diabeloop, Research, Grenoble, France, 3Centre d'Etudes et de Recherches pour l'Intensification du Traitement du Diabète, Diabétologie, Évry‐Courcouronnes, France, 4CHU de Grenoble, Endocrinologie, La Tronche, France
WITHDRAW
Topic:
AS03 Closed‐loop System and Algorithm
1IRCCS San Raffaele Hospital, Department Of Pediatrics, Milan, Italy, 2San Raffaele Scientific Hospital and Vita Salute San Raffaele University, Pediatric Diabetes Unit, Milan, Italy
The aim of our study was to evaluate the impact of Tandem Control IQ (CIQ) AHCL in a cohort of diabetic adolescents with suboptimal glucose control.
No differences in TBR 54‐70 mg/dl or <54 mg/dL were found.
Similar glucose profiles were found between 2‐weeks and 1 month of use of AHCL.
A patient suffered from a single event of mild DKA.
Topic:
AS03 Closed‐loop System and Algorithm
T. Van Den Heuvel1, P. Choudhary2,3, R. Kolassa4, W. Keuthage5, J. Kroeger6, C. Thivolet7, M. Evans8, R. Re9, J. Cellot9, J. Shin10, J. Castaneda1, L. Vorrink9, S. De Portu9,
1Medtronic Bakken Research Center, Medtronic Diabetes Emea, Maastricht, Netherlands, 2University of Leicester, Leicester Diabetes Centre, Leicester, United Kingdom, 3Kings College Hospital, Nhs Foundation Trust, London, United Kingdom, 4Diabetologische Schwerpunktpraxis, Department Of Diabetes, Bergheim, Germany, 5Schwerpunktpraxis für Diabetes und Ernährungsmedizin, Department Of Diabetes, Munster, Germany, 6Zentrum für Diabetologie Bergedorf, Department Of Diabetes, Hamburg, Germany, 7Hospices Civils de Lyon, Diab‐e Care, Lyon, France, 8University of Cambridge, Wellcome Trust Mrc Institute Of Metabolic Science And Department Of Medicine, Cambridge, United Kingdom, 9Medtronic International Trading Sàrl, Medtronic Diabetes Emea, Tolochenaz, Switzerland, 10Medtronic, Medtronic Diabetes Global, Northridge, United States of America
1 Choudhary et al., Advanced hybrid closed loop therapy versus conventional treatment in adults with type 1 diabetes (ADAPT), Lancet Diabetes Endocrinol. 2022 Oct;10(10)
Topic:
AS03 Closed‐loop System and Algorithm
1T1D Exchange, Chief Medical Officer, Boston, United States of America, 2T1D Exchange, Quality Improvement And Population Health, Boston, United States of America, 3University of Michigan, Cs Mott Children Hospital, Michigan, United States of America, 4SUNY Upstate New York, Endocrinology, Syracuse, United States of America, 5NYU Langone, Endocrinology, New York, United States of America, 6Children Hospital of Los Angeles, Endocrinology, Los Angeles, United States of America, 7Icahn School of Medicine at Mount Sinai, Endocrinology, New York, United States of America, 8University of Alabama at Birmingham, Endocrinology, Birmingham, United States of America, 9Northwell, Endocrinology, New York, United States of America, 10University of Miami, Endocrinology, Miami, United States of America
Topic:
AS03 Closed‐loop System and Algorithm
1Koç University, Department Of Pediatric Endocrinology And Diabetes, istanbul, Turkey, 2Koç University, School Of Medicine, istanbul, Turkey
Topic:
AS03 Closed‐loop System and Algorithm
University of Virginia, Center For Diabetes Technology, Charlottesville, United States of America
Topic:
AS03 Closed‐loop System and Algorithm
1Badajoz University Hospital, Paediatrics. Diabetes Technology Unit, Badajoz, Spain, 2Badajoz University Hospital, Endocrinology And Nutrition. Diabetes Technology Unit, Badajoz, Spain
Topic:
AS03 Closed‐loop System and Algorithm
1Koç University, School Of Medicine, Istanbul, Turkey, 2Barbara Davis Center for Diabetes, Adult Clinic, Aurora, United States of America
Topic:
AS03 Closed‐loop System and Algorithm
1Icahn School of Medicine, Endocrinology, NY, United States of America, 2Mayo Clinic, Division Of Endocrinology, Diabetes, Metabolism & Nutrition, Rochester, United States of America, 3Harvard University, John A. Paulson School Of Engineering And Applied Sciences, Boston, United States of America, 4Sansum Diabetes Research Institute, ‐, Santa Barbara, United States of America, 5Mayo Clinic, Obstetrics And Gynecology, Rochester, United States of America
Topic:
AS03 Closed‐loop System and Algorithm
1Steno Diabetes Center Copenhagen, Clinical Research, Herlev, Denmark, 2Technical University of Denmark, Department Of Applied Mathematics And Computer Science, Kgs. Lyngby, Denmark
Topic:
AS03 Closed‐loop System and Algorithm
Research Institute of the McGill University Health Centre, Experimental Medicine, Montreal, Canada
Topic:
AS03 Closed‐loop System and Algorithm
Sidra Medicine, Diabetes And Endocrine, Doha, Qatar
Topic:
AS03 Closed‐loop System and Algorithm
1Medtronic, Diabetes, Northridge, United States of America, 2Sheba Medical Center, Division Of Endocrinology, Tel‐Hashomer, Israel, 3Chaim Sheba Medical Center, Tel‐Aviv University, Division Of Endocrinology, Diabetes And Metabolism, Tel Hashomer, Israel
Topic:
AS03 Closed‐loop System and Algorithm
Diabettech Ltd, N/a, London, United Kingdom
Topic:
AS03 Closed‐loop System and Algorithm
A.O.U. Città della Salute e della Scienza di Torino, Department Of Public Health And Pediatric Sciences, Torino, Italy
GMI was lower in Group B from 6 months (6.8 ± 0.7 vs. 7.2 ± 0.8, p = 0.020) while Time in Range (TIR) was higher (73 ± 18 vs. 61 ± 19, p = 0.004). Same differences were found for TAR‐1 and TAR‐2 whereas no differences were observed for TBR‐1 and TBR‐2 for the whole study period. Regarding QoL, AHCL were found to improve patients' lives compared to MDI for all dimensions (mean difference +16, mean p < 0.001). Group B parents also scored higher in all domains except for communication (mean difference +14, mean p < 0.001).
Topic:
AS04 New Insulins
1LMC Diabetes and Endocrinology, Diabetes And Endocrinology, Brampton, Canada, 2Novo Nordisk A/S, Global Medical Affairs, Søborg, Denmark, 3Novo Nordisk A/S, Medical & Science, Søborg, Denmark, 4University Hospitals Leuven ‐ KU Leuven, Endocrinology, Leuven, Belgium, 5Scripps Whittier Diabetes Institute, Endocrinology, Diabetes And Metabolism, San Diego, United States of America, 6Novo Nordisk A/S, Biostatistics Insulin & Devices, Søborg, Denmark, 7UMC–University Children's Hospital, Faculty of Medicine, University of Ljubljana, Faculty Of Medicine, Ljubljana, Slovenia
Topic:
AS04 New Insulins
1International Diabetes Center at Park Nicollet, International Diabetes Center, Minneapolis, United States of America, 2University of California, Division Of Endocrinology And Metabolism, San Diego, United States of America, 3University of Leicester, Diabetes Research Centre, Leicester, United Kingdom, 4Children's and Youth Hospital “Auf Der Bult”, Diabetes Centre For Children And Adolescents, Hannover, Germany, 5Montpellier University Hospital, University of Montpellier, Department Of Endocrinology, Diabetes And Nutrition, Montpellier, France, 6Sanofi, Global Medical Franchise, Paris, France, 7Sanofi, Medical Global Health, Paris, France, 8Sanofi, Biostatistics And Programming, Paris, France, 9Sanofi, Diabetes And Cardiovascular Development, R&d, Paris, France, 10UMC–University Children's Hospital, Faculty of Medicine, University of Ljubljana, Faculty Of Medicine, Ljubljana, Slovenia
Topic:
AS05 Artificial Pancreas
Polbionica Ltd, Laboratory, Warsaw, Poland
Topic:
AS05 Artificial Pancreas
University of Virginia, Center For Diabetes Technology, Charlottesville, United States of America
Topic:
AS05 Artificial Pancreas
1Federico II University, Clinical Medicine And Surgery, Napoli, Italy, 2Federico II University, Clinical Medicine And Surgery, Cava De Tirreni, Italy
Topic:
AS05 Artificial Pancreas
1Mcgill University, Experimental Medicine, Montreal, Canada, 2Montreal Children's Hospital, Endocrinology, Montreal, Canada, 3IRCM, Promd, Montreal, Canada
Topic:
AS05 Artificial Pancreas
1University of Virginia, Pediatrics, Charlottesville, United States of America, 2University of Virginia, Center For Diabetes Technology, Charlottesville, United States of America
Topic:
AS05 Artificial Pancreas
1Radboud University Medical Center, Medical Psychology, Nijmegen, Netherlands, 2Diabeter, Pediatrics, Rotterdam, Netherlands, 3Radboud University Medical Center, Internal Medicine, Nijmegen, Netherlands, 4Tilburg University, Organization Studies, Tilburg, Netherlands, 5Radboud University Medical Center, Iq Healthcare, Nijmegen, Netherlands
Topic:
AS05 Artificial Pancreas
1University of Cambridge, Department Of Paediatrics, Cambridge, United Kingdom, 2Wellcome‐MRC Institute of Metabolic Science, University Of Cambridge, Cambridge, United Kingdom, 3Department of Diabetes and Endocrinology, Cambridge University Hospitals Nhs Foundation Trust, Cambridge, United Kingdom, 4Alder Hey Children's NHS Foundation Trust, Department Of Diabetes, Liverpool, United Kingdom, 5Alder Hey Children's NHS Foundation Trust, Nihr Alder Hey Clinical Research Facility, Liverpool, United Kingdom
Topic:
AS06 Glucose sensors
1DZD, German Center For Diabetes Research, Neuherberg, Germany, 2University of Ulm, Institute Of Epidemiology And Medical Biometry, Zibmt, Ulm, Germany, 3Supported by the program for female researchers from the Office for Gender Equality, University Of Ulm (research Assistant: Alexander Eckert, Grant‐number: 027/162/p/med), Ulm, Germany, 4Helmholtz Zentrum München (GmbH) – German Research Center for Environmental Health, Department Environmental Health, Neuherberg, Germany, 5Martin Luther University Halle‐Wittenberg, Department Of Economics, Halle (Saale), Germany, 6Medical University of Graz, Division Of Endocrinology And Diabetology, Graz, Austria, 7Carl von Noorden Klinikum Sachsenhausen, Department Of Diabetology And Metabolic Disorders, Frankfurt, Germany, 8Klinikum Dortmund GmbH, Diabetes Center, Dortmund, Germany, 9Augsburg Clinical Center, Diabetes Center, Augsburg, Germany, 10medius Klinik Ostfildern‐Ruit, Department Of Internal Medicine, Geriatric Medicine, Palliative Medicine And Diabetology, Ostfildern, Germany
Topic:
AS06 Glucose sensors
IKEM, Diabetes Centre, Prague, Czech Republic
Topic:
AS06 Glucose sensors
1Ilyinskaya Hospital, Endocrinology, Krasnogorsk, Russian Federation, 2ilyinskaya hospital, Endocrinology, MOSCOW, Russian Federation
Topic:
AS06 Glucose sensors
Centro Hospitalar E Universitário De Coimbra, Endocrinology, Coimbra, Portugal
Topic:
AS06 Glucose sensors
1University of Pisa, Clinical And Experimental Medicine, Pisa, Italy, 2University Hospital of Pisa, Department Of Medicine, Pisa, Italy, 3University Hospital of Pisa, Maternal‐infant Department, Pisa, Italy
Topic:
AS06 Glucose sensors
1University of Antwerp, Faculty Of Medicine And Health Sciences, Antwerp, Belgium, 2Antwerp University Hospital, Department Of Endocrinology, Diabetology And Metabolism, Edegem, Belgium, 3University of Antwerp, Laboratory Of Experimental Medicine And Pediatrics And Member Of The Infla‐med Centre Of Excellence, Antwerp, Belgium, 4Antwerp University Hospital, Department Of Gastroenterology And Hepatology, Edegem, Belgium
Topic:
AS06 Glucose sensors
1Kings College London, Diabetes Research Group, London, Ireland, 2Novo Nordisk, Pharmacometrics Department Of Data Science, Copenhagen, Denmark, 3Kings College London, Diabetes Research Group, London, United Kingdom, 4University of Southern Denmark, Department Of Psychology, denmark., Odense, Denmark, 5Radboud University Medical Center, Internal Medicine, Nijmegen, Netherlands, 6Nordsjællands Hospital Hillerød, Department Of Endocrinology And Nephrology, Hillerød, Denmark, 7University of Copenhagen, Institute Of Clinical Medicine, Copenhagen, Denmark, 8University of Dundee, School of Medicine, Division Of Molecular And Clinical Medicine, Dundee, United Kingdom, 9Montpellier University Hospital, University of Montpellier, Department Of Endocrinology, Diabetes And Nutrition, Montpellier, France, 10Institute of Functional Genomics, Cnrs Umr 5203, Inserm U1191, Montpellier, France, 11University of Sheffield, University Of Sheffield, Sheffield, United Kingdom, 12Wolfson Diabetes and Endocrine Clinic, Cambridge University Hospitals Nhs Foundation Trust, Cambridge, United Kingdom, 13Wellcome‐MRC Institute of Metabolic Science, University Of Cambridge, Cambridge, United Kingdom, 14Medical University of Graz, Division Of Endocrinology And Diabetology, Graz, Austria, 15Abbott, Abbott Diabetes Care, Wiesbaden, Germany, 16Deakin University, School Of Psychology, Victoria, Australia, 17Diabetes Victoria, The Australian Centre For Behavioural Research In Diabetes, Melbourne Victoria, Australia, 18University of Leicester, Leicester Diabetes Centre, Leicester, United Kingdom, 19Kings College Hospital, Nhs Foundation Trust, London, United Kingdom
Topic:
AS06 Glucose sensors
1Abbott Diabetes Care, Clinical Affairs, Alameda, United States of America, 2University of Leeds, Leeds Institute Of Cardiovascular And Metabolic Medicine, Leeds, United Kingdom
where AG is average glucose and KM is 472 mg/dL, thus allowing personalized HbA1C (pHbA1c):
where AGRref is assumed at 65.1 ml/g. Due to the known effect of glucose variability on the TIR‐HbA1c relationship, the paired metrics were grouped by quartiles of glucose CV, and agreements were evaluated by linear regression.
Topic:
AS06 Glucose sensors
1HOSPITAL CLINICO UNIVERSITARIO DE VALLADOLID, Endocrinology And Nutrition, VALLADOLID, Spain, 2CENTRO DE INVESTIGACION ENDOCRINOLOGIA Y NUTRICION, Facultad De Medicina. Universidad De Valladolid, VALLADOLID, Spain, 3HOSPITAL CLINICO UNIVERSITARIO DE VALLADOLID, Pedriatics, VALLADOLID, Spain
Topic:
AS06 Glucose sensors
Mansoura Faculty of Medicine, Internal Medicine, Mansoura, Egypt
Topic:
AS06 Glucose sensors
1Dexcom, Health Economics And Outcomes Research, Global Access, SanDiego, United States of America, 2Dexcom, Engineering, San Diego, United States of America, 3Dexcom, Medical Affairs, San Diego, United States of America
Topic:
AS06 Glucose sensors
1University of British Columbia, Endocrinology, Surrey, Canada, 2University of Alberta, Psychology, Calgary, Canada
Topic:
AS06 Glucose sensors
1Jothydev's Diabetes Research Centre, Diabetes, Trivandrum, India, 2Diabetes Care & Hormone Clinic, Diabetes, Ahmedabad, India, 3Lilavati Hospital and Research Centre, Diabetes, Mumbai, India, 4Lina diabetes Care Centre, Diabetes, Mumbai, India
Topic:
AS06 Glucose sensors
University and Azienda Ospedaliera Universitaria Integrata of Verona, Department Of Surgery, Dentistry, Pediatrics And Gynecology, Section Of Pediatric Diabetes And Metabolism, Verona, Italy
Topic:
AS06 Glucose sensors
1University of Virginia, Center For Diabetes Technology, charlottesvilee, United States of America, 2University of Virginia, Center For Diabetes Technology, Charlottesville, United States of America, 3University of Virginia, School Of Data Science, charlottesvilee, United States of America
Topic:
AS06 Glucose sensors
1Pfützner Science & Health Institute, Diabetes Center, Mainz, Germany, 2DTMD University, Internal Medicine & Laboratory Medicine, Luxembourg, Luxembourg, 3Lifecare AS, Nanobiosensors, Bergen, Norway, 4Lifecare Laboratory GmbH, R&d, Mainz, Germany
Topic:
AS06 Glucose sensors
1University and Azienda Ospedaliera Universitaria Integrata of Verona, Department Of Surgery, Dentistry, Pediatrics And Gynecology, Section Of Pediatric Diabetes And Metabolism, Verona, Italy, 2Federico II University of Naples, Regional Center Of Pediatric Diabetes, Department Of Traslational And Medical Sciences, Section Of Pediatrics, Federico Ii University, Naples, Italy, Napoli, Italy, 3University of the Study of Campania “Luigi Vanvitelli”, Regional Center For Pedatric Diabetes, Department Of Pediatrics, Naples, Italy, 4University Hospital of Bologna Sant'Orsola‐Malpighi, Paediatric Endocrine Unit, Bologna, Italy, 5Giovanni XXIII Children's Hospital, Metabolic Disorders And Genetic Diseases Unit, Bari, Italy, 6University of Messina, Department Of Human Pathology, Messina, Italy, 7San Raffaele Scientific Hospital and Vita Salute San Raffaele University, Pediatric Diabetes Unit, Milan, Italy
Topic:
AS06 Glucose sensors
1Western University, Epidemiology And Biostatistics, London, Canada, 2Western University, Family Medicine, London, Canada, 3Western University, Robarts Research Institute, London, Canada
Topic:
AS06 Glucose sensors
1University of Gothenburg, Department Of Molecular And Clinical Medicine, Gothenburg, Sweden, 2Chalmers University of Technology, Department Of Mathematical Sciences, Gothenburg, Sweden, 3Profil Institut für Stoffwechselforschung, ‐, Neuss, Germany, 4University of Washington, Department Of Medicine, Division Of Metabolism, Endocrinology, And Nutrition, Seattle, United States of America, 5Örebro University, Department Of Internal Medicine, Faculty Of Medicine And Health, Örebro, Sweden, 6Uppsala University, Department Of Medical Sciences, Uppsala, Sweden, 7Karolinska Institutet, Department Of Medicine, Karolinska University Hospital Huddinge, Stockholm, Sweden, 8Karolinska Institute, Department Of Clinical Science And Education, Södersjukhuset, Stockholm, Sweden, 9University of California, Behavioral Diabetes Institute, San Diego, United States of America
Topic:
AS06 Glucose sensors
1Ulm University, Institute For Epidemiology And Medical Biometry, Ulm, Germany, 2Children's Hospital Auf der Bult, Diabetes Center For Children And Adolescents, Hanover, Germany, 3Centre Hospitalier, Department Of Pediatric Diabetes And Endocrinology, Luxembourg, Luxembourg, 4Tübingen University Hospital, Children's Hospital, Tübingen, Germany, 5Leer Hospital, Clinic For Children And Adolescents, Leer, Germany, 6Catholic Children's Hospital Wilhelmstift, Catholic Children's Hospital Wilhelmstift, Hamburg, Germany, 7Darmstädter Kinderkliniken Prinzessin Margaret, Darmstädter Kinderkliniken Prinzessin Margaret, Darmstadt, Germany, 8Martin‐Luther University Halle‐Wittenberg, Department Of Pediatrics, Medical Faculty, Halle (Saale), Germany
Topic:
AS06 Glucose sensors
H. Deshmukh1,
1University of Hull, Diabetes And Endocrinology, Hull, United Kingdom, 2University Hospitals of Derby and Burton NHS Trust, Department Of Diabetes & Endocrinology, Derby, United Kingdom, 3Leicester General Hospital, Leicester, UK, Diabetes And Endocrinology, Leicester, United Kingdom, 4Tunbridge Wells Hospital, Tunbridge Wells, UK, Diabetes And Endocrinology, Tunbridge Wells, United Kingdom, 5Sandwell and West Birmingham Hospitals NHS Trust, Department Of Diabetes & Endocrinology, Birmingham, United Kingdom
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
P. Randine1,2, L. Pape‐Haugaard3, G. Hartvigsen2,
1University Hospital North Norway, Norwegian Centre For E‐health Research, Tromsø, Norway, 2University of Tromsø – The Arctic University of Norway, Department Of Computer Science, Tromsø, Norway, 3Aalborg University, Department Of Health Science And Technology, Aalborg, Denmark
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
1FIDAM GmbH, Forschungsinstitut Diabetes‐akademie Bad Mergentheim, Bad Mergentheim, Germany, 2Roche Diabetes Care GmbH, Roche Diabetes Care Gmbh, Mannheim, Germany, 3mySugr GmbH, Mysugr Gmbh, Vienna, Austria, 4Roche Diabetes Care Deutschland GmbH, Roche Diabetes Care Deutschland Gmbh, Mannheim, Germany
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
1Dario Health, Clinical, Caesarea, Israel, 2Dario Health, Data, Caesarea, Israel, 3Dario Health, Chief Medical Officer, New York, United States of America
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
1King's College London, Cardiovascular, London, United Kingdom, 2Factor 50, Analytics, nottingham, United Kingdom, 3Guy's and St Thomas Hosptial, Diabetes, London, United Kingdom
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
1University of Cincinnati, Lindner College Of Business, Cincinnati, United States of America, 2Stevens Institute of Technology, School Of Business, Hoboken, United States of America, 3University of Cincinnati, College Of Medicine, Cincinnati, United States of America, 4CuriMeta, Chief Medical Officer, St. Louis, United States of America
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
1Hospital Putrajaya, Endocrinology, Putrajaya, Malaysia, 2Universiti Kebangsaan Malaysia Medical Centre, Endocrinology, Kuala Lumpur, Malaysia
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
University of Virginia, Center For Diabetes Technology, Charlottesville, United States of America
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
1Hospital Universitari Vall d'Hebron, Endocrinology And Nutrition Department, Barcelona, Spain, 2Roche Spain, Roche Diabetes Care Spain Sl, San Cugat, Spain
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
University Hospital RWTH Aachen, Department Of Paediatrics, Aachen, Germany
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
Carbon Health, Virtual Diabetes Care, Oakland, United States of America
Topic:
AS08 Insulin Pumps
1Institut Necker Enfants Malades, Immediab, Paris, France, 2Air Liquide Healthcare, Explor!, BAGNEUX, France, 3Hôpital Lariboisière APHP, Inserm U1132, Bioscar, Paris, France, 4Hôpital Lariboisière APHP, Centre Universitaire D'étude Du Diabète Et De Ses Complications (cudc), Paris, France, 5Hôpital Bichat‐Claude Bernard APHP, Diabétologie ‐ Endocrinologie Et Nutrition, Paris, France
Topic:
AS08 Insulin Pumps
1Semmelweis University, Department Of Internal Medicine And Oncology, Budapest, Hungary, 2Óbuda University, Biomatics And Applied Artificial Intelligence Institute, John Von Neumann Faculty Of Informatics, Budapest, Hungary
Topic:
AS08 Insulin Pumps
1University of Pisa, Clinical And Experimental Medicine, Pisa, Italy, 2University Hospital of Pisa, Department Of Medicine, Pisa, Italy, 3University Hospital of Pisa, Maternal‐infant Department, Pisa, Italy
Topic:
AS09 New Medications for Treatment of Diabetes
University of Pittsburgh Medical Center Children's Hospital of Pittsburgh, Pediatric Surgery, Pittsburgh, United States of America
(Figure 1 demonstrates decreasing insulin requirements after gene therapy in one of our NHPs)
Topic:
AS09 New Medications for Treatment of Diabetes
1Eli Lilly & Company, Global Scientific Communications, Indianapolis, United States of America, 2Gemeinschaftspraxis für Innere Medizin und Diabetologie, Diabetes, Hamburg, Germany
Topic:
AS10 New Insulin Delivery Systems: Inhaled, Transderma, Implanted Devices
1MannKind Corporation, Medical Affairs, Westlake Village, United States of America, 2Loma Linda University, Endocrinology, Loma Linda, United States of America, 3Texas Diabetes and Endocrinology, Endocrinology, Austin, United States of America
Topic:
AS10 New Insulin Delivery Systems: Inhaled, Transderma, Implanted Devices
1Stanford University, Pediatric Endocrinology, Palo Alto, United States of America, 2Stanford University, Diabetes Research Center, Stanford, United States of America, 3Stanford University, Pediatric Radiology, Stanford, United States of America, 4Stanford University, Adult Endocrinology, Stanford, United States of America
Topic:
AS12 Advanced Medical Technologies to Be Used in Hospitals
1Stanford University, Division Of Endocrinology, Gerontology And Metabolism, Department Of Medicine, Stanford, United States of America, 2Emory University School of Medicine, Division Of Endocrinology, Metabolism, And Lipids, Department Of Medicine, Atlanta, United States of America, 3University of Virginia, Division Of Endocrinology, Center For Diabetes Technology, Charlottesville, United States of America, 4Jaeb Center for Health Research, Epidemiology, Tampa, United States of America, 5Insulet Corporation, Medical Director, Acton, United States of America, 6Stanford University, Division Of Pediatric Endocrinology, Department Of Pediatrics, Stanford, United States of America
Topic:
AS12 Advanced Medical Technologies to Be Used in Hospitals
1University Hospital Essen, Endocrinology, Essen, Germany, 2University Hospital Essen, Digital Transformation, Essen, Germany, 3University Hospital Essen, Central Information Technology, Essen, Germany, 4University Hospital Essen, Neurology, Essen, Germany, 5University Hospital Essen, Dermatology, Venereology & Allergology, Essen, Germany, 6University Hospital Essen, Trauma Surgery, Essen, Germany, 7University Hospital Essen, Intensive Care Medicine, Essen, Germany
Topic:
AS13 New Technologies for Treating Obesity and Preventing Related Diabetes
Ain Shams University, Department Of Pediatrics, Cairo, Egypt
Topic:
AS13 New Technologies for Treating Obesity and Preventing Related Diabetes
1Gentofte hospital, Center For Clinical Metabolic Research At Gentofte Hospital, Hellerup, Denmark, 2Zealand University Hospital, Department Of Medicine, Køge, Denmark, 3Herlev Hospital, Steno Diabetes Center Copenhagen, Herlev, Denmark, 4Faculty of Health and Medical Sciences, Department Of Clinical Medicine, University Of Copenhagen, Copenhagen, Denmark, 5Herlev Hospital, Steno Diabetes Center Copenhagen, Hellerup, Denmark, 6Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty Of Health And Medical Sciences, Copenhagen, Denmark
Topic:
AS14 Blood Glucose Monitoring and Glycemic Control in the Hospitals
1Hedia ApS, Clinical And Medical Affairs, Copenhagen, Denmark, 2Bispebjerg and Frederiksberg Hospital, The Parker Institute, Copenhagen, Denmark
Topic:
AS15 Human factor in the use of diabetes technology
1Northwestern University, Feinberg School Of Medicine, Chicago, United States of America, 2Jaeb Center for Health Research, Diabetes, Tampa, United States of America, 3Henry Ford Medical Center, Division Of Endocrinology, Detroit, United States of America, 4International Diabetes Center Park Nicollet, Endocrinology, Saint Louis Park, United States of America, 5Stanford University School of Medicine, Pediatrics ‐ Endocrinology And Diabetes, Stanford, United States of America, 6Cecilia Health, Clinical Services, New York City, United States of America, 7Lagoon Health, Director, Minneapolis, United States of America, 8University of Colorado Anschutz Medical Campus, Department Of Family Medicine, Aurora, United States of America, 9SUNY Upstate Medical University, Endocrinology, Diabetes, & Metabolism, Syracuse, United States of America
Topic:
AS15 Human factor in the use of diabetes technology
1Kings College London, Diabetes Research Group, London, United Kingdom, 2Centre for Implementation Science,Institute of Psychiatry, Psychology and Neuroscience, Health Service And Population Research Department, London, United Kingdom, 3University Hospitals Dorset NHS Foundation Trust, Diabetes, Bournemouth, United Kingdom, 4University of Sheffield, University Of Sheffield, Sheffield, United Kingdom, 5Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK, Biostatistics And Health Informatics, London, United Kingdom, 6King's College Hospital NHS Foundation Trust, Diabetes, London, United Kingdom
Topic:
AS15 Human factor in the use of diabetes technology
1Diabetes Center Berne, Clinical, Bern, Switzerland, 2Diabetes Center Berne, Data Sciences, Bern, Switzerland
Topic:
AS15 Human factor in the use of diabetes technology
1University of Pennsylvania, School Of Medicine, Philadelphia, United States of America, 2Jaeb Center for Health Research, Diabetes, Tampa, United States of America, 3Jaeb Center for Health Research, Biostatistics, Tampa, United States of America, 4International Diabetes Center Park Nicollet, Endocrinology, Saint Louis Park, United States of America, 5Jaeb Center for Health Research, Epidemiology, Tampa, United States of America, 6HealthPartners Institute, International Diabetes Center, Minneapolis, United States of America
Topic:
AS15 Human factor in the use of diabetes technology
1The diaTribe Foundation, Stigma, San Francisco, United States of America, 2dQ&A ‐ The Diabetes Research Company, Diabetes Technology, San Francisco, United States of America, 3Deakin University, School Of Psychology, Victoria, Australia, 4Diabetes Victoria, The Australian Centre For Behavioural Research In Diabetes, Melbourne Victoria, Australia, 5University of Michigan, School Of Nursing, Ann Arbor, United States of America, 6University of Florida, Department Of Clinical And Health Psychology, Gainesville, United States of America, 7Ohio University of Heritage College of Osteopathic Medicine, Department Of Primary Care, Athens, United States of America
Topic:
AS15 Human factor in the use of diabetes technology
1FIDAM GmbH, Forschungsinstitut Diabetes‐akademie Bad Mergentheim, Bad Mergentheim, Germany, 2Roche Diabetes Care GmbH, Roche Diabetes Care Gmbh, Mannheim, Germany, 3mySugr GmbH, Mysugr Gmbh, Vienna, Austria, 4Roche Diabetes Care Deutschland GmbH, Roche Diabetes Care Deutschland Gmbh, Mannheim, Germany
Topic:
AS15 Human factor in the use of diabetes technology
1FIDAM GmbH, Forschungsinstitut Diabetes‐akademie Bad Mergentheim, Bad Mergentheim, Germany, 2FIDAM, Forschungsinstitut Diabetes‐akademie Bad Mergentheim, Bad Mergentheim, Germany, 3Profil Institut für Stoffwechselforschung GmbH, Profil, Neuss, Germany
Topic:
AS15 Human factor in the use of diabetes technology
1Sapienza Università di Roma, Policlinico Umberto I, Roma, Italy, 2Sapienza University of Rome, Clinical And Molecular Medicine, Rome, Italy, 3Fondazione IRCCS Policlinico San Matteo, Endocrinology, Pavia, Italy, 4Ospedale Civico di Chivasso, Ssd Diabetologia E Malattie Metabolich, Torino, Italy, 5ASL Latina, Diabetology Unit, Latina, Italy, 6Università degli Studi di Catania, Medicina Clinica E Sperimentale, Catania, Italy, 7Ospedale San Paolo, Endocrinologia, Civitavecchia, Italy, 8Ospedale Policlinico Gemelli, Diabetologia, Roma, Italy, 9Israelitico Hospital, Diabetology Unit, Roma, Italy
Topic:
AS15 Human factor in the use of diabetes technology
1Nemour's Children's Health, Center For Healthcare Delivery Science, Jacksonville, United States of America, 2Jaeb Center for Health Research, Biostatistics, Tampa, United States of America, 3Yale School of Medicine, Pediatrics, New Haven, United States of America, 4Jaeb Center for Health Research, Diabetes, Tampa, United States of America, 5Children's Mercy Hospital, Endocrinology And Diabetes, Kansas City, United States of America, 6York University, School Of Kinesiology And Health Science At York, Toronto, Canada
Topic:
AS15 Human factor in the use of diabetes technology
E. Leutenegger1,
1Margaux, Epidemiology, Paris, France, 2CHU Caen, Diabetology, Caen, France, 3Montpellier University Hospital, University of Montpellier, Department Of Endocrinology, Diabetes And Nutrition, Montpellier, France, 4Hôpital Lariboisière APHP, Centre Universitaire D'étude Du Diabète Et De Ses Complications (cudc), Paris, France, 5CHU Toulouse, Diabetology, Toulouse, France, 6CHU Tours, Paediatry, Tours, France, 7Deep Digital Phenotyping Revercha Unit, Precision Health, Luxembourg, Luxembourg, 8Hopital Ste Margurite, Diabetology, Marseille, France
Topic:
AS15 Human factor in the use of diabetes technology
1York University, School Of Kinesiology And Health Science At York, Toronto, Canada, 2Jaeb Center for Health Research, Biostatistics, Tampa, United States of America, 3Jaeb Center for Health Research, Diabetes, Tampa, United States of America, 4Nemour's Children's Health, Center For Healthcare Delivery Science, Jacksonville, United States of America, 5Children's Mercy Hospital, Endocrinology And Diabetes, Kansas City, United States of America, 6Yale School of Medicine, Pediatrics, New Haven, United States of America
Topic:
AS16 Trials in progress
1University of Birmingham, Institute Of Immunology And Immunotherapy, Birmingham, United Kingdom, 2University of Birmingham, Institute Of Applied Health Research, Birmingham, United Kingdom, 3Birmingham Community Healthcare Trust, Schools, Birmingham, United Kingdom, 4Birmingham Community Healthcare Trust, Community Connexions, Birmingham, United Kingdom, 5Birmingham Children's Hospital, Department Of Endocrinology And Diabetes, Birmingham, United Kingdom, 6West Glasgow Ambulatory Care Hospital, Children's & Young People's Diabetes Service, Glasgow, United Kingdom, 7Institute of Cancer and Genomic Sciences, University Of Birmingham, Birmingham, United Kingdom
Topic:
AS16 Trials in progress
1Perelman School of Medicine, University of Pennsylvania, Endocrinology, Diabetes, & Metabolism, Philadelphia, United States of America, 2University of Miami, Medicine, Miami, United States of America, 3Vertex Pharmaceuticals Incorporated, Clinical Development, Boston, United States of America, 4Seattle Children's Research Institute, Pediatrics, Seattle, United States of America, 5University of Southern California, Medicine, Los Angeles, United States of America, 6University of Chicago, Surgery, Chicago, United States of America, 7Massachusetts General Hospital, Surgery, Boston, United States of America
Topic:
AS17 COVID‐19 and Diabetes
G. Alhamar1,
1Campus Biomedico University of Rome, Endocrinology And Diabetes, Rome, Italy, 2Campus Biomedico University of Rome, Endocrinology And Diabetology, Rome, Italy, 3Campus Bio‐Medico of Rome, Endocrinology And Diabetology, Roma, Italy, 4Università degli Studi della Campania “Luigi Vanvitelli”, Dipartimento Di Medicina Sperimentale, Naples, Italy, 5“Scuola Medica Salernitana”, Baronissi, Salerno, Italy;, Department Of Medicine, Surgery And Dentistry, Salerno, Italy, 6Consiglio Nazionale delle Ricerche, Istituto Per L'endocrinologia E L'oncologia Sperimentale “g. Salvatore”, Naples, Italy, 7Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Fondazione Santa Lucia, Unità Di Neuroimmunologia, Rome, Italy, 8Università Campus Bio‐Medico di Roma, Medicine, Rome, Italy, 9University of Bari “Aldo Moro”, Department Of Basic Medical Sciences, Neurosciences And Sense Organs,, Bari, Italy, 10Campus Biomedico University of Rome, Endocrinology And Diabetes, Roma, Italy, 11Azienda Sanitaria Locale (ASL) Frosinone, Dipartimentale Endocrinologia E Malattie Metaboliche, Frosinone, Italy, 12Campus Biomedico University of Rome, Science And Technology For Humans And The Environment, Roma, Italy
Topic:
AS17 COVID‐19 and Diabetes
A. Scorsone1,
1ASP PALERMO, Diabetes Unit‐ Partinico Civic Hospital, PARTINICO, Italy, 2ASP PALERMO, Diabetes Unit‐partinico Civic Hospital, PARTINICO, Italy
Topic:
AS18 Other
1Hospital General de Segovia, Endocrinology And Nutrition Unit, SEGOVIA, Spain, 2Insulcloud S.L., Research And Development Unit, Madrid, Spain, 3Cruces University Hospital, Endocrinology And Nutrition Service, Barakaldo, Spain, 4Hospital Arquitecto Marcide, Ferrol (A Coruña), Endocrinology And Nutrition Service, Ferrol, Spain, 5Hospital Universitario Central de Asturias, Endocrinology And Nutrition Service, Oviedo, Spain, 6Hospital Universitario 12 de Octubre, Endocrinology And Nutrition Service, Madrid, Spain, 7Hospital Universitario Infanta Sofía, Endocrinology And Nutrition Service, San Sebastián de los Reyes, Spain, 8Hospital de la Santa Creu i Sant Pau, Endocrinology And Nutrition Service, Barcelona, Spain
Topic:
AS18 Other
1University of Virginia, Center For Diabetes Technology, Charlottesville, United States of America, 2University of Virginia, Division Of Endocrinology, Center For Diabetes Technology, Charlottesville, United States of America
Topic:
AS18 Other
Chung Shan Medical University Hospital, Division Of Cardiology, Taichung, Taiwan
Topic:
AS18 Other
1Hospital of Braga, Endocrinology Department, Braga, Portugal, 2University of Minho, School Of Medicine, Braga, Portugal
Topic:
AS18 Other
1The Institute for endocrinology and diabetes, Schneider Children's Medical Center Of Israel, Petach‐Tikva, Israel, 2Tel‐Aviv University, Sackler Faculty Of Medicine, Tel Aviv, Israel, 3Felsenstein Medical Research Center, Laboratory Of Molecular Endocrinology And Diabetes, Petach Tikva, Israel
ATTD 2023 E-Poster Abstract Presentations
Topic:
AS01 Big data and artificial intelligence‐based decision support systems
University of Padova, Department Of Information Engineering, Padova, Italy
Topic:
AS01 Big data and artificial intelligence‐based decision support systems
1Illinois Institute of Technology, Chemical And Biological Engineering, Chicago, United States of America, 2Illinois Institute of Technology, Biomedical Engineering, Chicago, United States of America
Topic:
AS01 Big data and artificial intelligence‐based decision support systems
1University of Bristol, Computer Science, Bristol, United Kingdom, 2University of Bristol, Department Of Engineering Mathematics, Bristol, United Kingdom
Topic:
AS01 Big data and artificial intelligence‐based decision support systems
M. Chawla1,
1Lina Diabetes Care Centre, Diabetology, Mumbai, India, 2Eden Health Plus, Diabetology, Kolkata West Bengal, India, 3Artificial Learning Systems India Pvt Ltd, R&d, Bengaluru, India
Topic:
AS01 Big data and artificial intelligence‐based decision support systems
Hua Medicine, Research And Early Development, Shanghai, China
Topic:
AS01 Big data and artificial intelligence‐based decision support systems
United Healthcare, Diabetes, Dallas, United States of America
Topic:
AS01 Big data and artificial intelligence‐based decision support systems
University of Texas Southwestern Medical Center, Plastic And Reconstructive Surgery, Dallas, United States of America
Topic:
AS01 Big data and artificial intelligence‐based decision support systems
1Lilavati Hospital and Joshi Clinic, Endocrinology, Mumbai, India, 2Bangalore Diabetes Centre, Diabetes, Bangalore, India, 3Twin Health Inc, Diabetes, MOUNTAIN VIEW, United States of America, 4Twin Health, Diabetes, Bangalore, India, 5MS Ramaiah Medical College, Endocrinology, Bangalore, India, 6Diabetes Care & Hormone Clinic, Diabetes, Ahmedabad, India, 7Ramakrishna Hospital and Harvey Speciality Clinic, Endocrinology, Coimbatore, India, 8Sudha Prevention Center, Diabetes, Bangalore, India
Topic:
AS01 Big data and artificial intelligence‐based decision support systems
Eunpyeong St. Mary's Hospital. The Catholic University of Korea, Seoul, Neurology, SEOUL, Korea, Republic of
Topic:
AS01 Big data and artificial intelligence‐based decision support systems
1The Chinese University of Hong Kong, Medicine And Therapeutics, Hong Kong, Hong Kong PRC, 2The Chinese University of Hong Kong, Hong Kong Institute Of Diabetes And Obesity, Hong Kong, Hong Kong PRC, 3The Chinese University of Hong Kong, Li Ka Shing Institute Of Health Sciences, Hong Kong, Hong Kong PRC, 4Prince of Wales Hospital, Medicine And Therapeutics, Hong Kong, Hong Kong PRC, 5The Chinese University of Hong Kong, Phase 1 Clinical Trial Centre, Hong Kong, Hong Kong PRC
Topic:
AS01 Big data and artificial intelligence‐based decision support systems
J.I. Hidalgo1,
1Universidad Complutense de Madrid, Computer Architecture And Automation, MADRID, Spain, 2Hospital Virgen de la Salud, Endocrinology And Nutrition, Toledo, Spain, 3Hospital Príncipe de Asturias, Endocrinology And Nutrition, Alcalá de Henares, Spain
Topic:
AS01 Big data and artificial intelligence‐based decision support systems
1Kings College London, Department Of Diabetes, London, United Kingdom, 2Novo Nordisk, Data Science, Department Of Pharmacometrics, Copenhagen, Denmark, 3University of Southern Denmark, Department Of Psychology, Odense, Denmark, 4Novo Nordisk, Medical & Science, Patient Focused Drug Developement, Soborg, Denmark, 5Diabetes Victoria, The Australian Centre For Behavioural Research In Diabetes, Melbourne Victoria, Australia, 6Deakin University, School Of Psychology, Geelong, Australia, 7Steno Diabetes Center Odense, Steno Diabetes Center Odense (sdco), Odense, Denmark, 8University Hospital of Leicester NHS Trust, Leicester Diabetes Centres, Leicester, United Kingdom
Topic:
AS01 Big data and artificial intelligence‐based decision support systems
1Inselspital, Bern University Hospital, University of Bern, Department Of Diabetes, Endocrinology, Nutritional Medicine And Metabolism, Bern, Switzerland, 2Diabetes Center Berne, Data Sciences, Bern, Switzerland, 3QUMEA AG, Radar Systems Engineering, Solothurn, Switzerland
Topic:
AS01 Big data and artificial intelligence‐based decision support systems
Insulet Corporation, Algorithms And Data Science, Acton, United States of America
Topic:
AS01 Big data and artificial intelligence‐based decision support systems
1ARTORG Center for Biomedical Engineering Research, University Of Bern, Bern, Switzerland, 2Maastricht University, Carim School For Cardiovascular Diseases, Maastricht, Netherlands
Topic:
AS01 Big data and artificial intelligence‐based decision support systems
1Air Liquide, R&d, Les Loges en Josas, France, 2Paris‐Saclay University, Laboratory Of Mathematics And Computer Science, Gif sur Yvette, France
Topic:
AS01 Big data and artificial intelligence‐based decision support systems
K. Lamkin‐Kennard,
Rochester Institute of Technology, Mechanical Engineering, Rochester, United States of America
Topic:
AS01 Big data and artificial intelligence‐based decision support systems
1Universidad Industrial de Santander, Santander, Bucaramanga, Colombia, 2UNIVERSIDAD INDUSTRIAL DE SANTANDER, Electrical, Electronics And Telecomunications Engineering School, BUCARAMANGA, Colombia
Topic:
AS01 Big data and artificial intelligence‐based decision support systems
1York University, School Of Kinesiology And Health Science At York, Toronto, Canada, 2Jaeb Center for Health Research, Biostatistics, Tampa, United States of America, 3Jaeb Center for Health Research, Diabetes, Tampa, United States of America, 4Oregon Health and Sciences University, School Of Medicine, Portland, United States of America, 5Nemour's Children's Health, Center For Healthcare Delivery Science, Jacksonville, United States of America, 6Children's Mercy Hospital, Endocrinology And Diabetes, Kansas City, United States of America, 7Louisiana State University, Pennington Biomedical Research Center, Baton Rouge, United States of America, 8Harvard University, John A. Paulson School Of Engineering And Applied Sciences, Boston, United States of America, 9Perelman School of Medicine, University of Pennsylvania, Endocrinology, Diabetes, & Metabolism, Philadelphia, United States of America
Topic:
AS01 Big data and artificial intelligence‐based decision support systems
1Children's Mercy Hospital, Endocrinology, KANSAS CITY, United States of America, 2University of MIssouri, Institute For Data Science And Informatics, Columbia, United States of America, 3Children's Mercy Hospital, Health Services And Outcomes Research, KANSAS CITY, United States of America, 4Nemour's Children's Health, Center For Healthcare Delivery Science, Jacksonville, United States of America
Topic:
AS01 Big data and artificial intelligence‐based decision support systems
1Children's Mercy Hospital, Endocrinology, KANSAS CITY, United States of America, 2University of Missouri, Institute For Data Science And Informatics, Columbia, United States of America, 3Children's Mercy Hospital, Health Services And Outcomes Research, KANSAS CITY, United States of America, 4Nemour's Children's Health, Center For Healthcare Delivery Science, Jacksonville, United States of America
The model identified days since last DKA, cumulative DKAs, and most recent HbA1c value as the three most important features in predicting DKA risk.
Topic:
AS01 Big data and artificial intelligence‐based decision support systems
A.G. Gallardo‐Hernandez1,
1Instituto Mexicano del Seguro Social, Unidad De Investigación En Enfermedades Metabólicas, Mexico City, Mexico, 2Universidad Nacional Autónoma de México, Facultad De Ingeniería, Mexico City, Mexico, 3Universidad Autónoma de la Ciudad de México, Colegio De Ciencia Y Tecnología, Mexico City, Mexico, 4Instituto Politécnico Nacional, Escuela Superior De Ingeniería Mecánica Y Eléctrica Zacatenco, Mexico City, Mexico, 5Secretaría de la Defensa Nacional, Hospital Central Militar, Mexico City, Mexico
Topic:
AS01 Big data and artificial intelligence‐based decision support systems
1Mount Sinai Hospital, Leadership Centre For Diabetes, Toronto, Canada, 2Unity Health, Medicine, Toronto, Canada, 3ICES, Ices, Toronto, Canada, 4University of Toronto, Department Of Medicine, Toronto, Canada
Topic:
AS02 Clinical Decision Support Systems/Advisors
Diabeter Nederland, Research, Rotterdam, Netherlands
Topic:
AS02 Clinical Decision Support Systems/Advisors
R. Kelly1, E. Barnard2,
1Spotlight‐AQ Ltd, R&d, Fareham, United Kingdom, 2BHR Ltd, R&d, Fareham, United Kingdom
Topic:
AS02 Clinical Decision Support Systems/Advisors
Nectar Diabetes & Thyroid Centre, Diabetology, Pune, India
Topic:
AS02 Clinical Decision Support Systems/Advisors
University of Padova, Department Of Information Engineering, Padova, Italy
Topic:
AS02 Clinical Decision Support Systems/Advisors
1DiappyMed, ‐, Montpellier, France, 2Montpellier University Hospital, University of Montpellier, Department Of Endocrinology And Diabetes, Montpellier, France, 3Institute of Functional Genomics, Cnrs Umr 5203, Inserm U1191, Montpellier, France
Topic:
AS02 Clinical Decision Support Systems/Advisors
1DiappyMed, ‐, Montpellier, France, 2Montpellier University Hospital, University of Montpellier, Department Of Endocrinology And Diabetes, Montpellier, France, 3Institute of Functional Genomics, Cnrs Umr 5203, Inserm U1191, Montpellier, France
Topic:
AS02 Clinical Decision Support Systems/Advisors
C. Builes‐Montaño1, L. Lema‐Perez2,
1Hospital Pablo Tobón Uribe, Endocrinology, Medellín, Colombia, 2Norges teknisk‐naturvitenskapelige universitet (NTNU), Engineering Cybernetics, Trondheim, Norway, 3University of Virginia, Division Of Endocrinology, Center For Diabetes Technology, Charlottesville, United States of America, 4Universidad Nacional de Colombia, Facultad De Minas, Medellín, Colombia
Topic:
AS02 Clinical Decision Support Systems/Advisors
The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Department Of Endocrinology, Hefei, China
Topic:
AS02 Clinical Decision Support Systems/Advisors
Ruppin Academic Center, Nursing Sciences Department, Emek Hefer, Israel
Topic:
AS02 Clinical Decision Support Systems/Advisors
FIDAM, Forschungsinstitut Diabetes‐akademie Bad Mergentheim, Bad Mergentheim, Germany
Topic:
AS02 Clinical Decision Support Systems/Advisors
Taisei Gakuin University, Hirao, Sakai, Japan
Topic:
AS02 Clinical Decision Support Systems/Advisors
Hospital del Mar, Endocrinology, Barcelona, Spain
Topic:
AS02 Clinical Decision Support Systems/Advisors
1Imperial College London, Department Of Metabolism, Digestion And Reproduction, London, United Kingdom, 2Imperial College London, Department Of Electrical And Electronic Engineering, London, United Kingdom
Topic:
AS02 Clinical Decision Support Systems/Advisors
University of Padova, Department Of Information Engineering (dei), Padova, Italy
Topic:
AS02 Clinical Decision Support Systems/Advisors
1University College London, Institute Of Health Informatics, London, United Kingdom, 2Imperial College London, Centre For Bio‐inspired Technology, London, United Kingdom, 3University College London, Department Of Computer Science, London, United Kingdom, 4University College London Hospitals, Department Of Diabetes & Endocrinology, London, United Kingdom, 5University College London, Centre For Obesity & Metabolism, London, United Kingdom
Topic:
AS02 Clinical Decision Support Systems/Advisors
1Roche Diabetes Care Nederland B.V., Medical Affairs, Almere, Netherlands, 2Leiden University Medical Centre, Endocrinology, Leiden, Netherlands, 3Netherlands Organisation of Applied Scientific Research, Microbiology And Systems Biology, Leiden, Netherlands, 4Netherlands Organisation of Applied Scientific Research, Risk Analysis For Products In Development, Utrecht, Netherlands, 5Netherlands Organisation of Applied Scientific Research, Work Health Technology, Leiden, Netherlands
Topic:
AS02 Clinical Decision Support Systems/Advisors
1Imperial College London, Department Of Metabolism, Digestion And Reproduction, London, United Kingdom, 2Imperial College London, Department Of Electrical And Electronic Engineering, London, United Kingdom
Topic:
AS03 Closed‐loop System and Algorithm
University Hospital of north Durham, Diabetes And Endocrinology, Durham, United Kingdom
Topic:
AS03 Closed‐loop System and Algorithm
Hospital University Marqués of Valdecilla, Endocrinology, Cantabria, Spain
Topic:
AS03 Closed‐loop System and Algorithm
Hospital Universitario Ramón y Cajal, Pediatric Endocrinology, Madrid, Spain
Topic:
AS03 Closed‐loop System and Algorithm
1Badajoz University Hospital, Endocrinology And Nutrition, Badajoz, Spain, 2Badajoz University Hospital, Endocrinology And Nutrition. Diabetes Technology Unit, Badajoz, Spain
Topic:
AS03 Closed‐loop System and Algorithm
Hospital Universitari Vall d'Hebron, Endocrinology And Nutrition Department, Barcelona, Spain
Topic:
AS03 Closed‐loop System and Algorithm
Sorbonne Université‐APHP‐ Hopital Pitié Salpétrière, Diabetology Department, PARIS, France
Topic:
AS03 Closed‐loop System and Algorithm
Virgen del Rocío University Hospital, Endocrinology And Nutrition Department, Seville, Spain
Topic:
AS03 Closed‐loop System and Algorithm
Virgen del Rocío University Hospital, Endocrinology And Nutrition Department, Seville, Spain
Topic:
AS03 Closed‐loop System and Algorithm
Virgen del Rocío University Hospital, Endocrinology And Nutrition Department, Seville, Spain
Topic:
AS03 Closed‐loop System and Algorithm
1Spotlight‐AQ Ltd, R&d, Fareham, United Kingdom, 2BHR Ltd, R&d, Portsmoluth, United Kingdom, 3BHR Ltd, R&d, Fareham, United Kingdom, 4University of Otago, Paediatrics, New Zealand, New Zealand, 5Dexcom, R&d, Charlottesville, United States of America, 6University of Otago, Department Of Paediatrics, Christchurch, New Zealand
Topic:
AS03 Closed‐loop System and Algorithm
IRCCS Istituto Giannina Gaslini, Pediatric Clinic, Genova, Italy
Topic:
AS03 Closed‐loop System and Algorithm
1University of Edinburgh, Centre For Cardiovascular Science, Edinburgh, United Kingdom, 2Royal Infirmary of Edinburgh, Department Of Diabetes And Endocrinology, Edinburgh, United Kingdom
Topic:
AS03 Closed‐loop System and Algorithm
1Badajoz University Hospital, Endocrinology And Nutrition. Diabetes Technology Unit, Badajoz, Spain, 2Badajoz University Hospital, Endocrinology And Nutrition. Diabetes Technology Unit, BADAJOZ, Spain, 3Badajoz University Hospital, Paediatrics. Diabetes Technology Unit, Badajoz, Spain
The autocorrection feature was activated in all the subjects, the glucose target was 100 mg/dl in 84% and active insulin time was 2 hours in 76% of the individuals. At the end of the follow‐up, time in automode was 95.6 ± 7.2% and autocorrection insulin was 31.8 ± 14.1% of bolus insulin. The percentage of people with the optimal combination of TIR >70% and time <70mg/dl <4% increased from 23% to 53% after 1 year; the percentage with TIR >70% and time <54 mg/dl <1% increased from 20% to 42% (both p < 0.001). No differences were seen in TIR at the end of follow‐up in MDI vs pump users, people with high hypoglycaemia risk at baseline or children and younger adults (≤ 25 years old) compared to older adults. Similarly, no differences in improvement in TIR were seen in any of these subgroups.
Topic:
AS03 Closed‐loop System and Algorithm
1University of Pisa, Clinical And Experimental Medicine, Pisa, Italy, 2University Hospital of Pisa, Department Of Medicine, Pisa, Italy
Topic:
AS03 Closed‐loop System and Algorithm
F. Lombardo, S. Passanisi,
University of Messina, Department Of Human Pathology, Messina, Italy
Topic:
AS03 Closed‐loop System and Algorithm
1ASL2 Savonese, Internal Medicine, Savona, Italy, 2USL Reggio Emilia, Cure Primarie, Reggio Emilia, Italy
Case 2: A 31‐year‐old with T1D used Basal‐IQ system for 7 months during pregnancy without optimal glucose control, also for night‐eating disorder. Switch to Control‐IQ with sleep‐activity for 24 hours/day improved mean glucose value and glucose variability (figure 2), without any severe hypoglicemia. Delivery was induced at 38 weeks and Control‐IQ used during during labor and natural childbirth without complications.
Topic:
AS03 Closed‐loop System and Algorithm
1Medtronic LATAM, Diabetes, Santiago, Chile, 2Medtronic Diabetes, Bakken Research Center, Maastricht, Netherlands, 3Medtronic Bakken Research Center, Medtronic Diabetes Emea, Maastricht, Netherlands, 4Pontificia Universidad Católica De Chile, Nutrición, Diabetes Y Metabolismo, Santiago, Chile, 5Hospital Universitario San Ignacio, Endocrinology Unit, Bogota, Colombia, 6Santa Casa de São Paulo School of Medical Sciences, Pediatric Endocrinology Unit, São Paulo, Brazil, 7CPCLIN‐DASA Clinical Research Center, Department Of Diabetes, São Paulo, Brazil, 8Hospital Universitario Austral, Hospital Materno Infantil De Tigre, Buenos Aires, Argentina, 9Medtronic International Trading Sàrl, Medtronic Diabetes Emea, Tolochenaz, Switzerland
1. Choudhary P et al. Lancet Diabetes Endocrinol. 2022;10(10):720–731.
2. Arrieta A et al. Diabetes Obes Metab.2022;24(7):1370–1379.
3. Collyns OJ et al. Diabetes Care. 2021;44(4):969–975.
4. Akturk HK et al. Diabetes Care. 2021;44(6):e121–e123.
5. Phillip M et al. Endocr Rev. 2022; doi:10.1210/endrev/bnac022.
Topic:
AS03 Closed‐loop System and Algorithm
1University Hospitals of Derby and Burton NHS Trust, Department Of Diabetes & Endocrinology, Derby, United Kingdom, 2University of Nottingham, School Of Medicine, Nottingham, United Kingdom, 3University Hospital of Leicester NHS Trust, Leicester Diabetes Centres, Leicester, United Kingdom, 4University Hospitals Birmingham NHS Trust, Department Of Diabetes, Birmingham, United Kingdom, 5Guy's and St Thomas' Hospital NHS Trust, Department Of Diabetes, London, United Kingdom, 6Sheffield Teaching Hospitals NHS Trust, Department Of Diabetes, Sheffield, United Kingdom, 7Royal Surrey County Hospital NHS Foundation Trust, Department Of Diabetes & Endocrinology, Surrey, United Kingdom, 8Manchester University Hospitals NHS Trust, Department Of Diabetes, Manchester, United Kingdom, 9Oxford University Hospitals, Department Of Diabetes & Endocrinology, Oxford, United Kingdom, 10Harrogate and District NHS trust, Department Of Diabetes, Harrogate, United Kingdom, 11Sandwell and West Birmingham Hospitals NHS Trust, Department Of Diabetes & Endocrinology, Birmingham, United Kingdom, 12University of Leicester, Diabetes Research Centre, Leicester, United Kingdom
Topic:
AS03 Closed‐loop System and Algorithm
1University of Nottingham, School Of Medicine, Nottingham, United Kingdom, 2University Hospitals of Derby and Burton NHS Trust, Department Of Diabetes & Endocrinology, Derby, United Kingdom, 3University Hospital of Leicester NHS Trust, Leicester Diabetes Centres, Leicester, United Kingdom, 4Liverpool University Hospitals NHS Trust, Department Of Diabetes & Endocrinology, Liverpool, United Kingdom, 5University Hospitals Birmingham NHS Trust, Department Of Diabetes, Birmingham, United Kingdom, 6Guy's and St Thomas' Hospital NHS Trust, Department Of Diabetes, London, United Kingdom, 7Sheffield Teaching Hospitals NHS Trust, Department Of Diabetes, Sheffield, United Kingdom, 8Nottingham University Hospitals NHS Trust, Department Of Diabetes & Endocrinology, Nottingham, United Kingdom, 9Oxford University Hospitals, Department Of Diabetes & Endocrinology, Oxford, United Kingdom, 10Harrogate and District NHS trust, Department Of Diabetes, Harrogate, United Kingdom, 11Sandwell and West Birmingham Hospitals NHS Trust, Department Of Diabetes & Endocrinology, Birmingham, United Kingdom, 12University of Leicester, Diabetes Research Centre, Leicester, United Kingdom
Topic:
AS03 Closed‐loop System and Algorithm
1Jagiellonian University Medical College, Metabolic Diseases, Kraków, Poland, 2Jagiellonian University Medical College, Of Psychiatry, Kraków, Poland, 3Jagiellonian University Medical College, Of Metabolic Diseases, Kraków, Poland, 4Clinical Provincial Hospital of Frederic Chopin No. 1 in Rzeszów, Clinical Provincial Hospital Of Frederic Chopin No. 1 In Rzeszów, Kraków, Poland, 5Medtronic, Medtronic, Tolochenaz, Switzerland, 6Hospital University in Krakow, Metabolic Diseases Clinic, Kraków, Poland
Topic:
AS03 Closed‐loop System and Algorithm
University Hospital of Verona, Department Of Medicine, Section Of Endocrinology, Diabetes And Metabolism, Verona, Italy
Topic:
AS03 Closed‐loop System and Algorithm
1Giovanni XXIII Children's Hospital, Metabolic Disorders And Genetic Diseases Unit, Bari, Italy, 2University and Azienda Ospedaliera Universitaria Integrata of Verona, Department Of Surgery, Dentistry, Pediatrics And Gynecology, Section Of Pediatric Diabetes And Metabolism, Verona, Italy, 3University of Messina, Messina, Italy, Department Of Human Pathology In Adult And Developmental Age “gaetano Barresi”, Messina, Italy, 4San Raffaele Scientific Hospital and Vita Salute San Raffaele University, Pediatric Diabetes Unit, Milan, Italy, 5S. Chiara Hospital of Trento, Pediatric Department, Trento, Italy, 6Institute for Maternal and Child Health IRCCS “Burlo Garofolo”, Department Of Pediatrics, Trieste, Italy, 7Maggiore Hospital, Pediatric Department, Crema, Italy, 8University of the Study of Campania “Luigi Vanvitelli”, Regional Center For Pedatric Diabetes, Department Of Pediatrics, Naples, Italy, 9SS Antonio and Biagio and Cesare Arrigo, Pediatric Unit, Alessandria, Italy, 10Perrino Hospital, Pediatric Unit, Brindisi, Italy, 11Saint Paul Hospital, Pediatric Unit, Savona, Italy, 12“Casa Sollievo Della Sofferenza” Reserach Hospital, Pediatric Unit, San Giovanni Rotondo, Italy, 13Brotzu Hospital, Pediatric Unit, Cagliari, Italy
Topic:
AS03 Closed‐loop System and Algorithm
1DiappyMed, ‐, Montpellier, France, 2Montpellier University Hospital, University of Montpellier, Department Of Endocrinology And Diabetes, Montpellier, France, 3Institute of Functional Genomics, Cnrs Umr 5203, Inserm U1191, Montpellier, France
Topic:
AS03 Closed‐loop System and Algorithm
1T1D Exchange, Quality Improvement And Population Health, Boston, United States of America, 2Rady Children's Hospital, University of California, San Diego, CA, Endocrinology, San Diego, United States of America, 3Children's Mercy Hospital, Endocrinology And Diabetes, Kansas City, United States of America, 4Children National Hospital, Endocrinology, Washington, United States of America, 5University of Washington, Endocrinology, Seattle, United States of America, 6Cook Children Hospital, Endocrinology, Fort Worth, United States of America, 7Grady Memorial Hospital, Endocrinology, Atlanta, United States of America, 8Nationwide Children Hospital, Endocrinology, Columbus, United States of America
Topic:
AS03 Closed‐loop System and Algorithm
M. Siket1,2,3, K. Novák1,2,
1Óbuda University, Applied Informatics And Applied Mathematics Doctoral School, Budapest, Hungary, 2Óbuda University, Physiological Controls Research Center, Budapest, Hungary, 3Institute for Computer Science and Control, Computational Optical Sensing And Processing Laboratory, Budapest, Hungary, 4Óbuda University, Biomatics And Applied Artificial Intelligence Institute, John Von Neumann Faculty Of Informatics, Budapest, Hungary
Topic:
AS03 Closed‐loop System and Algorithm
1University of California San Francisco, Pediatrics, San Francisco, United States of America, 2Barbara Davis Center for Diabetes, Pediatrics, Aurora, United States of America, 3Yale School of Medicine, Pediatrics, New Haven, United States of America, 4Baylor College of Medicine, Diabetes And Endocrinology, Houston, United States of America, 5Stanford University, Pediatric Endocrinology, Palo Alto, United States of America, 6University of Virginia, Division Of Endocrinology, Center For Diabetes Technology, Charlottesville, United States of America, 7Insulet Corporation, Medical Affairs, Acton, United States of America
Topic:
AS03 Closed‐loop System and Algorithm
Ain Shams University, Department Of Pediatrics, Cairo, Egypt
Topic:
AS03 Closed‐loop System and Algorithm
1Koç University, Department Of Pediatric Endocrinology And Diabetes, istanbul, Turkey, 2Koç University, School Of Medicine, istanbul, Turkey
Topic:
AS03 Closed‐loop System and Algorithm
1Badajoz University Hospital, Endocrinology And Nutrition, Badajoz, Spain, 2Badajoz University Hospital, Endocrinology And Nutrition. Diabetes Technology Unit, BADAJOZ, Spain
Topic:
AS03 Closed‐loop System and Algorithm
1Universidad de Antioquia, Department Of Endocrinology And Metabolism, Medellin, Colombia, 2University of Virginia, Division Of Endocrinology, Center For Diabetes Technology, Charlottesville, United States of America
Topic:
AS03 Closed‐loop System and Algorithm
1Endocrine Research Society, Endocrinology, Vancouver, Canada, 2Division of Endocrinology, Department Of Medicine, University Of British Columbia, Vancouver, Canada
Topic:
AS03 Closed‐loop System and Algorithm
Singapore General Hospital, Endocrinology, Singapore, Singapore
Topic:
AS03 Closed‐loop System and Algorithm
M.C. Serafini, N. Rosales,
Grupo de Control Aplicado (GCA), Instituto Leici, Facultad De Ingenieria, Unlp‐conicet, La Plata, Argentina
‐ Test 1: piecewise reward.
‐ Test 2: exponentially shaped, continuous reward.
‐ Test 3: same as test 2 with a factor to give pre‐meal hyperglycemia more negative reward.
[1] Serafini, Rosales, Garelli, (2022) “Long‐Term Adaptation of Closed‐Loop Glucose Regulation Via Reinforcement Learning Tools”, IFAC‐PapersOnLine 55,7, pp. 649–654.
The ah‐hoc strategy has good average %TIR but does not avoid hypoglycemic episodes successfully. All adaptation schemes using RL agents avoid hypoglycemic episodes, but the ones trained under shaped rewards also achieve better %TIR (avoiding hyperglycemia). When adding a reward discount for premeal hyperglycemia (Test 3) this further improves.
Topic:
AS03 Closed‐loop System and Algorithm
F. Bianchi1, R. Sánchez Peña1,
1ITBA, Control, Buenos Aires, Argentina, 2LEICI (UNLP‐CONICET), Ee, La Plata, Argentina
Topic:
AS03 Closed‐loop System and Algorithm
1Pontificia Universidad Católica de Chile, Department Of Diabetes, Santiago, Chile, 2Pontificia Universidad Javeriana, Department Of Diabetes, Bogota, Colombia, 3Hospital Universitario San Ignacio, Endocrinology Unit, Bogota, Colombia, 4Santa Casa de São Paulo School of Medical Sciences, Pediatric Endocrinology Unit, São Paulo, Brazil, 5CPCLIN‐DASA Clinical Research Center, Department Of Diabetes, São Paulo, Brazil, 6Hospital Universitario Austral, Hospital Materno Infantil De Tigre, Buenos Aires, Argentina, 7Medtronic, Medtronic Diabetes Latam, Santiago, Chile, 8Medtronic, Medtronic Diabetes Global, Northridge, United States of America, 9Medtronic Bakken Research Center, Medtronic Diabetes Emea, Maastricht, Netherlands, 10Medtronic International Trading Sàrl, Medtronic Diabetes Emea, Tolochenaz, Switzerland
Topic:
AS03 Closed‐loop System and Algorithm
IKEM, Diabetes Centre, Prague, Czech Republic
The HbA1c significantly decreased in PC (by 13%) but did not change in WC. TIR increased similarly in both groups. Pts in WC experienced a greater decrease in TBR compared to PC (by 61% vs. 11%) and in SD value (by 14% vs. 11%). The daily dose of insulin (DDI) during HCL rose in both groups, surprisingly more in WC one (by 15% vs. 10%).
Topic:
AS03 Closed‐loop System and Algorithm
University of Padova, Department Of Information Engineering (dei), Padova, Italy
Topic:
AS03 Closed‐loop System and Algorithm
1UMC Utrecht, Internal Medicine, Utrecht, Netherlands, 2Rijnstate Hospital, Internal Medicine, Arnhem, Netherlands, 3Julius Center for Health Sciences and Primary Care, Umc Utrecht, Utrecht University, Utrecht, Netherlands, 4Diabetes Federation Netherlands, Diabetes, Amersfoort, Netherlands, 5Amsterdam UMC, Internal Medicine, Amsterdam, Netherlands, 6Amsterdam UMC, Medical Psychology, Amsterdam, Netherlands
The primary aim of the DARE‐study is to evaluate long‐term effects on glycaemic control, PROMs and cost‐effectiveness of DHFCL compared with currently most advanced technological care (i.e. HCL) and most used care (i.e. multiple daily insulin injections (MDI) in combination with FGM/CGM).
Topic:
AS03 Closed‐loop System and Algorithm
1School of Örebro University, Diabetes, Endocrinology And Metabolism, Örebro, Sweden, 2Medtronic International Trading SARL, Reimbursement & Health Economics, Diabetes, Tolochenaz, Switzerland, 3Medtronic International Trading SARL, Medical Affairs, Tolochenaz, Switzerland
1. Choudhary P, et al. Lancet Diabetes Endocrinol 2022
Topic:
AS03 Closed‐loop System and Algorithm
A. Thode Reenberg1, T. Ritschel1, E. Lindkvist2, C. Laugesen2, J. Svensson2, A. Ranjan2, K. Nørgaard2,
1Technical University of Denmark, Department Of Applied Mathematics And Computer Science, Kgs. Lyngby, Denmark, 2Steno Diabetes Center Copenhagen, Clinical Research, Herlev, Denmark
Topic:
AS03 Closed‐loop System and Algorithm
1Wellcome‐MRC Institute of Metabolic Science, University Of Cambridge, Cambridge, United Kingdom, 2Wolfson Diabetes and Endocrine Clinic, Cambridge University Hospitals Nhs Foundation Trust, Cambridge, United Kingdom
Topic:
AS03 Closed‐loop System and Algorithm
University Hospitals of Derby and Burton NHS Trust, Department Of Diabetes & Endocrinology, Derby, United Kingdom
Topic:
AS03 Closed‐loop System and Algorithm
Hospital Regional Universitario de Málaga, Servicio De Endocrinología Infantil, Málaga, Spain
Topic:
AS03 Closed‐loop System and Algorithm
1Hospital University in Krakow, Metabolic Diseases Clinic, Kraków, Poland, 2Jagiellonian University Medical College, Of Metabolic Diseases, Kraków, Poland, 3Clinical Provincial Hospital of Frederic Chopin No. 1 in Rzeszów, Clinical Provincial Hospital Of Frederic Chopin No. 1 In Rzeszów, Kraków, Poland, 4Medtronic, Medtronic, Northridge, United States of America, 5Medtronic, Medtronic, Tolochenaz, Switzerland
Topic:
AS03 Closed‐loop System and Algorithm
1Swansea University, Applied Sports, Technology, Exercise And Medicine (a‐stem) Research Centre, Swansea, United Kingdom, 2Steno Diabetes Center Copenhagen, Diabetes Technology, Herlev, Denmark, 3Danish Diabetes Academy, Danish Diabetes Academy, Odense, Denmark, 4Swansea University, Faculty Of Medicine, Swansea, United Kingdom, 5University of Copenhagen, Faculty Of Health And Medical Sciences, Copenhagen, Denmark
Topic:
AS03 Closed‐loop System and Algorithm
Monash University, Monash Centre For Health Research And Implementation, Clayton, Australia
Topic:
AS03 Closed‐loop System and Algorithm
O. Bitterman1,
1ASL Roma 4 S. Paolo Hospital, Diabetology Unit, Civitavecchia, Italy, 2Israelitico Hospital, Diabetology Unit, Roma, Italy, 3ASL Salerno, Endocrinology And Diabetes Unit, Salerno, Italy, 4Sapienza University, Department Of Experimental Medicine, Rome, Italy, 5ASL Latina, Diabetology Unit, Latina, Italy
Topic:
AS03 Closed‐loop System and Algorithm
Hospital Universitario Ramón y Cajal, Endocrinology And Nutrition Department, Madrid, Spain
Topic:
AS03 Closed‐loop System and Algorithm
1Shamir (Assaf Harofeh) Medical Center, Pediatric Endocrinology And Diabetes Institute, Zerifin, Israel, 2Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel, 3University of Bristol, Population Health Sciences, Bristol Medical School, Bristol, United Kingdom, 4Pediatric Endocrinology and Diabetes Unit, “dana‐dwek” Children's Hospital, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel, 5Edmond and Lily Safra Children's Hospital, Sheba Medical Center, Pediatric Endocrine And Diabetes Unit, Ramat Gan, Israel, 6Maccabi Healthcare Services, Juvenile Diabetes Center, Raanana, Israel, 7Soroka University Medical Center, Pediatric Endocrinology And Diabetes Unit, Beer Sheva, Israel, 8Ben‐Gurion University of the Negev, The Faculty Of Health Sciences, Beer Sheva, Israel, 9Edith Wolfson Medical Center, Pediatric Endocrinology Unit, Holon, Israel
Topic:
AS03 Closed‐loop System and Algorithm
1Pontificia Universidad Católica De Chile, Nutrición, Diabetes Y Metabolismo, Santiago, Chile, 2Pontificia Universidad Católica De Chile, Nutrición, Diabetes Y Metabolismo, Providencia, Chile
Topic:
AS03 Closed‐loop System and Algorithm
Research Institute of the McGill University Health Centre, Experimental Medicine, Montreal, Canada
Topic:
AS03 Closed‐loop System and Algorithm
Research Institute of the McGill University Health Centre, Experimental Medicine, Montreal, Canada
Topic:
AS03 Closed‐loop System and Algorithm
1University of Messina, Department Of Human Pathology, Messina, Italy, 2Hospital San Raffaele, Diabetes Research Insitute, Milano, Italy, 3Giovanni XXIII Children's Hospital, Metabolic Disorders And Genetic Diseases Unit, Bari, Italy, 4Federico II University of Naples, Department Of Translational Medical Science, Napoli, Italy, 5University and Azienda Ospedaliera Universitaria Integrata of Verona, Department Of Surgery, Dentistry, Pediatrics And Gynecology, Section Of Pediatric Diabetes And Metabolism, Verona, Italy
Topic:
AS03 Closed‐loop System and Algorithm
1Children's National Hospital, Division Of Endocrinology And Diabetes, Washington, United States of America, 2Children's National Hospital, Division Of Psychiatry, Washington, United States of America, 3Children's Hospital of Philadelphia, Division Of Endocrinology And Diabetes, Philadelphia, United States of America
Topic:
AS03 Closed‐loop System and Algorithm
1Sidra Medicine, Diabetes And Endocrine, Doha, Qatar, 2Medtronic Diabetes, Maastricht, Maastricht, Netherlands
Topic:
AS03 Closed‐loop System and Algorithm
1Pohang University of Science and Technology, Convergence It Engineering, Pohang, Korea, Republic of, 2Curestream, Research, Seoul, Korea, Republic of
Topic:
AS03 Closed‐loop System and Algorithm
1Pohang University of Science and Technology, Convergence It Engineering, Pohang, Korea, Republic of, 2Curestream, Research, Seoul, Korea, Republic of
Topic:
AS03 Closed‐loop System and Algorithm
1Medical University of Silesia, Department Of Children's Diabetology, Katowice, Poland, 2Institute of Medical Sciences, University of Opole, Department Of Pediatrics, Opole, Poland, 3Silesian University of Technology, Department Of Data Science And Engineering, Gliwice, Poland
AvgSG and GMI decreased significantly between SAP and AHCL (p < 0.05). The sensor glucose profile shifted significantly towards the target TIR (70–180 mg/dl) (p < 0.05).
Topic:
AS03 Closed‐loop System and Algorithm
1Telethon Kids Institute, Children's Diabetes Centre, Nedlands, Australia, 2Perth Children's Hospital, Endocrinology And Diabetes, Nedlands, Australia
Topic:
AS03 Closed‐loop System and Algorithm
V. Mohan1, S. Jain2, G. Jevalikar3, A. Kulkarni4, K. Swaminathan5, A. Arrieta6, J. Shin7,
1Madras Diabetes Research Foundation & Dr. Mohan's Diabetes Specialties Centre, Diabetology, Chennai, India, 2TOTALL Diabetes Hormone Institute, Diabetes And Endocrinology, Indore, India, 3Max Super Specialty Hospital, Pediatric Endocrinology, Delhi, India, 4SRCC Children's Hospital & Sir HN Reliance Foundation Hospital, Pediatric And Adolescent Endocrinology, Mumbai, India, 5Idhayangal Charitable Trust, Madhuram Diabetes And Thyroid Centre, Coimbatore, India, 6Medtronic Bakken Research Center, Clinical Research, Maastricht, Netherlands, 7Medtronic, Clinical Research, Northridge, United States of America, 8Medtronic, Medical Affairs, Northridge, United States of America
Topic:
AS03 Closed‐loop System and Algorithm
Alder Hey Children's NHS Foundation Trust, Department Of Paediatrics, Liverpool, United Kingdom
Hypoglycaemia was 3.13% at baseline and 2.31% at 18 months, mean difference ‐0.82 (CI ‐1.74 to 0.46, p = 0.082). HbA1c data was available for 38 patients. Mean HbA1c before commencing HCL was 58.7, at 18 months HbA1c mean was 55.7 – mean difference ‐3 (95% CI ‐7.8 to 1.8, p = 0.214).
Topic:
AS03 Closed‐loop System and Algorithm
1Cambridge University Hospitals NHS Foundation Trust, Weston Centre Paediatric Endocrinology And Diabetes Clinic, Cambridge, United Kingdom, 2Wolfson Diabetes and Endocrine Clinic, Cambridge University Hospitals Nhs Foundation Trust, Cambridge, United Kingdom, 3Wellcome‐MRC Institute of Metabolic Science, University Of Cambridge, Cambridge, United Kingdom
Topic:
AS04 New Insulins
1Sultan Bin Abdulaziz Humanitarian City, Research And Scientific Center, Riyadh, Saudi Arabia, 2NHS Foundation Trust, University Hospitals Of Derby And Burton Nhs Foundation Trust, Nottingham, United Kingdom, 3IQIVA, Patient‐centered Endpoint Soutions, Lyon, France, 4Sanofi, General Medicine, South Beach Tower, Singapore, 5Sanofi, Diabetes And Cardiovascular Development, R&d, Paris, France, 6IT&M Stats, Statictics, Neuilly‐sur‐Seine, France, 7Hospital de Clínicas Universidad de, Endocrinology, Buenos Aires, Argentina, 8Care Hospital, Endocrinology, Hyderabad, India
Topic:
AS04 New Insulins
1University Hospitals Leuven ‐ KU Leuven, Endocrinology, Leuven, Belgium, 2KU Leuven, Interuniversity Institute For Biostatistics And Statistical Bioinformatics, Leuven, Belgium, 3University of Antwerp – Antwerp University Hospital, Endocrinology, Diabetology & Metabolism, Edegem, Belgium, 4OLV Hospital Aalst, Endocrinology, Aalst, Belgium, 5Sanofi‐Aventis, Denmark A/s, Copenhagen, Denmark
Topic:
AS04 New Insulins
1University of Toronto, Department Of Medicine, Toronto, Canada, 2Texas Institute for Kidney and Endocrine Disorders, Endocrinology, Texas, United States of America, 3HealthPartners Institute, International Diabetes Center, Minneapolis, United States of America, 4Institute for Clinical and Experimental Medicine, Diabetes Centre, Prague, Czech Republic, 5PPD, part of Thermo Fisher Scientific, Clinical Research, Göteborg, Sweden, 6Sanofi, Global Medical Franchise, Paris, France, 7Sanofi, General Medicines, Singapore, Singapore, 8Sanofi, Global Medical Affairs And Clinical Data Science, Chilly‐Mazarin, France, 9Clinic University of Navarra, Department Of Endocrinology And Nutrition, Pamplona, Spain
Topic:
AS04 New Insulins
1University Children's Hospital, Endocrinology Department, Belgrade, Serbia, 2University Children's Hospital, Rheumatology Department, Belgrade, Serbia
Topic:
AS04 New Insulins
N. Khan1,
1Imperial College London, Diabetes Centre, Al Ain, United Arab Emirates, 2Chaim Sheba Medical Center, Tel‐Aviv University, Division Of Endocrinology, Diabetes And Metabolism, Tel Hashomer, Israel, 3Post Graduate Institute of Medical Education & Research, Division Of Endocrinology, Chandigarh, India, 4Universidad del Cauca, Division Of Endocrinology And Metabolism, Department Of Internal Medicine, Popayan, Colombia, 5Western University, Epidemiology And Biostatistics, London, Canada, 6IQIVA, Patient‐centered Endpoint Soutions, Lyon, France, 7Sanofi, General Medicines, Singapore, Singapore, 8Sanofi, Biostatistics And Programming, Paris, France, 9Sanofi, Diabetes And Cardiovascular Development, R&d, Paris, France, 10Endocrinology Research Centre of Health Care Ministry of Russian Federation, Department Of Diabetic Foot, Moscow, Russian Federation
Topic:
AS04 New Insulins
1Chaim Sheba Medical Center, Tel‐Aviv University, Division Of Endocrinology, Diabetes And Metabolism, Tel Hashomer, Israel, 2G.D Hospital and Diabetes Institute, Diabetes Care Services, Kolkata, India, 3Universidad del Cauca, Division Of Endocrinology And Metabolism, Department Of Internal Medicine, Popayan, Colombia, 4Sanofi, General Medicines, Singapore, Singapore, 5IviData, Life Sciences, Levallois‐Perret, France, 6Sanofi, Global Pharmacovigilance – Immunology & Inflammation, New Jersey, United States of America, 7Endocrinology Research Centre of Health Care Ministry of Russian Federation, Department Of Diabetic Foot, Moscow, Russian Federation
Topic:
AS05 Artificial Pancreas
M. Louis1, H. Romero‐Ugalde2,
1Diabeloop, Research And Development, Grenoble, France, 2Diabeloop, Isère, Grenoble, France
Topic:
AS05 Artificial Pancreas
1University of Bern, Institute Of Primary Health Care (biham), Bern, Switzerland, 2University of Bern, Graduate School For Health Sciences, Bern, United Kingdom, 3University of Cambridge, Wellcome Mrc Institute Of Metabolic Science, Cambridge, United Kingdom, 4University of Cambridge, Department Of Paediatrics, Cambridge, United Kingdom, 5Department of Diabetes and Endocrinology, Cambridge University Hospitals Nhs Foundation Trust, Cambridge, United Kingdom, 6Bern University Hospital, Department Of Diabetes, Endocrinology, Nutritional Medicine And Metabolism, Bern, Switzerland, 7UZ‐VUB, Department Of Paediatric Endocrinology, Jette, Belgium, 8Centre Hospitalier de Luxembourg, Deccp, Clinique Pédiatrique, Luxembourg, Luxembourg, 9Oxford University Hospitals NHS, Nihr Oxford Biomedical Research Centre, Oxford, United Kingdom, 10University of Oxford, Department Of Paediatrics, Oxford, United Kingdom, 11Leeds Children's Hospital, Department Of Paediatric Diabetes, Leeds, United Kingdom, 12Medical University of Graz, Department Of Pediatric And Adolescent Medicine, Graz, Austria, 13Alder Hey Children's NHS Foundation Trust, Department Of Paediatrics, Liverpool, United Kingdom, 14Department of Pediatrics I,, Medical University Of Innsbruck,, Innsbruck, Austria, 15University of Leipzig, Hospital For Children And Adolescents, Leipzig, Germany, 16Manchester University NHS Foundation Trust, Diabetes, Endocrinology And Metabolism Centre, Manchester, United Kingdom, 17University of Manchester, Division Of Diabetes, Endocrinology And Gastroenterology, Manchester, United Kingdom, 18Medical University of Graz, Division Of Endocrinology And Diabetology, Graz, Austria, 19Institute of Immunology and Immunotherapy, University Of Birmingham, Birmingham, United Kingdom, 20Birmingham Children's Hospital, Department Of Endocrinology And Diabetes, Birmingham, United Kingdom, 21Medical University of Vienna, Department Of Paediatrics And Adolescent Medicine, Vienna, Austria, 22Manchester Academic Health Science Centre, Diabetes, Endocrinology And Metabolism Centre, Manchester, United Kingdom, 23Nottingham University Hospitals NHS Trust, Department Of Paediatric Diabetes And Endocrinology, Nottingham, United Kingdom, 24Southampton Children's Hospital, Department Of Paediatric Endocrinology And Diabetes, Southampton, United Kingdom
Topic:
AS05 Artificial Pancreas
T. Gautier, M. Gerber,
Dexcom Inc, Typezero, Charlottesville, United States of America
Topic:
AS05 Artificial Pancreas
1Dexcom Inc, Typezero, Charlottesville, United States of America, 2Dexcom, Inc., Clinical Affairs, San Diego, United States of America, 3University of Otago, Paediatrics, New Zealand, New Zealand, 4University of Otago, Department Of Paediatrics, Christchurch, New Zealand
Topic:
AS05 Artificial Pancreas
1St. Olavs University Hospital, Dept. Of Endecrinology, Trondheim, Norway, 2St. Olavs University Hospital, Dept. Of Endocrinology, Trondheim, Norway
Topic:
AS05 Artificial Pancreas
1University of Padova, Department Of Women's And Children's Health, Padova, Italy, 2University of Pavia, Department Of Industrial And Information Engineering, Pavia, Italy
Topic:
AS05 Artificial Pancreas
1University of California San Francisco, Pediatrics, San Francisco, United States of America, 2University of Colorado Anschutz Medical Campus, Barbara Davis Center, Aurora, United States of America, 3Stanford University School of Medicine, Department Of Pediatrics, Palo Alto, United States of America, 4Stanford University, Division Of Pediatric Endocrinology, Department Of Pediatrics, Stanford, United States of America, 5University of Virginia, Center For Diabetes Technology, Charlottesville, United States of America, 6Barbara Davis Center for Diabetes, Pediatrics, Aurora, United States of America, 7University of Virginia, Pediatrics, Charlottesville, United States of America, 8Jaeb Center for Health Research, Diabetes, Tampa, United States of America
Topic:
AS05 Artificial Pancreas
J. Miragall1,
1Universitat Politècnica de València, Instituto Universitario De Automática E Informática Industrial, Valencia, Spain, 2Instituto de Salud Carlos III, Centro De Investigación Biomédica En Red De Diabetes Y Enfermedades Metabólicas Asociadas, Madrid, Spain
Topic:
AS05 Artificial Pancreas
1University of North Carolina, Health Sciences, Chapel Hill, United States of America, 2Creighton University, Pharmacy And Health Professions, Omaha, United States of America
Topic:
AS05 Artificial Pancreas
1Polbionica Ltd, Laboratory, Warsaw, Poland, 2Foundation of Research and Science Development, Foundation Of Research And Science Development, Warsaw, Poland, 3Medical University of Warsaw, Chair And Department Of Histology And Embryology, Warsaw, Poland, 4Medical University of Warsaw, Department Of Transplantology And Central Tissue Bank, Warsaw, Poland, 5Center for Pathomorphological Diagnostics Ltd, Center For Pathomorphological Diagnostics Ltd, Warsaw, Poland
Topic:
AS05 Artificial Pancreas
P. Patil1,
1National University of Science and Technology, Department Of Engineering Cybernetics, Høgskoleringen, Norway, 2National University of Science and Technology, Department Of Engineering Cybernetics, Høgskoleringen, Norway
Topic:
AS05 Artificial Pancreas
University of Padova, Department Of Information Engineering, Padova, Italy
The effectiveness of the two strategies is assessed in‐silico on the UVa/Padova T1D simulator. The test lasts 90 days and is performed on 50 subjects each dealing with 3 random nighttime faulty episodes.
Topic:
AS05 Artificial Pancreas
1Hospital Clínic de Barcelona, Endocrinology And Nutrition Department, Barcelona, Spain, 2University of Girona, Institute Of Informatics And Applications, Girona, Spain, 3Universitat Politècnica de València, Instituto Universitario De Automática E Informática Industrial, Valencia, Spain, 4Hospital Clínic de Barcelona, Fundació Clínic Per A La Recerca Biomèdica, Barcelona, Spain, 5Instituto de Salud Carlos III, Centro De Investigación Biomédica En Red De Diabetes Y Enfermedades Metabólicas Asociadas, Madrid, Spain, 6Institut d'investigacions biomèdiques August Pi i Sunyer, Idibaps, Barcelona, Spain
Topic:
AS05 Artificial Pancreas
M. Gerber1, T. Gautier1, E. Campos‐Nanez1, M. Collins1, C. Steele1, T. Walker2,
1Dexcom Inc, Typezero, Charlottesville, United States of America, 2Dexcom, Inc., Clinical Affairs, San Diego, United States of America, 3University of Otago, Paediatrics, New Zealand, New Zealand, 4University of Otago, Department Of Paediatrics, Christchurch, New Zealand
Topic:
AS05 Artificial Pancreas
S. Savastio1, R. Bonfanti2, C. Gorla1, E. Pozzi1, V. Castorani2, R. Di Tonno2, A. Scaramuzza3, A. Colasanto4, J.D. Coïsson4, M. Arlorio4,
1University of Piemonte Orientale, Department Of Health Sciences, Novara, Italy, 2San Raffaele Scientific Hospital and Vita Salute San Raffaele University, Pediatric Diabetes Unit, Milan, Italy, 3ASST Cremona, Division Of Pediatrics, Pediatric Diabetes, Endocrinology And Nutrition, Cremona, Italy, 4University of Piemonte Orientale, Department Of Drug Science, Novara, Italy
Topic:
AS05 Artificial Pancreas
1University of Virginia, Pediatrics, Charlottesville, United States of America, 2University of Virginia, Center For Diabetes Technology, Charlottesville, United States of America
Topic:
AS05 Artificial Pancreas
Stevens Institute of Technology, Computer Science, Hoboken, United States of America
Topic:
AS05 Artificial Pancreas
University of Virginia, Center For Diabetes Technology, Charlottesville, United States of America
Topic:
AS05 Artificial Pancreas
1Medical University of Vienna, Department Of Pediatrics And Adolescent Medicine, Vienna, Austria, 2Medical University of Graz, Department Of Internal Medicine, Graz, Austria, 3Medical University of Graz, Division Of Endocrinology & Diabetology, Graz, Austria
Topic:
AS06 Glucose sensors
1Dexcom, Inc., Data Science, Edinburgh, United Kingdom, 2Dexcom, Inc., Medical Affairs, San Diego, United States of America
Topic:
AS06 Glucose sensors
A. Al Hayek,
prince sultan military medical city, Endocrinology, Riyadh, Saudi Arabia
Topic:
AS06 Glucose sensors
1King Fahad Medical City, Obesity, Endocrine, And Metabolism Center, Riyadh, Saudi Arabia, 2King Fahad Medical City, Diabetes, Riyadh, Saudi Arabia
Topic:
AS06 Glucose sensors
1Dexcom, Inc, Health Economics And Outcomes Research, San Diego, United States of America, 2Vyoo Agency, Global Health Economics, Lyon, France, 3Centre Hospitalier Universitaire de Caen Normandie, Centre De Recherche Clinique‐chu Caen, Caen, France, 4Dexcom Inc., Global Access, San Diego, United States of America
Topic:
AS06 Glucose sensors
1Hospital Gregorio Marañón, Endocrinology, Madrid, Spain, 2Gregorio Marañon Hospital, Endocrinology, Madrid, Spain, 3Hospital Infanta Elena, Endocrinology, Valdemoro (Madrid), Spain
Topic:
AS06 Glucose sensors
Complejo Asistencial Universitario de León, Endocrinología Y Nutrición, Leon, Spain
Topic:
AS06 Glucose sensors
G. López Gallardo,
Hospital Universitario Virgen del Rocío, Endocrinology, Sevilla, Spain
Topic:
AS06 Glucose sensors
State University of Santa Catarina, Department Of Electrical Engineering, Joinville, Brazil
Topic:
AS06 Glucose sensors
1University Hospitals Leuven ‐ KU Leuven, Endocrinology, Leuven, Belgium, 2University of Antwerp – Antwerp University Hospital, Endocrinology, Diabetology & Metabolism, Edegem, Belgium, 3OLV Hospital Aalst, Endocrinology, Aalst, Belgium
Topic:
AS06 Glucose sensors
M. Qureshi1, S. Bain2, S. Luzio2, C. Handy1, B. Love3, N. Silva4, L. Ferreira4, K. Wareham5, L. Barlow5, G. Dunseath2, J. Crane2, I. Masso1, J. Ryan1,
1Afon Technology Ltd, Research And Development, Caldicot, United Kingdom, 2Diabetes Research Group, Faculty Of Medicine, Swansea, United Kingdom, 3Citalytics Corporation, N/a, Towson, United States of America, 4UnifAI Technology Limited, N/a, London, United Kingdom, 5Joint Clinical Research Facility, Research Facility, Swansea, United Kingdom
This model resulted in a 95% accuracy in predicting glucose levels above 8 mmol/L.
Topic:
AS06 Glucose sensors
J. Ling1, R. Ma1,2, A. Luk1,3, J. Chan2, C.C. Szeto1, J. Ng1,
1The Chinese University of Hong Kong, Medicine And Therapeutics, Hong Kong, Hong Kong PRC, 2The Chinese University of Hong Kong, Li Ka Shing Institute Of Health Sciences, Hong Kong, Hong Kong PRC, 3The Chinese University of Hong Kong, Phase 1 Clinical Trial Centre, Hong Kong, Hong Kong PRC
Topic:
AS06 Glucose sensors
1Federal University of State of Rio de Janeiro, Medical Clinic, Copacabana, Brazil, 2Federal University of State of Rio de Janeiro, Medical Clinic, Tijuca, Brazil
Topic:
AS06 Glucose sensors
1Antwerp University Hospital, Endocrinology, Diabetes And Metabolism, Edegem, Belgium, 2University of Antwerp, Laboratory Of Experimental Medicine And Pediatrics, Wilrijk, Belgium, 3University Hospital Antwerp, Endocrinology‐diabetology‐metabolism, Edegem, Belgium, 4Indigo Diabetes N.V., Medical Devices, Ghent, Belgium, 5University of Antwerp, Laboratory Of Experimental Medicine And Pediatrics And Member Of The Infla‐med Centre Of Excellence, Antwerp, Belgium
Topic:
AS06 Glucose sensors
1Amsterdam UMC, Medical Psychology, Amsterdam, Netherlands, 2Amsterdam UMC, Internal Medicine, Amsterdam, Netherlands, 3Medical Center Leeuwarden, Internal Medicine, Leeuwarden, Netherlands, 4Noordwest hospitals, Internal Medicine, Alkmaar, Netherlands
Topic:
AS06 Glucose sensors
1University of Bari Aldo Moro, Section Of Internal Medicine, Endocrinology, Andrology And Metabolic Diseases, Department Of Emergency And Organ Transplantation, Bari, Italy, 2University Magna Graecia, Department Of Health Science, Catanzaro, Italy, 3University Hospital Mater Domini, Diabetes Care Center, Catanzaro, Italy, 4University Magna Graecia, Department Of Clinical And Experimental Medicine, Catanzaro, Italy
Topic:
AS06 Glucose sensors
L. Di Gioia,
University of Bari Aldo Moro, Section Of Internal Medicine, Endocrinology, Andrology And Metabolic Diseases, Department Of Emergency And Organ Transplantation, Bari, Italy
Topic:
AS06 Glucose sensors
P. Perez Lopez1,
1HOSPITAL CLINICO UNIVERSITARIO DE VALLADOLID, Endocrinology And Nutrition, VALLADOLID, Spain, 2HOSPITAL CLINICO UNIVERSITARIO DE VALLADOLID, Pedriatics, VALLADOLID, Spain
Topic:
AS06 Glucose sensors
A. Kingsnorth1,
1University of Leicester, Diabetes Research Centre, Leicester, United Kingdom, 2Kings College London, Department Of Diabetes, London, United Kingdom, 3Novo Nordisk, Data Science, Department Of Pharmacometrics, Copenhagen, Denmark, 4University of Southern Denmark, Department Of Psychology, Odense, Denmark, 5Novo Nordisk, Medical & Science, Patient Focused Drug Developement, Soborg, Denmark, 6Maastricht University, Carim School For Cardiovascular Diseases, Maastricht, Netherlands, 7Radboud University Medical Center, Internal Medicine, Nijmegen, Netherlands, 8Nordsjællands Hospital Hillerød, Department Of Endocrinology And Nephrology, Hillerød, Denmark, 9University of Copenhagen, Institute Of Clinical Medicine, Copenhagen, Denmark, 10University of Dundee, School of Medicine, Systems Medicine, School Of Medicine, Dundee, United Kingdom, 11Montpellier University Hospital, University of Montpellier, Department Of Endocrinology, Diabetes And Nutrition, Montpellier, France, 12Institute of Functional Genomics, Cnrs Umr 5203, Inserm U1191, Montpellier, France, 13University of Sheffield, University Of Sheffield, Sheffield, United Kingdom, 14University of Cambridge, Wellcome Trust Mrc Institute Of Metabolic Science And Department Of Medicine, Cambridge, United Kingdom, 15Medical University of Graz, Division Of Endocrinology And Diabetology, Graz, Austria, 16Steno Diabetes Center Odense, Steno Diabetes Center Odense (sdco), Odense, Denmark
Topic:
AS06 Glucose sensors
1University of Dundee, School of Medicine, Division Of Molecular And Clinical Medicine, Dundee, United Kingdom, 2Newcastle University, Population Health Sciences Institute Faculty Of Medical Sciences, Newcastle upon Tyne, United Kingdom
Topic:
AS06 Glucose sensors
Abbott Diabetes Care, R&d, Alameda, United States of America
Topic:
AS06 Glucose sensors
1Hospital Universitario San Ignacio, Endocrinology Unit, Bogota, Colombia, 2Pontificia Universidad Javeriana, Medicine Faculty, bogota, Colombia, 3Pontificia Universidad Católica de Chile, Department Of Nutrition, Diabetes And Metabolism, Santiago de Chile, Chile
Topic:
AS06 Glucose sensors
J. Welsh1,
1Dexcom, Inc., Medical Affairs, San Diego, United States of America, 2Endocrinology & Diabetes Specialists of Northwest Ohio, Diabetes, Findlay, United States of America
Topic:
AS06 Glucose sensors
Badajoz University Hospital, Endocrinology And Nutrition, Badajoz, Spain
Topic:
AS06 Glucose sensors
1Silkeborg Regional Hospital, Diagnostic Centre, Silkeborg, Denmark, 2Department of Public Health, Section For Biostatistics, Aarhus, Denmark
Topic:
AS06 Glucose sensors
Abbott, Abbott Diabetes Care, Alameda, United States of America
Topic:
AS06 Glucose sensors
Abbott, Abbott Diabetes Care, Alameda, United States of America
Topic:
AS06 Glucose sensors
N. Alikhanova1,2, F. Takhirova1,2,
1Republican specialized scientific‐practical medical center of endocrinology named after academician Y.Kh.Turakulov, Diabetology, Tashkent, Uzbekistan, 2Center to support a healthy lifestyle and increase physical activity of the population, Metabolism, Tashkent, Uzbekistan
Topic:
AS06 Glucose sensors
Evangelismos General Hospital, Endocrinology And Diabetology Department, athens, Greece
Topic:
AS06 Glucose sensors
1M. Iashvili Children's Central Hospital, Endocrinology And Diabetes, Tbilisi, Georgia, 2University Trust, Region Skåne, Office Of Medical Services, Lund, Sweden, 3Lund University, Faculty Of Medicine, Lund, Sweden
Topic:
AS06 Glucose sensors
PAPAGEORGIOU GENERAL HOSPITAL, Third Department Of Internal Medicine, THESSALONIKI, Greece
In Case 1, BeAM: 68mg/dl and Fasting Plasma Glucose (FPG): 133mg/dl without hypoglycemia were detected (Figure 1). Prandial insulin before dinner was initiated. In Case 2, within target FPG and low BeAM (Figure 2a) were accompanied by hypoglycemia (Figure 2b). A 10% reduction in basal insulin and prandial insulin before lunch were proposed. In Case 3, BeAM :129mg/dl, FPG 131mg/dl (Figure 3a) and multiple episodes of nocturnal hypoglycemia (Figure 3b) were detected. Test for anti‐GAD antibodies was positive. Basal bolus was initiated as LADA. In Case 4, BeAM :52mg/dl, FPG :126mg/dl (Figure 4a) and hypoglycemia during nighttime and in the afternoon were detected (Figure 4b). A 10‐20% decrease in basal insulin with equal increase in prandial insulin were proposed.
Topic:
AS06 Glucose sensors
1Wayne State University, Biomedical Engineering, Detroit, United States of America, 2DSM Biomedical, Material ‐ Biology Interactions, Geleen, Netherlands, 3DSM Biomedical, Preclinical Studies, Exton, United States of America, 4University of Connecticut School of Medicine, Surgery, Farmington, United States of America
Topic:
AS06 Glucose sensors
R. Jeet Kaur1, I. Zaniletti2, B. Ozaslan3, C. Levy4, K. Castorino5, G. O'Malley6, C. Levister4, S. Rizvi1, M.M. Church5, D. Desjardins1, S. Mccrady‐Spitzer1, M.C. Trinidad7, S. Ogyaadu4, C. Reid1, W. Kremers8, F. Doyle9, J. Pinsker5, E. Dassau3,
1Mayo Clinic, Division Of Endocrinology, Diabetes, Metabolism & Nutrition, Rochester, United States of America, 2Mayo Clinic, Department Of Biomedical Statistics And Informatics, Scottdale, United States of America, 3Harvard University, John A. Paulson School Of Engineering, Boston, United States of America, 4Icahn School of Medicine, Endocrinology, NY, United States of America, 5Sansum Diabetes Research Institute, ‐, Santa Barbara, United States of America, 6Icahn School of Medicine at Mount Sinai, Endocrinology, New York, United States of America, 7Mayo Clinic, Obstetrics And Gynecology, Rochester, United States of America, 8Mayo Clinic, Department Of Biomedical Statistics And Informatics, Mayo Clinic, Rochester, United States of America, 9Harvard University, John A. Paulson School Of Engineering And Applied Sciences, Boston, United States of America
Topic:
AS06 Glucose sensors
1Ulm University, Institute Of Epidemiology And Medical Biometry, Zibmt, Ulm, Germany, 2AUF DER BULT, Diabetes‐center For Children And Adolescents, Hannover, Germany, 3St. Josefs Hospital GmbH, Heidelberg, Department Of Internal Medicine, Heidelberg, Germany, 4Darmstädter Kinderkliniken Prinzessin Margaret, Darmstadt, Darmstadt, Germany, 5Martin‐Luther University Halle‐Wittenberg, Department Of Pediatrics, Medical Faculty, Halle (Saale), Germany, 6Diabetes specialized practice for children and adolescents, Herford, Herford, Germany, 7Catholic Children's Hospital Wilhelmstift, Hamburg, Hamburg, Germany, 8University Children's Hospital, Tübingen, Tübingen, Germany, 9DZD, German Center For Diabetes Research, Neuherberg, Germany, 10University of Ulm, Institute Of Epidemiology And Medical Biometry, Zibmt, Ulm, Germany
Topic:
AS06 Glucose sensors
1AM Diabetes & Endocrinology, Endocrinology, Bartlett, United States of America, 2Physicians East, PA, Endocrinology, Greenville, United States of America, 3Diabetes and Endocrinology Specialists, Inc., Endocrinology, Chesterfield, United States of America, 4Senseonics Inc, Clinical Science, Germantown, United States of America, 5Senseonics Inc, Medical Affairs, Germantown, United States of America
Topic:
AS06 Glucose sensors
Italian National Research Centres on Aging (INRCA), Ancona, Metabolic Disease And Diabetology Unit, Ancona, Italy
Topic:
AS06 Glucose sensors
P. Baumann1, M. Cigler1, D. Kuznetsov2, M. Rosilio3, D. Hochfellner1, M. Ibberson4, S. Amiel5, S. Heller6, M.‐A. Gall7, B. De Galan8, T. Pieber1, P. Choudhary9,
1Medical University of Graz, Division Of Endocrinology & Diabetology, Graz, Austria, 2SIB Swiss Institute of Bioinformatics, Switzerland, Sib Swiss Institute Of Bioinformatics, Switzerland, Lausanne, Switzerland, 3Eli Lilly and Company Limited, UK, Eli Lilly And Company Limited, Uk, Windlesham, United Kingdom, 4SIB Swiss Institute of Bioinformatics, Sib Swiss Institute Of Bioinformatics, Lausanne, Switzerland, 5Kings College London, Diabetes Research Group, London, United Kingdom, 6University of Sheffield, University Of Sheffield, Sheffield, United Kingdom, 7Novo Nordisk A/S, Denmark, Novo Nordisk A/s, Denmark, Bagsværd, Denmark, 8Radboud, Radboud, Radboud, Netherlands, 9University Hospital of Leicester NHS Trust, Leicester Diabetes Centres, Leicester, United Kingdom
Topic:
AS06 Glucose sensors
1Medical University of Warsaw, 1st Department Of Obstetrics And Gynaecology, Warsaw, Poland, 2National Institute of Public Health NIH ‐ National Research Institute, Department Of Nutrition And The Nutritional Value Of Food, Warsaw, Poland
Topic:
AS06 Glucose sensors
1Indian Institute of Science Education and Research Pune, Department Of Biology, Pune, India, 2Savitribai Phule Pune University, Department Of Zoloogy, Pune, India, 3Savitribai Phule Pune University, Health Center, Pune, India, 4Savitribai Phule Pune University, Department Of Zoology, Pune, India
Topic:
AS06 Glucose sensors
1Centro Hospitalar e Universitário de Coimbra, Endocrinologia, Diabetes E Metabolismo, Coimbra, Portugal, 2Centro Hospitalar e Universitário de Coimbra, Patologia Clínica, Coimbra, Portugal
Topic:
AS06 Glucose sensors
1Dexcom, Health Economics And Outcomes Research, Global Access, SanDiego, United States of America, 2Dexcom, Marketing‐ Product Devlopment, SanDiego, United States of America
Topic:
AS06 Glucose sensors
Hospital Universitario Son Llàtzer, Endocrinology And Nutrition, Palma, Spain
Topic:
AS06 Glucose sensors
1T1D Exchange, Quality Improvement And Population Health, Boston, United States of America, 2University of Miami Miller School of Medicine, Endocrinology, Miami, United States of America, 3Department of Pediatrics, University of Florida, Endocrinology, Gainsville, United States of America, 4Upstate Medical University, Department Of Medicine, Syracuse, United States of America, 5NYU Langone, Endocrinology, NY, United States of America, 6Cincinnati Children's Hospitali, Uc Department Of Internal Medicine, OH, United States of America, 7Stanford Children's Health, Endocrinology, CA, United States of America, 8Mount Sinai, Pediatric Endocrinology And Diabetes, NY, United States of America
Topic:
AS06 Glucose sensors
1T1D Exchange, Quality Improvement And Population Health, Boston, United States of America, 22. University of Wisconsin School of Medicine and Public Health, Endocrinology, Madison, United States of America, 3Texas Children's Hospital, Endocrinology, Houston, United States of America, 4University of Miami, Endocrinology, Miami, United States of America, 5Icahn School of Medicine, Endocrinology, NY, United States of America, 6University of Colorado, Barbara Davis Center, Endocrinology, Aurora, United States of America
Topic:
AS06 Glucose sensors
S. Lundemose, A. Ranjan,
Steno Diabetes Center Copenhagen, Clinical Science, Diabetes Technology Research, Herlev, Denmark
Topic:
AS06 Glucose sensors
1T1D Exchange Boston, Quality Improvement And Population Helath, Boston, United States of America, 2Nationwide Children's Hospital Center for Clinical Excellence, Endocrinology, Columbus, United States of America, 3The University of Tennessee Health Science Center, Endocrinology, Memphis, United States of America, 4Albert Einstein College of Medicine, Endocrinology, Bronx, United States of America, 5Cincinnati Children Hospital Medical Center, Endocrinology, Columbus, United States of America, 6SUNY Upstate Joslin Diabetes Center, Endocrinology, Syracuse, United States of America
reduced by 6%. The trend was significant with a P value <0.05 for both groups.
Topic:
AS06 Glucose sensors
1Copenhagen University Hospital ‐ North Zealand, Department Of Endocrinology And Nephrology, Hillerød, Denmark, 2University of Copenhagen, Biostatistics, Department Of Public Health, Copenhagen, Denmark
Topic:
AS06 Glucose sensors
S. Vaughan1,
1Bigfoot Biomedical, Data Science, Milpitas, United States of America, 2Bigfoot Biomedical, Clinical And Medical Affairs, Milpitas, United States of America
Topic:
AS06 Glucose sensors
F. Benedetti1,2, P. Bertemes‐Filho1,
1State University of Santa Catarina, Department Of Electrical Engineering, Joinville, Brazil, 2Unifebe, Medicine, Brusque, Brazil
Topic:
AS06 Glucose sensors
1Department of Psychiatry, Radiology, Public Health, Nursing and Medicine, University Of Santiago De Compostela, Santiago de Compostela, Spain, 2Institute of Health Research, IDIS, Resmet Research Group, Santiago de Compostela, Spain, 3University of Santiago de Compostela, Faculty Of Nursing, Santiago de Compostela, Spain, 4Ciudad Real General University Hospital, Endocrinology And Nutrition, Ciudad Real, Spain, 5Institute of Health Research, IDIS, Sicrus Research Group, Santiago de Compostela, Spain, 6University of Santiago de Compostela, Clinursid Research Group, Santiago de Compostela, Spain, 7University Clinical Hospital of Santiago de Compostela, Pediatric Critical, Intermediate And Palliative Care Section., Santiago de Compostela, Spain, 8Carlos III Health Institute, Ricors, Rd21/0012/0025, Madrid, Spain, 9Santiago de Compostela Clinical Hospital, Pediatrics, Santiago de Compostela, Spain
Topic:
AS06 Glucose sensors
1Lifecare Laboratory GmbH, R&d, Mainz, Germany, 2DTMD University, Internal Medicine & Laboratory Medicine, Luxembourg, Luxembourg, 3Lifecare AS, Nanobiosensors, Bergen, Norway, 4Pfützner Science & Health Institute, Diabetes Center, Mainz, Germany
Topic:
AS06 Glucose sensors
1University and Azienda Ospedaliera Universitaria Integrata of Verona, Section Of Pediatric Diabetes And Metabolism, Department Of Surgery, Dentistry, Pediatrics, And Gynecology, University Of Verona, Verona, Italy., Verona, Italy, 2University and Azienda Ospedaliera Universitaria Integrata of Verona, Department Of Surgery, Dentistry, Pediatrics And Gynecology, Section Of Pediatric Diabetes And Metabolism, Verona, Italy, 3Federico II University of Naples, Regional Center Of Pediatric Diabetes, Department Of Traslational And Medical Sciences, Section Of Pediatrics, Federico Ii University, Naples, Italy, Napoli, Italy, 4University of the Study of Campania “Luigi Vanvitelli”, Regional Center For Pedatric Diabetes, Department Of Pediatrics, Naples, Italy, 5University Hospital of Bologna Sant'Orsola‐Malpighi, Paediatric Endocrine Unit, Bologna, Italy, 6Giovanni XXIII Children's Hospital, Metabolic Disorders And Genetic Diseases Unit, Bari, Italy
Topic:
AS06 Glucose sensors
M. Zin Oo1, D.S. Gardner2, H.C. Tan2,
1Singapore General Hospital, Medicine Academic Clinical Program, Singapore, Singapore, 2Singapore General Hospital, Endocrinology, Singapore, Singapore
Topic:
AS06 Glucose sensors
1Senseonics Inc., Product Development, Germantown, United States of America, 2Senseonics, Product Development, Germantown, United States of America, 3Senseonics, Inc., Engineering, GERMANTOWN, United States of America, 4Senseonics, Chemistry, Germantown, United States of America, 5Senseonics, Inc., Engineering, R&d, Germantown, United States of America, 6Senseonics Inc, Clinical Science, Germantown, United States of America, 7Senseonics Inc, Medical Affairs, Germantown, United States of America
Topic:
AS06 Glucose sensors
1Boston University School of Medicine, Endocrinology, Boston, United States of America, 2Biomedical Informatics Consultants, Llc, Potomac, United States of America
Topic:
AS06 Glucose sensors
1Wellcome‐MRC Institute of Metabolic Science, University Of Cambridge, Cambridge, United Kingdom, 2Wolfson Diabetes and Endocrine Clinic, Cambridge University Hospitals Nhs Foundation Trust, Cambridge, United Kingdom, 3University of Cambridge, Department Of Paediatrics, Cambridge, United Kingdom, 4Nottingham Children's Hospital, Department Of Paediatric Diabetes And Endocrinology, Nottingham, United Kingdom, 5Alder Hey Children's NHS Foundation Trust, Department Of Paediatrics, Liverpool, United Kingdom, 6University of Oxford, Department Of Paediatrics, Oxford, United Kingdom, 7Royal Hospital for Sick Children, Department Of Diabetes, Edinburgh, United Kingdom, 8Southampton Children's Hospital, Paediatric Diabetes, Southampton, United Kingdom, 9Leeds Children's Hospital, Department Of Paediatric Diabetes, Leeds, United Kingdom, 10Jaeb Center for Health Research, Jaeb Center For Health Research, Tampa, United States of America, 11Jaeb Center for Health Research, Biostatistics, Tampa, United States of America
Topic:
AS06 Glucose sensors
1Nordsjællands Hospital Hillerød, Department Of Endocrinology And Nephrology, Hillerød, Denmark, 2Radboud University Medical Center, Internal Medicine, Nijmegen, Netherlands
Topic:
AS06 Glucose sensors
Odense University Hospital, Svendborg Hospital, Medical Department M/fam, Department Of Endocrinology, Svendborg, Denmark
Topic:
AS06 Glucose sensors
1Inselspital Bern University Hospital and University of Bern, Department Of Diabetes, Endocrinology, Nutritional Medicine And Metabolism, Bern, Switzerland, 2University of Basel, Division Of Clinical Pharmacy And Epidemiology, Department Of Pharmaceutical Sciences, Basel, Switzerland, 3University of Padova, Department Of Information Engineering (dei), Padova, Italy
Topic:
AS06 Glucose sensors
1University Hospitals Leuven ‐ KU Leuven, Endocrinology, Leuven, Belgium, 2KU Leuven, Leuven Institute For Healthcare Policy (lihp), Leuven, Belgium, 3Vyoo Agency, Global Health Economics, San Diego, United States of America, 4Vyoo Agency, Global Health Economics, Lyon, France, 5University Hospital Antwerp ‐ University of Antwerp, Endocrinology‐diabetology‐metabolism, Antwerp, Belgium, 6Imeldaziekenhuis, Endocrinology, Bonheiden, Belgium, 7AZ Groeninge, Endocrinology, Kortrijk, Belgium, 8OLV Hospital Aalst, Endocrinology, Aalst, Belgium, 9Academic Hospital and Diabetes Research Centre, Vrije Universiteit Brussel, Brussels, Belgium, 10KU Leuven, Research Institute For Work And Society ‐ Hiva, Leuven, Belgium, 11Ghent University, Public Health And Primary Care, Interuniversity Centre For Health Economics Research (i‐cher), Ghent, Belgium
Topic:
AS06 Glucose sensors
1Abbott Diabetes Care, Clinical Affairs, Alameda, United States of America, 2University of Leeds, Leeds Institute Of Cardiovascular And Metabolic Medicine, Leeds, United Kingdom
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
1Nutrition Research Center, Shiraz University of Medical Sciences, Department Of Clinical Nutrition, Shiraz, Iran, 2School of Nutrition and Food Sciences, Shiraz University of Medical Sciences, Department Of Clinical Nutrition, Shiraz, Iran, 3Islamic Azad University of Kazeroun, Department Of Medical Engineering, Kazeroun, Iran, 4Endocrinology and Metabolism Research Center, Shiraz University of Medical Sciences, Department Of Internal Medicine, Shiraz, Iran, 5Nutrition Research Center, School of Nutrition and Food Sciences, Shiraz University of Medical Sciences, Department Of Food Hygiene And Quality Control, Shiraz, Iran
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
1Diabetes, Obesity & Thyroid Center, Diabetology, GWALIOR, India, 2Harinirmal Hospital, Internal Medicine, Morena, India, 3Diabetes, Obesity & Thyroid Centre, General Practice, Gwalior, India, 4BeatO, Health Arx Technologies, Operations, Delhi, India
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
M. Shomali1, C. Kelly2, A. Iyer1,
1Welldoc, Clinical And Data Analytics, Columbia, United States of America, 2Kelly Statistical Consulting, Statistics, San Diego, United States of America, 3Northwestern University, Feinberg School Of Medicine, Chicago, United States of America
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
1University of Tromsø – The Arctic University of Norway, Department Of Computer Science, Tromsø, Norway, 2University Hospital North Norway, Norwegian Centre For E‐health Research, Tromsø, Norway, 3Norwegian University of Science and Technology, Department Of Ict And Natural Sciences, Trondheim, Norway
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
dQ&A, Quantitative Research, San Francisco, United States of America
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
E. Cox, E. Lin, T. Bristow, R. Wood, C. Pang,
dQ&A, Data Analysis, San Francisco, United States of America
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
University of Padova, Department Of Information Engineering (dei), Padova, Italy
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
1Mexican Diabetes Association of Mexico City, A.C, (AMD), Cadena De Favores, CDMX, Mexico, 2Medtronic, Diabetes, Ciudad de México, Mexico
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
1Medical University of Lodz, Department Of Biostatistics And Translational Medicine, Lodz, Poland, 2Medical University of Lodz, Department Of Pediatrics, Diabetology, Endocrinology And Nephrology, Lodz, Poland
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
1University of Padova, Department Of Information Engineering (dei), Padova, Italy, 2Inselspital, Bern University Hospital, University of Bern, Department Of Diabetes, Endocrinology, Nutritional Medicine And Metabolism, Bern, Switzerland
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
E. Lin,
dQ&A, Data Analysis, San Francisco, United States of America
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
1Yale University, School Of Medicine, New Haven, United States of America, 2Yale School of Medicine, Department Of Pediatrics, New Haven, United States of America, 3Yale School of Medicine, Medical Informatics, New Haven, United States of America
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
1Endocrinology Research Centre of Health Care Ministry of Russian Federation, Department Of Diabetic Foot, Moscow, Russian Federation, 2Endocrinology Research Centre, Predicting And Innovating Diabetes, Moscow, Russian Federation, 3Vologda Regional Diabetes Center, Endocrinology, Vologda, Russian Federation, 4Medical Center “Mayakovsky”, Endocrinology, Kirov, Russian Federation, 5Endocrinology Research Centre, Director Of The Diabetes Institute, Moscow, Russian Federation
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
1Toronto Metropolitan University, Dietetics, Toronto, Canada, 2University of Toronto, Medicine, Toronto, Canada, 3RxFood Co, Engineering, Toronto, Canada, 4SickKids, Pediatric Endocrinology, Toronto, Canada
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
1Hospital Universitario de San Ignacio, Departamento De Endocrinologia, Bogota, Colombia, 2Clínica Integral de Diabetes (CLID), Departamento De Endocrinologia, Medellín, Colombia, 3Hospital Universitario Fundacion Valle del Lili, Departamento De Endocrinologia, Cali, Colombia, 4Sanofi, Medical Department, Bogota, Colombia, 5Sanofi US Services Inc., Real World Evidence Generation Team, Massachusetts, United States of America
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
1Harvard Medical School, Department Of Biomedical Informatics, Boston, United States of America, 2Massachusetts Institute of Technology, Health Sciences And Technology, Cambridge, United States of America
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
J. Litwin1,
1Children's Mercy Hospital, Endocrinology, KANSAS CITY, United States of America, 2Children's Mercy‐Kansas City, Endocrinology, Kansas City, United States of America, 3Nemour's Children's Health, Center For Healthcare Delivery Science, Jacksonville, United States of America, 4Children's Mercy Hospital, Health Services And Outcomes Research, KANSAS CITY, United States of America, 5Children's Mercy Hospital, Endocrinology And Diabetes, Kansas City, United States of America
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
1Children's Mercy Hospital, Endocrinology, KANSAS CITY, United States of America, 2Children's Mercy Hospital, Health Services And Outcomes Research, KANSAS CITY, United States of America, 3Children's Mercy‐Kansas City, Endocrinology, Kansas City, United States of America, 4Nemour's Children's Health, Center For Healthcare Delivery Science, Jacksonville, United States of America, 5Children's Mercy Hospital, Endocrinology And Diabetes, Kansas City, United States of America
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
1Roche Diabetes Care Spain SL, Medical Affairs, Sant Cugat del Vallès, Spain, 2mySugr GmbH, Medical Department, Vienna, Austria, 3Roche Diabetes Care UK, Medical Department, Burgess Hill, United Kingdom
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
University of Virginia, Center For Diabetes Technology, Charlottesville, United States of America
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
1Monash University, Monash Centre For Health Research And Implementation, Clayton, Australia, 2Monash University, Medicine, Clayton, Australia, 3Monash University, It, Clayton, Australia, 4Monash University, Physiology, Clayton, Australia
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
B. Spartz1,
1Children's Mercy Hospital, Endocrinology, Kansas City, United States of America, 2Children's Mercy Hospital, Endocrinology, KANSAS CITY, United States of America, 3Children's Mercy Hospital, Endocrinology And Diabetes, Kansas City, United States of America
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
1Hospital Universitario del Rio, Endocrinología, CUENCA, Ecuador, 2MEDDI Hub, Ingeniery, brno, Czech Republic, 3MEDDI Hub, Direction, Praga, Czech Republic
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
V. Schatz1, M. Eichenlaub2, T. Schrills3, H.‐J. Lüddeke4,
1Technische Hochschule Ulm, Department Of Mechatronics And Medical Engineering, Ulm, Germany, 2Institut für Diabetes‐Technologie Forschungs‐ und Entwicklungsgesellschaft mbH an der Universität Ulm, Scientific Operations, Ulm, Germany, 3University of Luebeck, Institute For Multimedia And Interactive Systems, Luebeck, Germany, 4Diabeteszentrum Bogenhausen, ‐, München, Germany
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
M. Eichenlaub1, G. Freckmann1, T. Schrills2, A. Thode Reenberg3, T. Ritschel3, J. Jørgensen3, H.‐J. Lüddeke4, S. Hohm5, I. Seidler5, J. Schmitzer5, H. Betz5, R. Blechschmidt5,
1Institut für Diabetes‐Technologie Forschungs‐ und Entwicklungsgesellschaft mbH an der Universität Ulm, Scientific Operations, Ulm, Germany, 2University of Luebeck, Institute For Multimedia And Interactive Systems, Luebeck, Germany, 3Technical University of Denmark, Department Of Applied Mathematics And Computer Science, Kgs. Lyngby, Denmark, 4Diabeteszentrum Bogenhausen, ‐, München, Germany, 5Technische Hochschule Ulm, Department Of Mechatronics And Medical Engineering, Ulm, Germany
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
G. Conceição1,
1Faculty of Medicine, University of Porto, Department Of Community Medicine, Information And Health Decision Sciences (medcids), Porto, Portugal, 2Institute of Engineering, Polytechnic of Porto, Research Group On Intelligent Engineering And Computing For Advanced Innovation And Development (gecad), Porto, Portugal, 3CLOO, Behavioral Insights Unit, Porto, Portugal
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
Air Liquide, R&d, Les Loges en Josas, France
Recent AI algorithms can automatically detect meal intake phases from CGM time series only.
We propose a hybrid AI algorithm for meal detection, it relies on a combination of CGM data and glucose kinetics. This study shows the benefits of such an algorithm with respect to common machine learning algorithms.
We benchmark the hybrid AI against common machine learning algorithms in a leave‐one patient‐out‐scheme.
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
D.S. Gardner1, H.C. Tan1, G.H. Lim2, M. Zin Oo3, X. Xin2, A. Kingsnorth4, P. Choudhary4,
1Singapore General Hospital, Endocrinology, Singapore, Singapore, 2Singapore General Hospital, Health Services Research Unit, Singapore, Singapore, 3Singapore General Hospital, Medicine Academic Clinical Program, Singapore, Singapore, 4University of Leicester, Diabetes Research Centre, Leicester, United Kingdom
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
Carbon Health, Virtual Diabetes Care, Oakland, United States of America
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
1British Hospital, Endocrinology, buenos Aires, Argentina, 2British Hospital, Nutrition, buenos Aires, Argentina
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
Prayas Diabetes Center, Diabetology, Indore City, India
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
1SIPPA Solutions, R&d, Queens, United States of America, 2University Graduate Center and Queens College/City U. of New York, Computer Science Department, New York, United States of America, 3Berufsgenossenschaftliche Unfallklinik, Fuß Und Sprunggelenkschirurgie, Murnau, Germany
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
Netherlands Organisation of Applied Scientific Research (TNO), Microbiology & Systems Biology, Leiden, Netherlands
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
Nestlé Research, Nestlé Institute Of Health Sciences, Lausanne, Switzerland
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
E. Lykoudi1,
1University of West Attica, Nursing, Egaleo, Greece, 2University of West Attica, Department Of Nursing, School Of Health Sciences, Athens, Greece, 3National and Kapodistrian University of Athens, Choremeio Research Laboratory, First Department Of Pediatrics, Athens, Greece, 4University of West Attica, Department Of Nursing, Athens, Greece
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
1National Taiwan University College of Medicine, Internal Medicine, Kaohsiung, Taiwan, 2Sanofi, Real‐world Evidence, Bridgewater, United States of America, 3University of Maryland, School Of Pharmacy, Baltimore, United States of America, 4Sanofi, Integrated Care, Shanghai, China, 5Sanofi, Diabetes, Hong Kong & Taiwan, Taipei, Taiwan, 6Chang Gung Memorial Hospital, Internal Medicine, Taoyuan City, Taiwan
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
F. Shihadeh, M. Rivera Davilla,
University of Texas Health Science Center at Houston, Pediatric Endocrinology, Houston, United States of America
Topic:
AS08 Insulin Pumps
Hospital of Lithuanian University of Health Sciences Kaunas Clinics, Endocrynology, Kaunas, Lithuania
Topic:
AS08 Insulin Pumps
1University Hospitals of Derby and Burton NHS Trust, Department Of Diabetes & Endocrinology, Derby, United Kingdom, 2University of Nottingham, School Of Medicine, Nottingham, United Kingdom, 3Tunbridge Wells Hospital, Department Of Diabetes, Tunbridge Wells, United Kingdom, 4University Hospital of Llandough, Department Of Diabetes & Endocrinology, Llandough, United Kingdom, 5Stepping Hill Hospital, Department Of Diabetes & Endocrinology, Stockport, United Kingdom, 6Oxford University Hospitals, Department Of Diabetes & Endocrinology, Oxford, United Kingdom, 7Manchester University Hospitals NHS Trust, Department Of Diabetes & Endocrinology, Manchester, United Kingdom, 8University Hospitals Birmingham NHS Trust, Department Of Diabetes, Birmingham, United Kingdom, 9King's College London, Diabetes, London, United Kingdom, 10Edinburgh Royal Infirmary, Department Of Diabetes & Endocrinology, Edinburgh, United Kingdom, 11Harrogate and District NHS trust, Department Of Diabetes, Harrogate, United Kingdom, 12University of Leicester, Diabetes Research Centre, Leicester, United Kingdom
Topic:
AS08 Insulin Pumps
Grupo LaTir México (Latin America Time In Range), Diabetes, Mexico, Mexico
Topic:
AS08 Insulin Pumps
1Aristotle University of Thessaloniki, Diabetes Center, 1st Propaedeutic Clinic Of Internal Medicine, Aristotle University Of Thessaloniki, General University Hospital Of Thessaloniki Ahepa, Greece, THESSALONIKI, Greece, 2Aristotle University of Thessaloniki, Department Of Nutrition & Dietetics, General University Hospital Of Thessaloniki Ahepa, Greece Diabetes Center, 1st Propaedeutic Clinic Of Internal Medicine, Aristotle University Of Thessaloniki, General University Hospital Of Thessaloniki Ahepa, Greece, THESSALONIKI, Greece
Topic:
AS08 Insulin Pumps
1Eli Lilly and Company, Lead‐pen‐based Solutions & Product Development, Indianapolis, United States of America, 2Eli Lilly and Company, Ddcs Design & Human Factors, Indianapolis, United States of America, 3Eli Lilly and Company, Connected Care Clinical Team, Indianapolis, United States of America, 4Eli Lilly and Company, Clinical Connected Care, Indianapolis, United States of America
Topic:
AS08 Insulin Pumps
C. Hopley1,
1Insulet International Ltd, Health Economics And Outcomes Research, London, United Kingdom, 2Birmingham City University, Faculty Of Health, Birmingham, United Kingdom, 3Insulet International Ltd, Health Economics, London, United Kingdom
Topic:
AS08 Insulin Pumps
C. Hopley1,
1Insulet International Ltd, Health Economics And Outcomes Research, London, United Kingdom, 2Birmingham City University, Faculty Of Health, Birmingham, United Kingdom, 3Insulet International Ltd, Medical Affairs, London, United Kingdom
Topic:
AS08 Insulin Pumps
Indian Institute of Science, Centre For Product Design And Manufacturing, Bengaluru, India
Topic:
AS08 Insulin Pumps
Indian Institute of Science, Centre For Product Design And Manufacturing, Bengaluru, India
Topic:
AS08 Insulin Pumps
1Wayne State University, Biomedical Engineering, Detroit, United States of America, 2BD Technologies, Parenteral Sciences Coe, Research Triangle Park, United States of America, 3University of Connecticut School of Medicine, Surgery, Farmington, United States of America, 4Cyclopure, R&d, Skokie, United States of America
Topic:
AS08 Insulin Pumps
1King Abdulaziz Medical City, Department Of Family Medicine, Jeddah, Saudi Arabia, 2King Abdulaziz Medical City, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Sciences, Department Of Medicine, Jeddah, Saudi Arabia, 3King Abdulaziz Medical City, Department Of Medicine, Jeddah, Saudi Arabia, 4King Saud bin Abdulaziz University for Health Sciences, College Of Medicine, Jeddah, Saudi Arabia, 5King Abdulaziz Medical City, King Abdullah International Medical Research Center, Department Of Medicine, Jeddah, Saudi Arabia
Topic:
AS08 Insulin Pumps
University and Azienda Ospedaliera Universitaria Integrata of Verona, Department Of Surgery, Dentistry, Pediatrics And Gynecology, Section Of Pediatric Diabetes And Metabolism, Verona, Italy
Topic:
AS08 Insulin Pumps
Dasman Diabetes institute, Medical Division, Kuwait, Kuwait
Topic:
AS08 Insulin Pumps
Institute for Clinical and Experimental Medicine, Diabetes Centre, Prague, Czech Republic
Topic:
AS08 Insulin Pumps
111 Lafayette Avenue, Quality Improvement And Population Health, Boston, United States of America, 2The University of Tennessee Health Science Center, Endocrinology, Memphis, United States of America, 3Northwestern University, Feinberg School Of Medicine, Chicago, United States of America, 4Albert Einstein College of Medicine, Endocrinology, Bronx, United States of America, 5Icahn School of Medicine at Mount Sinai, Endocrinology, New York, United States of America, 6Washington University in St. Louis, Endocrinology, St. Louis, United States of America, 7Stanford Children's Health, Endocrinology, CA, United States of America, 8Stanford University, Endocrinology, Stanford, United States of America, 9Children Hospital of Los Angeles, Endocrinology, Los Angeles, United States of America
Topic:
AS08 Insulin Pumps
General Hospital of Piraeus “Tzaneio”, 1st Department Of Internal Medicine And Diabetes Center, Piraeus, Greece
Topic:
AS08 Insulin Pumps
A. Munda1, C. Kovacic2,
1University Medical Centre Ljubljana, Department Of Endocrinology, Diabetes And Metabolic Diseases, Ljubljana, Slovenia, 2Faculty of Medicine, University Of Ljubljana, Ljubljana, Slovenia
Topic:
AS08 Insulin Pumps
K. Tian,
Singapore General Hospital, Endocrinology, Singapore, Singapore
Topic:
AS08 Insulin Pumps
Semmelweis University Faculty of Internal Medicine and Hematology, Dept. Of Endocrinology And Metabolism, Budapest, Hungary
Topic:
AS08 Insulin Pumps
V. Chatziravdeli1, G. Lambrou2, A. Samartzi3, N. Kotsalas4, E. Vlachou5, J. Komninos3,
1General Hospital “Ippokrateion”, Department Of Orthopedics, Thessaloniki, Greece, 2National and Kapodistrian University of Athens, Choremeio Research Laboratory, First Department Of Pediatrics, Athens, Greece, 3Naval Hospital of Athens, Diabetes Endocrinology And Metabolism, Athens, Greece, 4Naval Hospital of Athens, Nephrology, Athens, Greece, 5University of West Attica, Department Of Nursing, School Of Health Sciences, Athens, Greece
Topic:
AS08 Insulin Pumps
A. Ntikoudi1, A. Giannaraki1, E. Lykoudi1, N. Margari1, E. Evangelou1, A. Tsartsalis2,
1University of West Attica, Department Of Nursing, Athens, Greece, 2Naval Hospital of Athens, Diabetes Endocrinology And Metabolism, Athens, Greece, 3University of West Attica, Department Of Nursing, School Of Health Sciences, Athens, Greece
Topic:
AS08 Insulin Pumps
1University of West Attica, Department Of Nursing, School Of Health Sciences, Athens, Greece, 2University of West Attica, Department Of Nursing, Athens, Greece, 3Naval Hospital of Athens, Diabetes Endocrinology And Metabolism, Athens, Greece
Topic:
AS08 Insulin Pumps
A. Zeng,
dQ&A, Data Analysis, San Francisco, United States of America
Topic:
AS09 New Medications for Treatment of Diabetes
T. Lo1, Y. Lee2, C.‐Y. Tseng2, Y. Hu2, C. Mantzoros3,
1Brigham and Women's Hospital, Department Of Surgery, Boston, United States of America, 2Brigham and Women's Hospital, Harvard Medical School, Department Of Anesthesiology, Perioperative, And Pain Medicine, Boston, United States of America, 3Beth Israel Deaconess Medical Center, Endocrine, Boston, United States of America, 4AltrixBio Inc, N/a, Cambridge, United States of America
Topic:
AS09 New Medications for Treatment of Diabetes
1Eli Lilly & Company, Global Scientific Communications, Indianapolis, United States of America, 2Velocity Clinical Research, Westlake, Los Angeles, United States of America
Topic:
AS09 New Medications for Treatment of Diabetes
Q. Wang, A. Knights,
Eli Lilly and Company, Global Scientific Communications, Indianapolis, United States of America
Topic:
AS09 New Medications for Treatment of Diabetes
Hebrew Uiversity of Jerusalem, Ganz Chair Heart Studies, Jerusalem, Israel
Topic:
AS09 New Medications for Treatment of Diabetes
1GBUZ “City hospital, Moscow DZM”, Mosсow Healthcare Department, Moscow, Russian Federation, 2Endocrinological Dispensary of the Mosсow Healthcare Department, Mosсow Healthcare Department, Moscow, Russian Federation, 33. GBUZ “City polyclinic No. 12 DZM”, Mosсow Healthcare Department, Moscow, Russian Federation, 4City Polyclinic N
Topic:
AS09 New Medications for Treatment of Diabetes
1Pontificia Universidad Católica De Chile, Nutrición, Diabetes Y Metabolismo, Santiago, Chile, 2Pontificia Universidad Católica de Chile, Escuela De Química Y Farmacia, Santiago, Chile
Topic:
AS09 New Medications for Treatment of Diabetes
1Pontificia Universidad Católica De Chile, Nutrición, Diabetes Y Metabolismo, Santiago, Chile, 2Hospital Sótero del Río, Diabetes, Santiago, Chile
Topic:
AS09 New Medications for Treatment of Diabetes
1Institute for Clinical and Experimental Medicine, Diabetes Centre, Prague, Czech Republic, 2University of Toronto, Department Of Medicine, Toronto, Canada, 3Velocity Clinical Research, Westlake, Los Angeles, United States of America, 4King Saud University, Department Of Internal Medicine, Riyadh, Saudi Arabia, 5Sanofi, General Medicines Global Business Unit, Paris, France, 6Sanofi, R&d Dcv Clinical Development, Paris, France, 7IVIDATA Life Sciences, Clinical Sciences And Operations, Levallois‐Perret, France, 8Velocity Clinical Research at Medical City Dallas, Director, Dallas, United States of America
Topic:
AS09 New Medications for Treatment of Diabetes
1Velocity Clinical Research at Medical City Dallas, Director, Dallas, United States of America, 2University of Toronto, Department Of Medicine, Toronto, Canada, 3Velocity Clinical Research, Westlake, Los Angeles, United States of America, 4King Saud University, Department Of Internal Medicine, Riyadh, Saudi Arabia, 5Sanofi, General Medicines Global Business Unit, Paris, France, 6Sanofi, R&d Dcv Clinical Development, Paris, France, 7IVIDATA Life Sciences, Clinical Sciences And Operations, Levallois‐Perret, France, 8Institute for Clinical and Experimental Medicine, Diabetes Centre, Prague, Czech Republic
Topic:
AS09 New Medications for Treatment of Diabetes
N. Srinivasa Rao1, G.J. Naga Raju2,
1GITAM School of Science (GSS), Department Of Physics, Visakhapatnam, India, 2JNTU‐GV, Department Of Physics, Vizianagaram, India
Topic:
AS09 New Medications for Treatment of Diabetes
1Institute of Diabetes, School of Medicine, Southeast University, Department Of Endocrinology, Zhongda Hospital, Nanjing, China, 2Institute of Diabetes, School of Medicine, Southeast University, 1. department Of Clinical Nutrition, Zhongda Hospital, Nanjing, China, 3nstitute of Diabetes, School of Medicine, Southeast University, Department Of Endocrinology, Dongtai People's Hospital, Yancheng, China, 4The University of Adelaide, Adelaide Medical School, Adelaide, Australia
Topic:
AS10 New Insulin Delivery Systems: Inhaled, Transderma, Implanted Devices
1Gemeinschaftskrankenhaus Bonn, Endocrinology, Bonn, Germany, 2Novo Nordisk A/S, Advanced Analytics, Bagsværd, Denmark, 3Novo Nordisk A/S, Digital Health, Bagsværd, Denmark, 4School of Örebro University, Diabetes, Endocrinology And Metabolism, Örebro, Sweden
Topic:
AS11 Devices Focused on Diabetic Preventions
L. Teixeira, W. Castañeda, F. Cordova,
State University of Santa Catarina, Department Of Electrical Engineering, Joinville, Brazil
Topic:
AS11 Devices Focused on Diabetic Preventions
E. Rizos1, A. Kanellopoulou2,
1University Hospital of Ioannina, Department Of Internal Medicine, Ioannina, Greece, 2University of Ioannina School of Medicine, Department Of Hygiene And Epidemiology, Ioannina, Greece, 3Aristotle University of Thessaloniki, Pharmacy Department, Thessaloniki, Greece, 4Kapodistrian University of Athens, First Department Of Propaedeutic And Internal Medicine, Athens, Greece
Topic:
AS11 Devices Focused on Diabetic Preventions
Eunpyeong St. Mary's Hospital. The Catholic University of Korea, Seoul, Neurology, SEOUL, Korea, Republic of
WITHDRAWN
Topic:
AS11 Devices Focused on Diabetic Preventions
Nutrisense Inc., Data Science And Analytics, Chicago, United States of America
Topic:
AS11 Devices Focused on Diabetic Preventions
1University of Malta, Podiatry Department, Msida, Malta, 2University of Malta, Podiatry, Msida, Malta
Topic:
AS12 Advanced Medical Technologies to Be Used in Hospitals
Scientific Centre for Family Health and Human Reproduction Problems, Department Of Personalized And Preventive Medicine, Irkutsk, Russian Federation
Topic:
AS12 Advanced Medical Technologies to Be Used in Hospitals
Scientific Centre for Family Health and Human Reproduction Problems, Department Of Personalized And Preventive Medicine, Irkutsk, Russian Federation
Topic:
AS12 Advanced Medical Technologies to Be Used in Hospitals
1University of Debrecen, Department Of Internal Medicine, Icu And Aphesis Unit, Debrecen, Hungary, 2University of Debrecen, Departmen Of Operative Techniques And Surgical Research, Debrecen, Hungary, 3University of Debrecen, Department Of Food Technology, Faculty Of Agricultural And Food Sciences And Environmental Management, Debrecen, Hungary
Topic:
AS12 Advanced Medical Technologies to Be Used in Hospitals
1Profil Institut für Stoffwechselforschung GmbH, Profil, Neuss, Germany, 2FIDAM GmbH, Forschungsinstitut Diabetes‐akademie Bad Mergentheim, Bad Mergentheim, Germany
However, a large number of diabetologists (40.3%) can imagine that DDC for the care of special groups such as athletes or people from a different cultural background or with a lack of German language skills could certainly be advantageous as an additional service.
Most of the diabetologists disagreed that DDCs could be a competition for their own facilities.
However, only a few of the diabetologists (18.3%) believed that such virtual services substantially improve the care of PwD.
Topic:
AS12 Advanced Medical Technologies to Be Used in Hospitals
N. Willer1,
1Norfolk and Norwich NHS Foundation Trust, Diabetes And Antenatal Care, Norwich, United Kingdom, 2Norfolk and Norwich NHS Foundation Trust, Elsie Bertram Diabetes Centre, Norwich, United Kingdom
Topic:
AS12 Advanced Medical Technologies to Be Used in Hospitals
1University of Antwerp, Laboratory Of Experimental Medicine And Pediatrics And Member Of The Infla‐med Centre Of Excellence, Antwerp, Belgium, 2Antwerp University Hospital, Department Of Endocrinology, Diabetology And Metabolism, Edegem, Belgium, 3Antwerp University Hospital, Department Of Clinical Biology, Edegem, Belgium, 4Antwerp University Hospital, Department Of Gastroenterology And Hepatology, Edegem, Belgium
Topic:
AS12 Advanced Medical Technologies to Be Used in Hospitals
1Emory University School of Medicine, Endocrinology, Atlanta, United States of America, 2Emory University, Rollins School Of Public Health, Atlanta, United States of America, 3Ideal Medical Technologies, Ideal Medical Technologies, Asheville, United States of America
Topic:
AS13 New Technologies for Treating Obesity and Preventing Related Diabetes
L.‐T. Tsai1, P. Schwarz2, C. Brandt3, E. Kirchner4,
1University of Southern Denmark, Odense, Denmark, Research Unit For Orl – Head & Neck Surgery And Audiology, Odense, Denmark, 2Medical Faculty Carl Gustav Carus at the Technical University of Dresden, Department Of Medicine Iii, Dresden, Germany, 3Institute for Health Services, Research Department For General Practice, Odense, Denmark, 4Liva Healthcare, Department Of Research And Innovation, Berlin, Germany, 5Liva Healthcare, Department Of Research And Innovation, Copenhagen K, Denmark
Topic:
AS13 New Technologies for Treating Obesity and Preventing Related Diabetes
R. Šket1, T. Tesovnik1, B. Jenko Bizjan1, P. Kotnik2, T. Battelino3,
1University Medical Centre Ljubljana, Division Of Paediatrics, Clinical Institute Of Special Laboratory Diagnostics, Ljubljana, Slovenia, 2University Medical Centre Ljubljana, Division Of Paediatrics, Clinical Department Of Endocrinology, Diabetes And Metabolic Disorders, Ljubljana, Slovenia, 3UMC–University Children's Hospital, Faculty of Medicine, University of Ljubljana, Faculty Of Medicine, Ljubljana, Slovenia
Topic:
AS13 New Technologies for Treating Obesity and Preventing Related Diabetes
K.W. Ong,
National University Hospital, Dietetics, Singapore, Singapore
Topic:
AS13 New Technologies for Treating Obesity and Preventing Related Diabetes
W. Huaijie1, X. Lulu2, Y. Qingtao3, C. Lei1, T. Yuanyuan1,
1Weifang Second People's Hospital, Translational Medical Center, Weifang, China, 2Weifang Maternal and Child Health Hospital, Department Of Pharmacy, Weifang, China, 3Weifang People's Hospital, Department Of Pediatric Surgery, Weifang, China
Topic:
AS13 New Technologies for Treating Obesity and Preventing Related Diabetes
1University of Bern, Artorg Center For Biomedical Engineering Research, Bern, Switzerland, 2Bern University of Applied Sciences, Department Of Health Professions, Bern, Switzerland, 3University of Bern, Department Of Emergency Medicine, Inselspital, University Hospital, Bern, Switzerland
Topic:
AS14 Blood Glucose Monitoring and Glycemic Control in the Hospitals
1APDP Diabetes, Diabetology, Lisboa, Portugal, 2Hospital Fernando da Fonseca EPE, Internal Medicine, Sintra, Portugal
Topic:
AS14 Blood Glucose Monitoring and Glycemic Control in the Hospitals
1Antwerp University Hospital, Department Of Endocrinology, Diabetology And Metabolism, Edegem, Belgium, 2University of Antwerp, Faculty Of Medicine And Health Sciences, Antwerp, Belgium, 3University of Antwerp, Laboratory Of Experimental Medicine And Pediatrics And Member Of The Infla‐med Centre Of Excellence, Antwerp, Belgium, 4Antwerp University Hospital, Department Of Gastroenterology And Hepatology, Edegem, Belgium
Topic:
AS14 Blood Glucose Monitoring and Glycemic Control in the Hospitals
Campus Bio‐Medico of Rome, Endocrinology And Diabetology, Rome, Italy
Topic:
AS14 Blood Glucose Monitoring and Glycemic Control in the Hospitals
1University of Colorado Anschutz Medical Campus, Barbara Davis Center, Aurora, United States of America, 2Colorado School of Public Health, Biostatistics And Informatics, Aurora, United States of America
Topic:
AS14 Blood Glucose Monitoring and Glycemic Control in the Hospitals
1Hedia ApS, Data Science, Copenhagen NV, Denmark, 2Bispebjerg and Frederiksberg Hospital, The Parker Institute, Frederiksberg, Denmark, 3Hedia ApS, Clinical And Medical Affairs, Copenhagen NV, Denmark, 4University of Copenhagen, The Research Unit For General Practice And Section Of General Practice, Copenhagen K, Denmark
Data was analysed with GLMM for all users and for a subgroup of poorly regulated users (mean baseline BGL ≥10mmol/l, eA1c≥7.9% in week 0).
Topic:
AS14 Blood Glucose Monitoring and Glycemic Control in the Hospitals
1Jothydev's Diabetes Research Centre, Diabetes, Trivandrum, India, 2Diabetes Care & Hormone Clinic, Diabetes, Ahmedabad, India, 3Lilavati Hospital and Research Centre, Diabetes, Mumbai, India
Topic:
AS14 Blood Glucose Monitoring and Glycemic Control in the Hospitals
1Jeonbuk National University Medical School, Pediatrics, Jeonjusi, Korea, Republic of, 2Jeonbuk National University Hospital, Pediatrics, Jeonjusi, Korea, Republic of
Topic:
AS14 Blood Glucose Monitoring and Glycemic Control in the Hospitals
1Inselspital, Bern University Hospital, University of Bern, Department Of Diabetes, Endocrinology, Nutritional Medicine And Metabolism, Bern, Switzerland, 2Inselspital, Bern University Hospital, University of Bern, Department Of Anaesthesiology And Pain Medicine, Bern, Switzerland, 3University of Bern, Institute Of Social And Preventive Medicine (ispm), Bern, Switzerland
Topic:
AS14 Blood Glucose Monitoring and Glycemic Control in the Hospitals
1Queens Hospital ‐London, Diabetes And Endocrinology, London, United Kingdom, 2American University of the Caribbean School of Medicine‐St.Maarten, Internal Medicine, London, United Kingdom
Topic:
AS14 Blood Glucose Monitoring and Glycemic Control in the Hospitals
Queens Hospital ‐London, Diabetes And Endocrinology, London, United Kingdom
Topic:
AS14 Blood Glucose Monitoring and Glycemic Control in the Hospitals
1University of Pisa, Clinical And Experimental Medicine, Pisa, Italy, 2University Hospital of Pisa, Department Of Medicine, Pisa, Italy, 3University Hospital of Pisa, Maternal‐infant Department, Pisa, Italy
Topic:
AS14 Blood Glucose Monitoring and Glycemic Control in the Hospitals
Komatsu University, Faculty Of Health Sciences, Department Of Nursing, Komatsu city, Japan
Topic:
AS14 Blood Glucose Monitoring and Glycemic Control in the Hospitals
Gregorio Marañon Hospital, Endocrinology, Madrid, Spain
Topic:
AS14 Blood Glucose Monitoring and Glycemic Control in the Hospitals
C. Mckeag, G. Jones,
Gartnavel General Hospital, Department Of Diabetes, Glasgow, United Kingdom
Topic:
AS14 Blood Glucose Monitoring and Glycemic Control in the Hospitals
1Imperial College London, Department Of Metabolism, Digestion And Reproduction, London, United Kingdom, 2Imperial College Healthcare NHS Trust, Kidney And Transplant Services, London, United Kingdom
Topic:
AS15 Human factor in the use of diabetes technology
L. Jepson1,
1Dexcom, Inc., Medical Affairs, San Diego, United States of America, 2Dexcom, Inc., Data Science, Edinburgh, United Kingdom
Topic:
AS15 Human factor in the use of diabetes technology
L. Tindall,
Eli Lilly and Company, Department Of Insulins And Connected Care, Indianapolis, United States of America
Topic:
AS15 Human factor in the use of diabetes technology
Clear. BV, Personalized Nutrition, Amsterdam, Netherlands
Topic:
AS15 Human factor in the use of diabetes technology
1ASL2 Savonese, Internal Medicine, Savona, Italy, 2ASL 2 Savonese, Internal Medicine, Pietra Ligure, Italy
Topic:
AS15 Human factor in the use of diabetes technology
1Pediatric Endocrinology and Diabetes Unit and Social Services, “dana‐dwek” Children's Hospital, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel, 2Bar Ilan University, The Louis And Gabi Weisfeld School Of Social Work, Ramat Gan, Israel, 3Pediatric Endocrinology and Diabetes Unit and The Nutrition & Dietetics Unit, “dana‐dwek” Children's Hospital, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel, 4Pediatric Endocrinology and Diabetes Unit and Nursing Services, “dana‐dwek” Children's Hospital, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel, 5Pediatric Endocrinology and Diabetes Unit, “dana‐dwek” Children's Hospital, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel, 6Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
Topic:
AS15 Human factor in the use of diabetes technology
dQ&A, Data Analysis, San Francisco, United States of America
Topic:
AS15 Human factor in the use of diabetes technology
dQ&A, Data Analysis, San Francisco, United States of America
Topic:
AS15 Human factor in the use of diabetes technology
1University of Waikato, Te Huataki Waiora School Of Health, Hamilton, New Zealand, 2University of Otago, Department Of Paediatrics, Christchurch, New Zealand, 3OpenAPS.org, Openaps.org, Seattle, United States of America, 4Starship Children's Hospital, Department Of Paediatric Endocrinology, Auckland, New Zealand, 5University of Otago, Department Of Women's And Children's Health, Dunedin, New Zealand, 6Paediatric Department, Te Whatu Ora Southern, Dunedin, New Zealand, 7Te Whatu Ora Waikato, Waikato Regional Diabetes Service, Hamilton, New Zealand, 8University of Otago, Department Of Population Health, Christchurch, New Zealand, 9Nightscout New Zealand, Nightscout New Zealand, Hamilton, New Zealand
Topic:
AS15 Human factor in the use of diabetes technology
1FIDAM GmbH, Forschungsinstitut Diabetes‐akademie Bad Mergentheim, Bad Mergentheim, Germany, 2embecta, Medical Affairs Emea, München, Germany, 3embecta, Medical Affairs International, Eysins, Switzerland, 4embecta, Medical Affairs Emea, Heidelberg, Germany, 5Diabetes Center, Diabetes Clinic, Bad Mergentheim, Germany
Topic:
AS15 Human factor in the use of diabetes technology
L. Bejinariu, L. Lanoiu, A. Mihai, G. Radulian,
Carol Davila University of Medicine and Pharmacy, Diabetes, Nutrition And Metabolic Diseases, Bucharest, Romania
Topic:
AS15 Human factor in the use of diabetes technology
CHU de la Réunion, Department Of Endocrinology, Diabetology And Nutrition, ST DENIS, France
Topic:
AS15 Human factor in the use of diabetes technology
1Charité ‐ Universitätsmedizin Berlin, Sozialpädiatrisches Zentrum, Interdisziplinär, Pädiatrische Diabetologie Und Endokrinologie, Berlin, Germany, 2Ulm University, Institute For Epidemiology And Medical Biometry, Ulm, Germany, 3AUF DER BULT, Diabetes‐zentrum Für Kinder Und Jugendliche, Hannover, Germany, 4Sana Klinikum Lichtenberg, Sozialpädiatrisches Zentrum & Kinderklinik, Diabeteszentrum Für Kinder Und Jugendliche, Berlin, Germany, 5Katholisches Kinderkrankenhaus WILHELMSTIFT gGmbH, Päd. Endokrinologie Und Diabetologie, Hamburg, Germany, 6DRK Kliniken Berlin Westend, Diabeteszentrum Für Kinder Und Jugendliche, Berlin, Germany, 7Klinikum Hanau GmbH, Zentrum Für Kinderdiabetes, Hanau, Germany, 8Helmholtz Zentrum, Helmholtz Zentrum München, München, Germany
Topic:
AS15 Human factor in the use of diabetes technology
1children's national hospital, Endocrinology, Washington, United States of America, 2Children's National Hospital, Endocrinology, Washington, United States of America, 3Children's Hospital of Philadelphia, Endocrinology, philadelphia, United States Minor Outlying Islands, 4Children's Hospital of Philadelphia, Division Of Endocrinology And Diabetes, Philadelphia, United States of America
Topic:
AS15 Human factor in the use of diabetes technology
1University of Virginia, Center For Diabetes Technology, Charlottesville, United States of America, 2University of Virginia, Division Of Endocrinology, Center For Diabetes Technology, Charlottesville, United States of America
Topic:
AS15 Human factor in the use of diabetes technology
1University of Virginia, Center For Diabetes Technology, Charlottesville, United States of America, 2University of Virginia, Division Of Endocrinology, Center For Diabetes Technology, Charlottesville, United States of America
Topic:
AS15 Human factor in the use of diabetes technology
1Profil Institut für Stoffwechselforschung GmbH, Profil, Neuss, Germany, 2FIDAM GmbH, Forschungsinstitut Diabetes‐akademie Bad Mergentheim, Bad Mergentheim, Germany
Topic:
AS15 Human factor in the use of diabetes technology
Hospital Universitari Arnau de Vilaona, Endocrinologia Y Nutrición, Lleida, Spain
Topic:
AS15 Human factor in the use of diabetes technology
P. Edmiston1, S. Klippel2, J. Baran2,
1University of Colorado, Boulder, Anthropology, Boulder, United States of America, 2University of Washington, Diabetes Institute, Seattle, United States of America
Topic:
AS15 Human factor in the use of diabetes technology
1University of Applied Sciences, Department Of Health Management, Neu‐Ulm, Germany, 2Medicover MVZ, Specialist In Internal Medicine, Endocrinology And Diabetology, Ulm, Germany
Topic:
AS15 Human factor in the use of diabetes technology
1FIDAM GmbH, Forschungsinstitut Diabetes‐akademie Bad Mergentheim, Bad Mergentheim, Germany, 2FIDAM, Forschungsinstitut Diabetes‐akademie Bad Mergentheim, Bad Mergentheim, Germany, 3diabetesDE, Deutsche Diabetes‐hilfe, Berlin, Germany
Topic:
AS15 Human factor in the use of diabetes technology
1University of Michigan, Internal Medicine/endocrinology, Ann Arbor, United States of America, 2University of Michigan, Family Medicine, Ann Arbor, United States of America, 3University of Michigan, Department Of Health Behavior And Health Education, Ann Arbor, United States of America
Topic:
AS15 Human factor in the use of diabetes technology
1Perth Children's Hospital, Endocrinology And Diabetes, Nedlands, Australia, 2Telethon Kids Institute, Children's Diabetes Centre, Perth, Australia, 3Telethon Kids Institute, Children's Diabetes Centre, Nedlands, Australia, 4University of Otago, Department Of Paediatrics, Christchurch, New Zealand
(*on behalf of the ADDN study group).
Topic:
AS15 Human factor in the use of diabetes technology
1Kings College London, Department Of Diabetes, London, United Kingdom, 2University of Southern Denmark, Department Of Psychology, Odense, Denmark, 3Novo Nordisk, Medical & Science, Patient Focused Drug Developement, Soborg, Denmark, 4Novo Nordisk, Data Science, Department Of Pharmacometrics, Copenhagen, Denmark, 5Deakin University, School Of Psychology, Geelong, Australia, 6Diabetes Victoria, The Australian Centre For Behavioural Research In Diabetes, Melbourne Victoria, Australia, 7Steno Diabetes Center Odense, Steno Diabetes Center Odense (sdco), Odense, Denmark, 8University of Leicester, Diabetes Research Centre, Leicester, United Kingdom
Topic:
AS15 Human factor in the use of diabetes technology
B.H. Chew1, N.H. Mahamad Sobri1, I. Iman1, N. Hotung2, M. Benton2, I. Papachristou2, I.Z. Ismail1, F. Hassan1, S.M. Ching1, K. Goldsmith3, B.N. Mohd Yusof4, A. Baharom5, N.I. Basri6, M. Salim7, A. Forbes8, N. Guess9,
1Universiti Putra Malaysia, Family Medicine, UPM Serdang, Malaysia, 2King's College London, Psychological Medicine, London, United Kingdom, 3King's College London, Biostatistics & Health Informatics, London, United Kingdom, 4Universiti Putra Malaysia, Dietetics, Serdang, Malaysia, 5Universiti Putra Malaysia, Community Health, UPM Serdang, Malaysia, 6Universiti Putra Malaysia, Obstetrics And Gynaecology, UPM Serdang, Malaysia, 7Universiti Putra Malaysia, Rehabilitation Medicine, UPM Serdang, Malaysia, 8King's College London, Division Of Care In Long‐term Conditions, London, United Kingdom, 9University of Westminster, Research Centre For Optimal Health,, London, United Kingdom, 10University of East Anglia, Department Of Medicine, Norfolk, United Kingdom, 11Universiti Putra Malaysia, Software Engineering And Information System, Serdang, Malaysia
Topic:
AS15 Human factor in the use of diabetes technology
1SUNY Downstate Health Sciences University, College Of Medicine, Brooklyn, United States of America, 2Hassenfeld Children's Hospital at NYU Langone, Department Of Child & Adolescent Psychiatry, New York, United States of America, 3Hassenfeld Children's Hospital at NYU Langone, Pediatric Diabetes Center, New York, United States of America
Topic:
AS15 Human factor in the use of diabetes technology
1University of Messina, Department Of Human Pathology, Messina, Italy, 2Copenhagen University Hospital, Steno Diabetes Center Copenhagen, Copenhagen, Denmark, 3Institute of Medical Sciences, University of Opole, Department Of Pediatrics, Opole, Poland, 4Instituto Hispalense de Pediatria, Pediatrics Unit, Vithas Almeria, Almeria, Spain, 5University and Azienda Ospedaliera Universitaria Integrata of Verona, Section Of Pediatric Diabetes And Metabolism, Department Of Surgery, Dentistry, Pediatrics, And Gynecology, University Of Verona, Verona, Italy., Verona, Italy, 6University of Colorado School of Medicine, Barbara Davis Center For Diabetes, Aurora, United States of America, 7ISPAD, International Society For Pediatric And Adolescent Diabetes, Berlin, Germany
Topic:
AS15 Human factor in the use of diabetes technology
S. Lockwood‐Lee1,
1University Hospitals of Leicester NHS Trust, Diabetes/ Childrens Research, Leicester, United Kingdom, 2HEAL.med CIC, The Innovation Centre, LEICESTER, United Kingdom
Topic:
AS15 Human factor in the use of diabetes technology
1Birmingham Children's Hospital, Department Of Endocrinology And Diabetes, Birmingham, United Kingdom, 2Institute of Metabolism and Systems Research, College of Medical and Dental Sciences,, University Of Birmingham, Birmingham, United Kingdom, 3Institute of Cancer and Genomic Sciences, University Of Birmingham, Birmingham, United Kingdom
Topic:
AS15 Human factor in the use of diabetes technology
A. Munda1, Z. Mlinaric2, P.A. Jakin2,
1University Medical Centre Ljubljana, Department Of Endocrinology, Diabetes And Metabolic Diseases, Ljubljana, Slovenia, 2University of Ljubljana, Medical Faculty, Ljubljana, Slovenia, 3Faculty of Medicine, University Of Ljubljana, Ljubljana, Slovenia
Topic:
AS15 Human factor in the use of diabetes technology
1University of Gothenburg, Department Of Molecular And Clinical Medicine, Gothenburg, Sweden, 2Chalmers University of Technology, Department Of Mathematical Sciences, Gothenburg, Sweden, 3University of California, Department Of Medicine, Encinitas, United States of America, 4Uppsala University, Department Of Medical Sciences, Uppsala, Sweden, 5Örebro University, Department Of Internal Medicine, Faculty Of Medicine & Heatlh, Örebro, Sweden, 6Profil, ‐, Neuss, Germany, 7Karolinska Institute, Department Of Clinical Science And Education Södersjukhuset, Stockholm, Sweden, 8Medicine Diabetes Institute, ‐, Seattle, United States of America, 9Karolinska Institute, Department Of Medicine, Karolinska University Hospital Huddinge, Stockholm, Sweden
Topic:
AS15 Human factor in the use of diabetes technology
D.S. Gardner1, M. Zin Oo2, G.H. Lim3, X. Xin3, H.C. Tan1,
1Singapore General Hospital, Endocrinology, Singapore, Singapore, 2Singapore General Hospital, Medicine Academic Clinical Program, Singapore, Singapore, 3Singapore General Hospital, Health Services Research Unit, Singapore, Singapore
Topic:
AS15 Human factor in the use of diabetes technology
1Institute for Clinical and Experimental Medicine, Diabetes Centre, Prague, Czech Republic, 21st Faculty of Medicine, Charles University and General University Hospital in Prague, 4th Department Of Internal Medicine, Prague, Czech Republic
Topic:
AS15 Human factor in the use of diabetes technology
1Steno Diabetes Center Copenhagen, Clinical Research, Herlev, Denmark, 2Steno Diabetes Center South, Paediatric Department, Aabenraa, Denmark, 3Steno Diabetes Center Aarhus, Paediatric Department, Aarhus, Denmark, 4Steno Diabetes Center Sjaelland, Paediatric Department, Roskilde, Denmark, 5Steno Diabetes Center North, Paediatric Department, Aalborg, Denmark
Topic:
AS15 Human factor in the use of diabetes technology
F. Çetin1,
1Turkish Diabetes Association NB Kadikoy Hospital, Internal Medicine, İSTANBUL, Turkey, 2Turkish Diabetes Association NB Kadikoy Hospital, Clinical Nutrition, İSTANBUL, Turkey, 3Kayseri Erciyes University, Computer Engineering, Kayseri, Turkey, 4Kayseri Erciyes University, Microbiology And Clinical Microbiology, Kayseri, Turkey
Topic:
AS15 Human factor in the use of diabetes technology
1Aalborg Universitetshospital, Steno Diabetes Center Nordjylland, Aalborg, Denmark, 2Regionshospital Nordjylland, Afdeling For Hjerte, Diabetes Og Hormonsygdomme, Hjoerring, Denmark, 3Center for Clinical Research, North Denmark Regional Hospital, Hjoerring, Denmark, 4Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
Topic:
AS15 Human factor in the use of diabetes technology
1The Ohio State University, Endocrinology, Diabetes And Metabolism, Columbus, United States of America, 2The Ohio State University, Endocrinology, Columbus, United States of America
Topic:
AS15 Human factor in the use of diabetes technology
dQ&A, Quantitative Research, San Francisco, United States of America
Topic:
AS15 Human factor in the use of diabetes technology
A. Zeng,
dQ&A, Data Analysis, San Francisco, United States of America
Topic:
AS15 Human factor in the use of diabetes technology
S. Mahmoud, A. Shah,
University of Texas Health Science Center at Houston, Pediatric Endocrinology, Houston, United States of America
Topic:
AS15 Human factor in the use of diabetes technology
1Kings College London, Department Of Diabetes, London, United Kingdom, 2Novo Nordisk, Data Science, Department Of Pharmacometrics, Copenhagen, Denmark, 3University of Southern Denmark, Department Of Psychology, Odense, Denmark, 4Novo Nordisk, Medical & Science, Patient Focused Drug Developement, Soborg, Denmark, 5Steno Diabetes Center Odense, Steno Diabetes Center Odense (sdco), Odense, Denmark, 6Deakin University, School Of Psychology, Geelong, Australia, 7Diabetes Victoria, The Australian Centre For Behavioural Research In Diabetes, Melbourne Victoria, Australia, 8University of Leicester, Diabetes Research Centre, Leicester, United Kingdom
Topic:
AS16 Trials in progress
Oviva, Diabetes Remission, Runway East, United Kingdom
Topic:
AS16 Trials in progress
1Nemour's Children's Health, Center For Healthcare Delivery Science, Jacksonville, United States of America, 2Nemours Children's Health‐Orlando, Center For Healthcare Delivery Science‐ Florida, Orlando, United States of America, 3Children's Mercy Hospital, Endocrinology And Diabetes, Kansas City, United States of America
Topic:
AS16 Trials in progress
1UiT The Arctic University of Norway, Computer Science, Tromsø, Norway, 2Oslo University Hospital, Clinical Medicine, Oslo, Norway, 3University of Tromsø – The Arctic University of Norway, Department Of Computer Science, Tromsø, Norway
Topic:
AS16 Trials in progress
1Medical University of Gdansk, Department Of Medical Immunology, Gdańsk, Poland, 2Medical University of Gdansk, Department Of Laboratory Medicine, Gdańsk, Poland
Topic:
AS16 Trials in progress
1“Aghia Sofia” Children's Hospital, Diabetes Center, Division Of Endocrinology, Diabetes And Metabolism, First Department Of Pediatrics, Medical School, National And Kapodistrian University Athens, Athens, Greece, 2National Technical University of Athens, School Of Electrical And Computer Engineering, Athens, Greece, 3UBITECH, Research And Development Department, Athens, Greece, 4Inspiring Earth, Pegneon, Athens, Greece
Topic:
AS17 COVID‐19 and Diabetes
1Kuwait University, Faculty Of Medicine, Kuwait City, Kuwait, 2Dasman Diabetes Institute, Population Health, Kuwait City, Kuwait, 3Farwaniya Hospital, Pediatrics, Kuwait City, Kuwait
Topic:
AS17 COVID‐19 and Diabetes
Evangelismos General Hospital, Endocrinology And Diabetology Department, athens, Greece
Topic:
AS17 COVID‐19 and Diabetes
Karaganda Medical University, Internal Medicine, Karaganda, Kazakhstan
Topic:
AS17 COVID‐19 and Diabetes
1Glooko, Inc., Clinical Research & Evidence Generation, Palo Alto, United States of America, 2Glooko, Inc., Chief Medical Officer, Palo Alto, United States of America, 3Glooko, Inc., Chief Operating Officer, Palo Alto, United States of America
Topic:
AS17 COVID‐19 and Diabetes
Vanderbilt University Medical Center, Pediatrics, Nashville, United States of America
Topic:
AS17 COVID‐19 and Diabetes
1GOVERNMENT MEDICAL COLLEGE OMANDURAR, General Medicine, Chennai, India, 2GOVERNMENT MEDICAL COLLEGE OMANDURAR, General Medicine, Chennai, India
Topic:
AS17 COVID‐19 and Diabetes
GOVERNMENT MEDICAL COLLEGE OMANDURAR, General Medicine, Chennai, India
Topic:
AS17 COVID‐19 and Diabetes
1IDF, Young Leaders In Diabetes, SÃO PAULO, Brazil, 2ADJ Diabetes Brasil, Educando Educadores, São Paulo, Brazil
Topic:
AS17 COVID‐19 and Diabetes
King Saud University Medical City, University Diabetes Center, Riyadh, Saudi Arabia
Topic:
AS18 Other
1Hospital Universitario La Paz, Unidad De Diabetes, Madrid, Spain, 2Hospital Universitario La Paz, Endocrinología Y Nutrición, Unidad De Diabetes, Madrid, Spain
Topic:
AS18 Other
University of Liverpool, School Of Medicine, BX, United Kingdom
Topic:
AS18 Other
1Dasman Diabetes Institute, Department Of Population Health, Kuwait city, Kuwait, 2Kuwait University, Department Of Pediatrics, Faculty Of Medicine, Kuwait, City, Kuwait, 3Farwaniya Hospital, Department Of Pediatrics, Kuwait city, Kuwait, 4Dasman Diabetes Institute, Population Health, Kuwait City, Kuwait
Topic:
AS18 Other
A. Al Hayek,
prince sultan military medical city, Endocrinology, Riyadh, Saudi Arabia
Topic:
AS18 Other
D. Khalifa1, T. Alqaisi2, F. Othman1, F. Al‐Juailla1,
1Dasman Diabetes Institute, Population Health, Kuwait City, Kuwait, 2New York University, School Of Global Public Health, New York, United States of America, 3Kuwait University, Faculty Of Medicine, Kuwait City, Kuwait, 4Farwaniya Hospital, Pediatrics, Kuwait City, Kuwait
Topic:
AS18 Other
Aitkhozhin Institute of Molecular Biology and Biochemistry, Structural And Functional Genomics Laboratory, Almaty, Kazakhstan
Topic:
AS18 Other
1Botkin Hospital, ., Mosсow, Russian Federation, 2Russian Medical Academy of Continuous Professional Education., Mosсow, Russian Federation
Topic:
AS18 Other
Mustapha Hospital, Diabetes Department, Algiers, Algeria
Topic:
AS18 Other
1Fonna Health Trust, Haugesund Hospital, Pediatric And Adolescent Medicine, Haugesund, Norway, 2Fonna Health Trust, Department Of Research And Innovation, Haugesund, Norway, 3Oslo University Hospital, Division Of Childhood And Adolescent Medicine, Oslo, Norway, 4Haukeland University Hospital, Child And Youth Clinic, Bergen, Norway, 5University of Oslo, Institute of Clinical Medicine, Faculty Of Medicine, Oslo, Norway
Topic:
AS18 Other
1Medtronic Bakken Research Center, Medtronic Diabetes Emea, Maastricht, Netherlands, 2Medtronic International Trading Sàrl, Medtronic Diabetes Emea, Tolochenaz, Switzerland
Topic:
AS18 Other
1Illinois Institute of Technology, Chemical And Biological Engineering, Chicago, United States of America, 2Illinois Institute of Technology, Biomedical Engineering, Chicago, United States of America, 3University of Illinois at Chicago, College Of Nursing, Chicago, United States of America
Topic:
AS18 Other
Scientific Centre for Family Health and Human Reproduction Problems, Department Of Personalized And Preventive Medicine, Irkutsk, Russian Federation
Topic:
AS18 Other
Ulyanovsk State University, Therapy And Occupational Diseases Department, Ulyanovsk, Russian Federation
Topic:
AS18 Other
Ulyanovsk State University, Therapy And Occupational Diseases Department, Ulyanovsk, Russian Federation
Topic:
AS18 Other
Ulyanovsk State University, Therapy And Occupational Diseases Department, Ulyanovsk, Russian Federation
Topic:
AS18 Other
1University of Campinas, Internal Medicine Post Graduation, Campinas ‐São Paulo, Brazil, 2University of Campinas, Endocrinology Division, Department Of Internal Medicine, Faculty Of Medical Sciences, Campinas ‐São Paulo, Brazil, 3Regional Statistical Council, 3th Region, São Paulo, Brazil, 4Private practice, Endocrinology And Diabetes, Itajaí, Brazil
Topic:
AS18 Other
1Nightingale Hospital, Endocrinology, KOLKATA, India, 2AMRI Hospitals, Endocrinology, Kolkata, India
Topic:
AS18 Other
1Institute for Clinical and Experimental Medicine, Department Of Diabetes, Prague, Czech Republic, 2Institute for Clinical and Experimental Medicine, Transplant Surgery, Prague, Czech Republic, 3Faculty Hospital Brno, Department Of Surgery, Brno, Czech Republic
Topic:
AS18 Other
1University of Virginia, Center For Diabetes Technology, Charlottesville, United States of America, 2University of Virginia, Division Of Endocrinology, Center For Diabetes Technology, Charlottesville, United States of America
Topic:
AS18 Other
University of Dohuk, College of Pharmacy, Medicinal Chemistry, Dohuk, Iraq
Topic:
AS18 Other
1Roche Diabetes Care, Algorithms And Advanced Analytics, Sant Cugat del Vallès, Spain, 2Roche Diabetes Care, Algorithms And Advanced Analytics, Burgess Hill, United Kingdom, 3Roche Diabetes Care, Algorithms And Advanced Analytics, Indianapolis, United States of America
Topic:
AS18 Other
1Institute of neurobiology, Beahvioural Neurobiology, Sofia, Bulgaria, 2Medical University Sofia, Faculty Of Medicine, Sofia, Bulgaria, 3Medical University of Sofia, Pharmacology And Toxicology, Sofia, Bulgaria
Topic:
AS18 Other
1Koç University, School Of Medicine, istanbul, Turkey, 2Koç University, Public Health, Istanbul, Turkey, 3Diyarbakır Child Diseases Hospital, Pediatric Endocrinology And Diabetes, Diyarbakır, Turkey, 4University of Health Sciences Gazi Yasargil Training and Research Hospital, Pediatric Endocrinology And Diabetes, Diyarbakır, Turkey, 5Dr. Sami Ulus Obstetrics and Gynecology and Pediatrics Training and Research Hospital, Pediatric Endocrinology And Diabetes, Ankara, Turkey, 6Çukurova University Balcalı Hospital, Pediatric Endocrinology And Diabetes, Adana, Turkey, 7Necmettin Erbakan University, Pediatric Endocrinology And Diabetes, Konya, Turkey, 8Marmara University, Pediatric Endocrinology And Diabetes, Diyarbakır, Turkey, 9Ondokuz Mayıs University, Pediatric Endocrinology And Diabetes, Samsun, Turkey, 10University of health sciences,Umraniye Training and Research Hospital, Pediatric Endocrinology And Diabetes, İstanbul, Turkey, 11Koç University, Department Of Pediatric Endocrinology And Diabetes, istanbul, Turkey
Topic:
AS18 Other
1meala GmbH, Communications, Berlin, Germany, 2HTW Berlin, meala GmbH, Wirtschafts‐ Und Rechtswissenschaften, Berlin, Germany
Topic:
AS18 Other
1Ciudad Real General University Hospital, Endocrinology And Nutrition, Ciudad Real, Spain, 2Guadalajara University Hospital, Endocrinology And Nutrition, Guadalajara, Spain, 3Albacete University Hospital, Endocrinology And Nutrition, Albacete, Spain, 4Mancha Centro Hospital, Endocrinology And Nutrition, Alcazar de San Juan, Spain, 5Virgen del Prado Hospital, Endocrinology And Nutrition, Talavera de la Reina, Spain, 6Villarobledo General Hospital, Endocrinology And Nutrition, Villarobledo, Spain, 7Hellin General Hospital, Endocrinology And Nutrition, Hellin, Spain, 8Almansa General Hospital, Endocrinology And Nutrition, Almansa, Spain, 9Virgen de la Luz Hospital, Endocrinology And Nutrition, Cuenca, Spain, 10Santa Barbara Hospital, Endocrinology And Nutrition, Puertollano, Spain, 11Valdepeñas General Hospital, Endocrinology And Nutrition, Valdepeñas, Spain, 12Toledo University Hospital, Endocrinology And Nutrition, Toledo, Spain
Topic:
AS18 Other
1Schneider Children's Medical Center of Israel, The Jesse Z. And Sara Lea Shafer Institute For Endocrinology And Diabetes, National Center For Childhood Diabetes, Petah Tikva, Israel, 2Sapir Medical Center, Department Of Pediatrics, Kfar Saba, Israel
Topic:
AS18 Other
A. Giandalia1, G. Russo1, P. Ruggeri2, A. Giancaterini3, E. Brun4, M.R. Cristofaro5, A. Bogazzi6, M.C. Rossi7, G. Lucisano7, A. Rocca8, V. Manicardi9, P. Di Bartolo10, G. Di Cianni11, C. Giuliani12,
1University of Messina, Messina, Department Of Clinical And Experimental Medicine,, Messina, Italy, 2ASST Cremona, Italy, Uod Diabetes Center, Cremona, Italy, 3ASST Brianza, Uosd Malattie Endocrine, Del Ricambio E Della Nutrizione,, Desio Monza, Italy, 4Malattie Endocrine, del Ricambio e della Nutrizione, Ospedale Civile Di Vicenza, VICENZA, Italy, 5Cardarelli Hospital, Campobasso, S. C. Malattie Endocrine‐diabetologia,, CAMPOBASSO, Italy, 6ASL TO 3, Ssvd Malattie Endocrine E Diabetologia,, TORINO, Italy, 7CORESEARCH, Pescara, Center For Outcomes Research And Clinical Epidemiology, PESCARA, Italy, 8Ospedale Bassini Cinisello Balsamo, AST Nord Milano, Ss Diabetologia E Malattie Metaboliche, MILANO, Italy, 9Diabetes clinic, Azienda Usl‐irccs Di Reggio Emilia, Reggio Emilia, reggio emilia, Italy, 10AUSL Diabetes Unit Romagna, Ravenna, Ausl Diabetes Unit Romagna, RAVENNA, Italy, 11Health Local Unit North‐West Tuscany, Livorno, Diabetes And Metabolic Diseases Unit,, LIVORNO, Italy, 12Sapienza University of Rome, Italy, Department Of Experimental Medicine, ROME, Italy, 13International University of health Sciences “Unicamillus”, Rome, Italy, International University Of Health Sciences “unicamillus”, ROME, Italy
Topic:
AS18 Other
1University Hospital Limerick, Blood Sciences Laboratory, F, Ireland, 2University Hospital Limerick, Paediatrics Department, F, Ireland
Topic:
AS18 Other
1Children´s Clinic of Tartu University Hospital, Department Of General Paediatrics, Tartu, Estonia, 2University of Tartu, Department Of Pediatrics, Tartu, Estonia, 3University of Tartu, Institute Of Chemistry, Tartu, Estonia, 4LDI Innovation Ltd, N/a, Peetri, Estonia
Topic:
AS18 Other
Hawler Medical University/ College of Nursing, Nursing, Hawler, Iraq
Topic:
AS18 Other
Rinker Nutritional Consulting, Medical, Waynesville, United States of America
Topic:
AS18 Other
1Hannover Medical School, Medical Sociology Unit, Hannover, Germany, 2St. Vincenz Hospital, Accident And Emergency Department, Paderborn, Germany, 3Hannover Medical School, Medical Psychology Unit, Hannover, Germany
Topic:
AS18 Other
1Private practice, Endocrinology And Diabetes, Itajaí, Brazil, 2University of Campinas, Internal Medicine Post Graduation, Campinas ‐São Paulo, Brazil, 3University of Campinas, Endocrinology Division, Department Of Internal Medicine, Faculty Of Medical Sciences, Campinas ‐São Paulo, Brazil
Topic:
AS18 Other
D. Gašparini1,2, I. Kavazović2, I. Klarić3, A. Prunk Drmić4, V. Peršić3,5, F. Wensveen2,
1Special Hospital Thalassotherapia Opatija, Center For Diabetes, Endocrinology And Cardiometabolism, Opatija, Croatia, 2University of Rijeka, Faculty of Medicine, Department Of Histology And Embryology, Rijeka, Croatia, 3Special Hospital Thalassotherapia Opatija, Cardiology Department, Opatija, Croatia, 4Special Hospital Thalassotherapia Opatija, Neurology Department, Opatija, Croatia, 5University of Rijeka, Faculty of Medicine, Department Of Rehabilitation And Sports Medicine, Rijeka, Croatia, 6University of Rijeka, Faculty of Medicine, Department Of Internal Medicine, Rijeka, Croatia, 7Clinical Hospital Center Rijeka, Department Of Endocrinology, Diabetology And Metabolic Disorders, Rijeka, Croatia
Topic:
AS18 Other
1ETH Zurich, Department Of Management, Technology, And Economics, Zurich, Switzerland, 2University of Bern, Institute Of Social And Preventive Medicine, Bern, Switzerland, 3Kantonsspital Olten, Department Of Endocrinology And Metabolic Diseases, Olten, Switzerland, 4Bern University Hospital, Department Of Diabetes, Endocrinology, Nutritional Medicine And Metabolism, Bern, Switzerland, 5Team Novo Nordisk Professional Cycling Team, ‐, Atlanta, United States of America, 6University of Rijeka, Faculty Of Medicine, Rijeka, Croatia, 7University of Verona, Department Of Neuroscience, Biomedicine And Movement Sciences, Verona, Italy, 8Friedrich‐Alexander University Erlangen‐Nürnberg, School Of Business, Economics And Society, Nuremberg, Germany, 9LMU Munich, Institute Of Ai In Management, Munich, Germany, 10University of St. Gallen, Institute Of Technology Management, St Gallen, Switzerland
ATTD 2023 Late Breaking Abstracts
Topic:
AS01 Big data and artificial intelligence‐based decision support systems
M. Karaglani1,2, M. Panagopoulou1,2, C. Chemonidou2,3, E. Apalaki1,3, I. Tsamardinos4, T. Theodosiou1, N. Papanas5, D. Papazoglou5, T.C. Constantinidis6, S. Gerou7,
1Democritus University of Thrace, Laboratory Of Pharmacology, Department Of Medicine, Alexandroupolis, Greece, 2Hellenic Mediterranean University Research Centre, Institute Of Agri‐food And Life Sciences, Heraklion, Greece, 3FORTH, Institute Of Molecular Biology And Biotechnology, Crete, Greece, 4Science and Technology Park of Crete, Jadbio, Crete, Greece, 5University Hospital of Alexandroupolis, Diabetes Centre, 2nd Department Of Internal Medicine, Alexandroupolis, Greece, 6Democritus University of Thrace, Lab. Hygiene And Environmental Protection, Alexandroupolis, Greece, 7Analysi Medical, Thessaloniki, Greece
This research was co‐financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (T1EDK‐00940).
Topic:
AS01 Big data and artificial intelligence‐based decision support systems
1Charles University in Prague, 1st Faculty of Medicine, 3rd Dept. Of Internal Medicine, Prague, Czech Republic, 2Military University Hospital Prague, Department Of Ophthalmology, 1st Faculty Of Medicine Of Charles University And Military University Hospital, Prague, Czech Republic, 3Aireen, , Prague, Czech Republic, 4Targa Team, , Prague, Czech Republic, 5DSC Services, , Tišnov, Czech Republic
Topic:
AS01 Big data and artificial intelligence‐based decision support systems
M. Christou1, D. Katsarou2, E. Georga2,3, A. Siolos1, P. Christou1, C. Zisidis1, C. Papaloukas4,
1Department of Endocrinology, University Hospital Of Ioannina, Ioannina, Greece, 2Unit of Medical Technology and Intelligent Information Systems, Department Of Materials Science And Engineering, University Of Ioannina, Ioannina, Greece, 3Biomedical Research Institute, FORTH, University Campus Of Ioannina, Ioannina, Greece, 4Department of Biological Applications and Technology, University Of Ioannina, Ioannina, Greece
Topic:
AS02 Clinical Decision Support Systems/Advisors
1Dario Health, Clinical, Caesarea, Israel, 2Dario Health, Data, Caesarea, Israel, 3Dario Health, Chief Medical Officer, New York, United States of America
Topic:
AS02 Clinical Decision Support Systems/Advisors
1University of Malta, Faculty Of Health Sciences, Msida, Malta, 2University of Malta, Centre For Biomedical Cybernetics, Msida, Malta
Topic:
AS03 Closed‐loop System and Algorithm
1Instituto médico Río Cuarto, Endocrinología, Rio cuarto, Argentina, 2Hospital Privado de Cordoba, Diabetes, Cordoba, Argentina, 3Clínica Universitaria Reina Fabiola, Diabetes, Cordoba, Argentina, 4Sanatorio Santa Fe, Diabetes, Santa Fe, Argentina, 5Hospital de Niños de Cordoba, Diabetes, Cordoba, Argentina, 6centro de diagnóstico cardio vascular, Diabetes, santiago del estero, Argentina, 7hospital allende, Diabetes, cordoba, Argentina, 8gsbio, Diabetes, cordoba, Argentina, 9Cadjujuy consultorio, Diabetes, Jujuy, Argentina, 10Sanatorio de Niños y Consultorio Integral Diabetes, Diabetes, Rosario, Argentina, 11Hospital Público Materno Infantil de Salta, Diabetes, Salta, Argentina, 12Instituto de Clínica Médica y Diabetes, Diabetes, mendoza, Argentina, 13Clinica San Martin, Diabetes, VILLA MARIA, Argentina
Topic:
AS03 Closed‐loop System and Algorithm
1IRCCS San Raffaele Hospital, Department Of Pediatrics, Milan, Italy, 2Hospital San Raffaele, Diabetes Research Insitute, Milano, Italy, 3Università Vita‐Salute San Raffaele, School Of Medicine, Milan, Italy
Topic:
AS03 Closed‐loop System and Algorithm
1Sud‐Francilien Hospital, Endocrinology‐diabetology, Corbeil‐Essonnes, France, 2CHU Caen, Diabetology, Caen, France, 3CHU Toulouse, Diabetology, Toulouse, France, 4CHU de Grenoble, Endocrinologie, La Tronche, France, 5Robert Debré Hospital, Endocrinology, Diabetes And Nutrition, Reims, France, 6Hopital Ste Margurite, Diabetology, Marseille, France, 7Strasbourg Universitary Hospital, Endocrinology, Diabetes And Nutrition, Strasbourg, France, 8Centre d'Etudes et de Recherches pour l'Intensification du Traitement du Diabète, Diabétologie, Évry‐Courcouronnes, France, 9Toulouse Universitary Hospital, Pediatric Endocrinology, Diabetes, Toulouse, France, 10APHP ‐ Necker Hospital, Endocrinology, Gynecology And Pediatrics Diabetes, Paris, France
Topic:
AS06 Glucose sensors
1AMCR Institute, Diabetes Technology, Escondido, United States of America, 2MDRequest, Office Of The Cmo, Shavano Park, United States of America, 3MKAnders Consulting, Office Of The Cmo, Redwood City, United States of America, 4Lifeplus, Inc., Research & Development, San Jose, United States of America, 5Second Medical Faculty, Charles University, Department Of Internal Medicine, Prague, Czech Republic
Topic:
AS06 Glucose sensors
1Rainier Clinical Research Center, Time Square Office Park, Renton, United States of America, 2Sinocare, Cgm Business Unit, Changsha, China
Topic:
AS06 Glucose sensors
Università di Pisa, Dietologia Universitaria, Pisa, Italy
Topic:
AS06 Glucose sensors
1University of Florence, Experimental And Clinical Biomedical Sciences “mario Serio” Department, Firenze, Italy, 2Careggi Hospital, Diabetes Unit, Firenze, Italy
Topic:
AS06 Glucose sensors
O. Hauss1,
1Dr. Hauss Training & Consulting, Owner, Maxdorf, Germany, 2Roche Diabetes Care GmbH, Medical Science, Global Medical & Scientific Affairs, Mannheim, Germany, 3Roche Diabetes Care, Inc., Test Development, Diabetes Care, Indianapolis, United States of America
Topic:
AS06 Glucose sensors
1Doctor Peset University Hospital, Department Of Endocrinology, Valencia, Spain, 2Doctor Peset University Hospital, Fisabio Foundation, Valencia, Spain
An estimated HbA1c was calculated for every TIR with a result of 7.1% for TIR of 70%. The HbA1c‐GMI discordance (the absolute difference between the two) was analyzed in subgroups.
To analyze the influence of glycemic variability on GMI‐HbA1c relationship, the group was split according to coefficient of variation (CV).
Topic:
AS06 Glucose sensors
F. Benedetti1,2,
1State University of Santa Catarina, Department Of Electrical Engineering, Joinville, Brazil, 2Unifebe, Medicine, Brusque, Brazil
Topic:
AS06 Glucose sensors
1Imperial College, Department Of Metabolism, Digestion And Reproduction,, London, United Kingdom, 2Imperial College Healthcare NHS Trust, Diabetes & Endocrinology, London, United Kingdom
Topic:
AS06 Glucose sensors
1Poznan University of Medical Sciences, Department Of Reproduction, Poznan, Poland, 2Poznan University of Medical Sciences, Department Of Histology And Embryology, Poznan, Poland, 3Poznan University of Medical Sciences, Doctoral School, Poznan, Poland
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
E. Benito Garcia1, J. Vega2, E. Daza2, W.‐N. Lee2, A. Kennedy3,
1Sanofi, General Medicines, Paris, France, 2Evidation Health Inc., Data Analytics, San Mateo, United States of America, 3Sanofi, General Medicines, Bridgewater, United States of America
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
Una Health GmbH, Medical & Regulatory, Berlin, Germany
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
1The University of Western Ontario, Schulich School Of Medicine & Dentistry, London, Canada, 2University of Bordeaux, Faculty Of Medicine, Bordeaux, France, 3Sanofi, Digital Health Care, Bridgewater, United States of America, 4Optum, Health Economics & Outcomes Research, Eden Prairie, United States of America, 5Sanofi, Real‐world Evidence, Bridgewater, United States of America, 6The Frist Clinic, Internal Medicine, Nashville, United States of America, 7University Hospital of Freiburg, Department Of Internal Medicine Ii, Freiburg, Germany
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
1Sanofi, General Medicines, Bridgewater, United States of America, 2Sanofi, General Medicines, Cambridge, United States of America, 3Evidinno Outcomes Research Inc., Evidence Synthesis, Vancouver, Canada
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
M. Caccelli,
GluCare Integrated Diabetes Center, Diabetes, Dubai, United Arab Emirates
Background and Aims: Implementation by health care providers (HCPs) of remote monitoring programs combined with digital health solutions suggests a promising direction in medication regimen simplification and adherence in improving chronic diseases such as type 2 Diabetes Mellitus (T2D) and complications. The primary goal of this study is to assess the effectiveness and safety of a novel continuous GluCare.Health care model for the management of T2D over one year. The study assesses the model's association with medication reduction, increased medication adherence, and improved clinical biomarkers related to T2D and glycated hemoglobin reduction over one year.
Methods: A retrospective study including 71 T2D patients was conducted. Medication records were analyzed and statistically compared between the baseline and 3, 6, 9, and 12 months after the intervention. T‐tests and nonparametric Wilcoxon were applied. Statistically significant results were set at 5% and 10% levels. We used a two‐tailed p‐value as a more conservative approach than a one‐tailed one. Additionally, an effect size analysis was conducted to make judgments about the magnitude of medication reduction.
Results: The results suggest that the effect of the GluCare intervention in medication reduction is already significant at 3 months follow‐up (p = 0.002) and are also relevant after one year (p = 0.02).
Conclusions: Among T2D patients, the strategy of medication reduction guided by continuous monitoring and engagement via the hyper‐personalized, technology‐enabled GluCare.Health model of care had a positive impact within a 3‐month period. The intervention improved medication adherence and it may be a cost‐effective and cost‐saving solution for the management of T2D.
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
1Fitterfly Healthtech Pvt Ltd, Chief Executive Officer, Navi Mumbai, India, 2Fitterfly Healthtech Pvt Ltd, Scientific Writing And Research Department, Navi mumbai, India, 3Apollo Hospital, Department Of Endocrinology And Diabetology, Navi Mumbai, India, 4MS Ramaiah Memorial Hospital, Department Of Endocrinology And Diabetology, Bengaluru, India, 5Jupiter Hospital, Department Of Endocrinology And Diabetology, Mumbai, India, 6Dr. Diabeat, Department Of Diabetes, Obesity And Metabolic Disorders, Ahmedabad, India, 7Medisecure Superspecialtiy Hospital and Nursing Home, Department Of Diabetology, Navi Mumbai, India, 8Cordis Heart Institute, Department Of Cardiology, Mumbai, India, 9Fortis Hospital, Department Of Endocrinology And Diabetology, Mumbai, India, 10Blue Circle Clinic, Department Of Diabetology, Mumbai, India
Background and Aims: The study aims at analyzing the effectiveness of the Fitterfly Diabetes CGM digital therapeutics program for improving glycemic control and metabolic parameters among people with T2DM.
Methods: De‐identified data of 145 participants with T2DM and BMI ≥25.0 kg/m2 (Mean age: 47.45 ± 12.71 years, Females: 56.97 % (94/145)) was analyzed. The participants had access to continuous glucose monitoring (CGM) in the first 14 days of the program. Based on usual lifestyle in week‐1, a modified lifestyle plan was introduced from week 2 till program completion (day 90). HbA1c, weight, and BMI was analyzed pre and post the program. Wilcoxon signed rank test was used for statistical analysis. All data has been reported as median (IQR).
Results: In the week‐2, a significant median reduction in average blood glucose and time‐above‐range was observed by ‐8.00 (‐23.00, ‐0.50) mg/dl and ‐5.40 (‐16.00, 0.00) % from week‐1 baseline of 140.00 (115.00, 170.00) mg/dl and 27.60 (11.10, 48.50) % respectively (p < 0.0001 for both). Time‐in‐range significantly increased by 6.00 (‐2.00, 14.00) % from a baseline of 66.00 (48.60, 79.00) % (p < 0.0001). After the program, a significant median reduction in HbA1c, weight and BMI was observed by ‐1.00 (‐2.00, ‐0.30) %, ‐2.30 (‐5.00, ‐1.00) kg, and ‐0.80 (‐1.70, ‐0.20) kg/m2 from a pre‐program baseline of 8.10 (7.20, 9.10) %, 80.00 (72.00, 90.50) kg and 28.80 (26.50, 31.70) kg/m2 respectively (p < 0.0001 for all).
Conclusions: CGM based Fitterfly Diabetes CGM program can help in improved diabetes management, as significant improvement in glycemic control and metabolic parameters was observed after program completion.
Topic:
AS07 Informatics in the Service of Medicine; Telemedicine, Software and other Technologies
1Fitterfly Healthtech Pvt Ltd, Chief Executive Officer, Navi Mumbai, India, 2Fitterfly Healthtech Pvt Ltd, Scientific Writing And Research Department, Navi mumbai, India, 3Bharti Research Institute of Diabetes & Endocrinology, Haryana, Department Of Endocrinology And Diabetology, Karnal, India, 4Apollo Spectra, Department Of Diabetology, Bengaluru, India, 5Sweet Clinics, Department Of Endocrinology And Diabetology, Navi Mumbai, India, 6Dr Raskar's Diabetes Clinic, Department Of Endocrinology And Diabetology, Mumbai, India, 7Sharda Skin and Sugar Clinic, Department Of Diabetology, Bangalore, India, 8Shivam Medi‐Care Clinic, Diabetology, Ahmedabad, India, 9Fitterfly Healthtech Pvt Ltd, Scientific Writing And Research Department, Navi Mumbai, India, 10Fitterfly Healthtech Pvt Ltd, Department Of Operations, Navi Mumbai, India, 11Fitterfly Healthtech Pvt Ltd, Department Of Metabolic Nutrition, Navi Mumbai, India
Background and Aims: Continuous glucose monitoring (CGM) enables real‐time glucose monitoring, glycemic awareness, and control. Newer CGM devices do not require calibration using SMBG readings. The present study leverages participant's baseline characteristics to predict blood glucose levels. This will help reduce the need for SMBG and fingerstick testing while instilling confidence in CGM among patients.
Methods: A multiple linear regression model was used to examine the influences of baseline HbA1c level on CGM based fasting blood glucose reading (CGM‐FBS, 5 am) and postprandial blood glucose reading (CGM‐PPBG, 2h post lunch and dinner) after controlling for baseline factors of age and gender. All participants (n = 237) were enrolled in the Fitterfly Diabetes program (Fitterfly Healthtech Pvt Ltd, Mumbai) with CGM sensors (FreeStyle Libre Pro, Abbott Diabetes Care) being applied on day 1 of the program. Statistical analysis was performed using R‐software (Version 4.1.2).
Results: CGM‐FBS reading was found to be significantly associated with HbA1c (β = 14.894, P < 0.001) with an intercept of 23.638 (P = 0.199). No significant association was observed for age (β = ‐0.148, P = 0.578) and gender (male, β = ‐8.225, P = 0.188). CGM‐PPBG reading was found to be significantly associated with HbA1c (β = 19.101, P < 0.001) and gender (male, β = ‐16.996, P = 0.040) with an intercept of 48.115 (P = 0.048). No significant association was observed for age (β = ‐0.136, P = 0.697).
Conclusions: HbA1c values along with baseline parameters can help in prediction of FBS and PPBS readings on a CGM device, thus helping in more informed use of CGM sensors. This data can also be used for assessing the need for calibration of CGM sensors.
Topic:
AS08 Insulin Pumps
SAINT GEORGE CHANIA GENERAL HOSPITAL, Pediatric ‐pediatric Diabetes Clinic, CHANIA CRETE GREECE, Greece
Topic:
AS08 Insulin Pumps
1Copenhagen University Hospital, Steno Diabetes Center Copenhagen, Copenhagen, Denmark, 2Herlev University Hospital, Department Of Pediatrics, Herlev, Denmark, 3Dapplix, Medical, Herlev, Denmark
Topic:
AS08 Insulin Pumps
1Clinica Santa Maria, Endocrinology, Santiago, Chile, 2Clinica Universidad de Los Andes, Pediatric Endocrinology, Santiago, Chile, 3University of Chile, Nutrition, Santiago, Chile
Background and Aims: The use of hybrid closed loop systems has significantly grown to improve glycemic control. The aim of this study is to evaluate the impact of switching from MDI or 640G to a Minimed 780G and its effect on time in range (TIR, 70‐180mg/dL) in a group of type 1 diabetic children and adolescents.
Methods: Prospective study. Recruitment of 14 patients, 9.7 ± 2 years of age and 31.3% female. Clinical characteristics described in table 1. They were separated into two groups: MDI (G1; n = 6) and 640G users (G2; n = 8). Both groups changed their therapy to 780G. Changes in TIR, frequency of hypo and hyperglycemia, insulin/kg dose and GMI were evaluated at 1, 3, 6, 9 and 12 months using it. Comparisons determined using ANOVA test.
Results: TIR increased, time spent >180mg/dL decreased significantly and hypoglycemia showed lower frequency for all patients. G2 patients presented higher improvement in TIR in the first month and continued afterwards. (Figure 1. Variation in TIR during study).
GMI was reduced only in G2 (7.3 ± 0.4% before study and 6.7 ± 0.3% after one month using 780G (p = 0.018) and stabilized during follow‐up (Figure 2. Variation of GMI during study in G1 and G2).
G1 increased progressively insulin/kg from 0.4 ± 0.2U/kg to 0.7 ± 0.1U/kg at 12 months (p = 0.05). Patients spent 90.4 ± 1.2% (range 64‐100%) of the time in Smart Guard mode during study.
Conclusions: Switching therapy from MDI or 640G to 780G improved TIR and reduced hyperglycemia, without increasing hypoglycemia, in this group of children and adolescents from Chile.
Topic:
AS08 Insulin Pumps
Hospital Universitario San Ignacio, Endocrinology Unit, Bogota, Colombia
Topic:
AS08 Insulin Pumps
1Ulm University, Institute Of Epidemiology And Medical Biometry, Zibmt, Ulm, Germany, 2Children's Hospital, Sana Klinikum Lichtenberg, Berlin, Germany, 3Children's Hospital, Clinical Center Chemnitz, Chemnitz, Germany, 4Heinrich‐Heine‐University Düsseldorf, Department Of Pediatrics, Düsseldorf, Germany, 5Medical University of Vienna, Department Of Pediatric And Adolescent Medicine, Vienna, Austria, 6Clinical Centre Hanau, Clinic For Children And Adolescent Medicine, Hanau, Germany, 7AKK Altonaer Kinderkrankenhaus, Department Of Paediatric Endocrinology, Hamburg, Germany, 8St. Marien Hospital Landshut, Department Of Pediatrics, Landshut, Germany, 9Children's and Youth Hospital “Auf Der Bult”, Diabetes Centre For Children And Adolescents, Hannover, Germany
Topic:
AS09 New Medications for Treatment of Diabetes
1Lexicon Pharmaceuticals, Clinical Development, Basking Ridge, United States of America, 2University of Toronto, Department Of Medicine, Toronto, Canada, 3University Medical Center Groningen, Department Of Pharmacy And Pharmacology, Groningen, Netherlands, 4BDC, Pediatrics And Internal Medicine, aurora, United States of America
Topic:
AS12 Advanced Medical Technologies to Be Used in Hospitals
1School of Medicine, University of Zagreb, Department Of Obstetrics And Gynecology, Zagreb, Croatia, 2University Hospital Centre Zagreb, Department Of Obstetrics And Gynecology, Zagreb, Croatia
Topic:
AS13 New Technologies for Treating Obesity and Preventing Related Diabetes
C.O. Choi1, D.G. Kim1, C.‐B. Park1, H.C. Jung1, S.H. Ki2,
1Gwangju Pharmaceutical Association, Policy Team, Gyeongyeol‐ro, Seo‐gu, Korea, Republic of, 2Chosun University, College Of Pharmacy, Gwangju, Korea, Republic of, 3Mokpo National University, College Of Pharmacy, Jeonnam, Korea, Republic of
Topic:
AS13 New Technologies for Treating Obesity and Preventing Related Diabetes
1Shiga University of Medical Science, Department Of Fundamental Nursing, Ōtsu, Japan, 2Kobe University, Graduate School of Health Sciences, Department Of Nursing, Kobe, Japan, 3Osaka University, Graduate School of Medicine, Department Of Mathematical Health Science, Suita, Japan, 4Osaka University, Graduate School of Medicine, Department Of Metabolic Medicine, Suita, Japan
Topic:
AS13 New Technologies for Treating Obesity and Preventing Related Diabetes
J. Lu, M. Li, A. Zhu, N. Zeng,
Guangdong Pharmaceutical University, Key Laboratory Of Metabolic Phenotyping In Model Animals, Guangzhou, China
Topic:
AS14 Blood Glucose Monitoring and Glycemic Control in the Hospitals
T. Ashraf,
Imperial College London Diabetes Centre, Research Institute, Abu Dhabi, United Arab Emirates
Topic:
AS14 Blood Glucose Monitoring and Glycemic Control in the Hospitals
G. Aguayo1,
1Luxembourg Institute of Health, Department Of Precision Health, Strassen, Luxembourg, 2Fondation Francophone pour la Recherche sur le Diabète, Diabète, Paris, France, 3Université Paris 13, Sorbonne Paris Cité, Umr U1153 Inserm/u1125 Inra/cnam, Bobigny, France, 4AP‐HP, Avicenne Hospital, Paris 13 University, Sorbonne Paris Cité, CRNH‐IdF, CINFO, Department Of Endocrinology‐diabetology‐nutrition, Bobigny, France, 5Hôpital Lariboisière APHP, Centre Universitaire D'étude Du Diabète Et De Ses Complications (cudc), Paris, France, 65. Institut Necker Enfants Malades, INSERM U1151, CNRS UMR 8253, Immediab Laboratory, Paris, France
Topic:
AS15 Human factor in the use of diabetes technology
1IRCCS San Raffaele Hospital, Department Of Pediatrics, Milan, Italy, 2Diabetes Research Institute, Hospital San Raffaele, Milano, Italy, 3Università Vita‐Salute San Raffaele, School Of Medicine, Milan, Italy
Topic:
AS15 Human factor in the use of diabetes technology
1University of Colorado Anschutz / Barbara Davis Center, Pediatric Endocrinology, Aurora, United States of America, 2University of Colorado Anschutz, College Of Nursing, Aurora, United States of America, 3University of Colorado Boulder, Department Of Information Science, Boulder, United States of America, 4University of Colorado Boulder, Department Of Computer Science, Boulder, United States of America, 5University of Colorado Anschutz Medical Campus, Barbara Davis Center, Aurora, United States of America, 6University of Colorado School of Medicine, Barbara Davis Center For Diabetes, Aurora, United States of America
Topic:
AS15 Human factor in the use of diabetes technology
1CHU de la Réunion, Department Of Endocrinology, Diabetology And Nutrition, ST DENIS, France, 2CHU de Strasbourg, Department Of Endocrinology Diabetes And Nutrition, Strasbourg, France, 3Dinnosante, Diabetes, ST DENIS, France, 4ISIS, Diabetes, ST DENIS, France, 5Caz Diabete, Medical Cabinet, ST DENIS, France, 6CHU de la Réunion, Inserm Cic1410, ST DENIS, France
Topic:
AS15 Human factor in the use of diabetes technology
A. Jalilova1, B. Şentürk Pilan2, G. Demir1, B. Özbaran2, S.G. Köse2, S. Özen1, Ş. Darcan1,
1Ege University Faculty of Medicine, Department Of Pediatric Endocrinology, BORNOVA, Turkey, 2Ege University Faculty of Medicine, Department Of Child And Adolescent Psychiatry, BORNOVA, Turkey
Topic:
AS15 Human factor in the use of diabetes technology
1Sanofi, General Medicines, Paris, France, 2Sturm & Drang, Innovation Strategy, Hamburg, Germany, 3Sanofi, General Medicines, Reading, United Kingdom, 4Sanofi, General Medicines, Bridgewater, United States of America, 5Independent researcher, N/a, Lakeland, United States of America, 6Ottawa Hospital Research Institute, Centre For Implementation Research, Ottawa, Canada, 7University of Otttawa, Faculty Of Medicine, Ottawa, Canada, 8Jewish General Hospital, Centre For Nursing Research, Montreal, Canada
Topic:
AS15 Human factor in the use of diabetes technology
1Catholic University School of Medicine, Diabetes Care Unit, Rome, Italy, 2Catholic University, Department Of Psychology, Rome, Italy, 3Catholic University School of Medicine, Department Of Endocrinology, Rome, Italy
Topic:
AS15 Human factor in the use of diabetes technology
1Catholic University School of Medicine, Diabetes Care Unit, Rome, Italy, 2Catholic University School of Medicine, Institute Of Neurology, Rome, Italy
Topic:
AS15 Human factor in the use of diabetes technology
Catholic University School of Medicine, Diabetes Care Unit, Rome, Italy
Topic:
AS18 Other
1Regional Centre for Biotechnology, Disease Biology Laboratory, Faridabad, India, 2All India Institute of Medical Sciences, Endocrinology, Delhi, India
Topic:
AS18 Other
1Tashkent Pediatric Medical Institute, Endocrinology, Tashkent, Uzbekistan, 2Tashkent Pediatric Medical Institute, Endocrinology, тaшкeнт, Uzbekistan
Topic:
AS18 Other
IIT Ropar, Department Of Biomedical Engineering, Ropar, India
Topic:
AS18 Other
1Novo Nordisk, Medical & Science, Patient Focused Drug Developement, Søborg, Denmark, 2Kings College London, Department Of Diabetes, London, United Kingdom, 3University of Southern Denmark, Department Of Psychology, Odense, Denmark, 4University of Southern Denmark, Department Of Psychology, denmark., Odense, Denmark, 5Kings College London, Diabetes Research Group, London, United Kingdom, 6Kings College London, Diabetes Research Group, London, Ireland, 7Radboud University Medical Center, Medical Psychology, Nijmegen, Netherlands, 8Novo Nordisk, Pharmacometrics Department Of Data Science, Copenhagen, Denmark, 9Novo Nordisk, Data Science, Department Of Pharmacometrics, Copenhagen, Denmark, 10Division of Endocrinology and Diabetology, Department Of Internal Medicine, Medical University Of Graz, Graz, Austria, 11Medical University of Graz, Division Of Endocrinology & Diabetology, Graz, Austria, 12Montpellier University Hospital, University of Montpellier, Department Of Endocrinology, Diabetes And Nutrition, Montpellier, France, 13Radboud university medical center, Department Of Internal Medicine, Nijmegen, Netherlands, 14Division of Endocrinology & Diabetology, Medical University Of Graz, Graz, Austria, 15University of Sheffield, University Of Sheffield, Sheffield, United Kingdom, 16Maastricht University, Carim School For Cardiovascular Diseases, Maastricht, Netherlands, 17Radboud University Medical Center, Internal Medicine, Nijmegen, Netherlands, 18University of Copenhagen, Institute Of Clinical Medicine, Copenhagen, Denmark, 19Nordsjællands Hospital Hillerød, Department Of Endocrinology And Nephrology, Hillerød, Denmark, 20University of Cambridge, Wellcome Trust Mrc Institute Of Metabolic Science And Department Of Medicine, Cambridge, United Kingdom, 21School of Health and Related Research (ScHARR), University of Sheffield, University Of Sheffield, Sheffield, United Kingdom, 22University of Dundee, School of Medicine, Division Of Molecular And Clinical Medicine, Dundee, United Kingdom, 23School of Psychology,, Deakin University, Deakin, Australia, 24Diabetes Victoria, The Australian Centre For Behavioural Research In Diabetes, Melbourne Victoria, Australia, 25Deakin University, School Of Psychology, Geelong, Australia, 26University Hospital of Leicester NHS Trust, Leicester Diabetes Centres, Leicester, United Kingdom, 27Kings College London, 2. department Of Diabetes, School Of Life Course Sciences, Faculty Of Life Sciences And Medicine, London, United Kingdom, 28Steno Diabetes Center Odense, Steno Diabetes Center Odense (sdco), Odense, Denmark
ATTD 2023 Read by Title
1Tribe Consulting, Diabetes Research, Neutral Bay, Australia, 2Royal Melbourne Hospital, Diabetes And Endocrinology, Parkville, Australia
Topic:
AS06 Glucose sensors
Meenakshi Mission Hospital and Research Centre, Diabetology, Madurai, India
Topic:
AS06 Glucose sensors
University Clinic for Endocrinology, Diabetes and Metabolic Disorders, Ss. Cyril and Methodius University, Centar For Insulin Pump And Sensors, Skopje, North Macedonia
Topic:
AS09 New Medications for Treatment of Diabetes
1College of Medicine, AlMaarefa University, Basic Medical Sciences, Riyadh, Saudi Arabia, 2Faculty of Pharmacy, University of Tabuk, Department Of Pharmaceutical Chemistry, Tabuk, Saudi Arabia, 3Faculty of Pharmacy, Mansoura University, Biochemistry Department, Mansoura, Egypt, 4Faculty of Pharmacy, Mansoura University, Pharmacology And Toxicology Department, Mansoura, Egypt
Topic:
AS09 New Medications for Treatment of Diabetes
University of Madeira, Cqm ‐ Centro De Química Da Madeira, Funchal, Portugal
Topic:
AS09 New Medications for Treatment of Diabetes
Mettu university, Pharmacy, Mettu, Ethiopia
Topic:
AS09 New Medications for Treatment of Diabetes
Universidade Federal do Ceará, Química Orgânica E Inorgânica, Fortaleza CE, Brazil
Topic:
AS11 Devices Focused on Diabetic Preventions
1Lithuanian University of Health Sciences, Endocrinology, Kaunas, Lithuania, 2Lithuanian University of Health Sciences, Institute Of Endocrinology, Medical Academy, Kaunas, Lithuania
Topic:
AS13 New Technologies for Treating Obesity and Preventing Related Diabetes
1UNAM, Instituto De Neurobiología, Juriquilla, Mexico, 2Instituto de Neurobiología, Unam, Querétaro, Mexico, 3UNAM, Facultad De Ingenieria, CDMX, Mexico, 4Facultad de Ingenieria, Inteligencia Artificial, CDMX, Mexico, 5Instituto de Neurobiología UNAM, Cell And Molecular Neurobiology, Juriquilla, Mexico, 6IIMAS, Unam, CDMX, Mexico, 7UNAM, Facultad De Ciencias Umdi, Juriquilla, Mexico, 8APEC Hospital de la Ceguera, Research Department, CDMX, Mexico, 9UNAM ENES León, Clínica De Salud Visual, León, Mexico, 10INDEREB, Ophthalmology, Querétaro, Mexico, 11Instituto Mexicano de Oftalmología, Retina, Queretaro, Mexico
Topic:
AS14 Blood Glucose Monitoring and Glycemic Control in the Hospitals
King Saud University Medical City, University Diabetes Center, Riyadh, Saudi Arabia
Topic:
AS15 Human factor in the use of diabetes technology
University Hospitals of Geneva, Diabetes Pediatric, Geneva, Switzerland
Topic:
AS16 Trials in progress
1Primary healthcare center Casco Antiguo, Primary Care, Cartagena, Spain, 2Primary healthcare center Bollullos de la Mitación, Primary Care, Sevilla, Spain, 3Primary healthcare center Sarria, Primary Care, Barcelona, Spain, 4Sanofi, Medical Department, Barcelona, Spain
Topic:
AS18 Other
1Medical University of Sofia, Pharmacology And Toxicology, Sofia, Bulgaria, 2Institute of Neurobiology, Behavior Neurobiology, Sofia, Bulgaria
Topic:
AS18 Other
Norfolk and Norwich University Hospitals NHS Foundation Trust, Paediatrics, NORWICH, United Kingdom
Topic:
AS18 Other
Nursing, Community, Faculty Of Nursing, Egypt
Topic:
AS18 Other
M. Fermin, N. Rubio,
University of Texas Health Science Center at Houston, Pediatric Endocrinology, Houston, United States of America