Journal of Diabetes Science and Technology

Insulin is a top source of adverse drug events in the hospital, and glycemic control is a focus of improvement efforts across the country. Yet, the majority of hospitals have no data to gauge their performance on glycemic control, hypoglycemia rates, or hypoglycemic management. Current tools to outsource glucometrics reports are limited in availability or function.

Society of Hospital Medicine (SHM) faculty designed and implemented a web-based data and reporting center that calculates glucometrics on blood glucose data files securely uploaded by users. Unit labels, care type (critical care, non–critical care), and unit type (eg, medical, surgical, mixed, pediatrics) are defined on upload allowing for robust, flexible reporting. Reports for any date range, care type, unit type, or any combination of units are available on demand for review or downloading into a variety of file formats. Four reports with supporting graphics depict glycemic control, hypoglycemia, and hypoglycemia management by patient day or patient stay. Benchmarking and performance ranking reports are generated periodically for all hospitals in the database.

In all, 76 hospitals have uploaded at least 12 months of data for non–critical care areas and 67 sites have uploaded critical care data. Critical care benchmarking reveals wide variability in performance. Some hospitals achieve top quartile performance in both glycemic control and hypoglycemia parameters.

This new web-based glucometrics data and reporting tool allows hospitals to track their performance with a flexible reporting system, and provides them with external benchmarking. Tools like this help to establish standardized glucometrics and performance standards.

In the setting of Meaningful Use laws and professional society guidelines, hospitals are rapidly implementing electronic glycemic management order sets. There are a number of best practices established in the literature for glycemic management protocols and programs. We believe that this is the first published account of the detailed steps to be taken to design, implement, and optimize glycemic management protocols in a commercial computerized provider order entry (CPOE) system.

Prior to CPOE implementation, our hospital already had a mature glycemic management program. To transition to CPOE, we underwent the following 4 steps: (1) preparation and requirements gathering, (2) design and build, (3) implementation and dissemination, and (4) optimization. These steps required more than 2 years of coordinated work between physicians, nurses, pharmacists, and programmers. With the move to CPOE, our complex glycemic management order sets were successfully implemented without any significant interruptions in care. With feedback from users, we have continued to refine the order sets, and this remains an ongoing process.

Successful implementation of glycemic management protocols in CPOE is dependent on broad stakeholder input and buy-in. When using a commercial CPOE system, there may be limitations of the system, necessitating workarounds. There should be an upfront plan to apply resources for continuous process improvement and optimization after implementation.

Currently used error grids for assessing clinical accuracy of blood glucose monitors are based on out-of-date medical practices. Error grids have not been widely embraced by regulatory agencies for clearance of monitors, but this type of tool could be useful for surveillance of the performance of cleared products. Diabetes Technology Society together with representatives from the Food and Drug Administration, the American Diabetes Association, the Endocrine Society, and the Association for the Advancement of Medical Instrumentation, and representatives of academia, industry, and government, have developed a new error grid, called the surveillance error grid (SEG) as a tool to assess the degree of clinical risk from inaccurate blood glucose (BG) monitors.

A total of 206 diabetes clinicians were surveyed about the clinical risk of errors of measured BG levels by a monitor. The impact of such errors on 4 patient scenarios was surveyed. Each monitor/reference data pair was scored and color-coded on a graph per its average risk rating. Using modeled data representative of the accuracy of contemporary meters, the relationships between clinical risk and monitor error were calculated for the Clarke error grid (CEG), Parkes error grid (PEG), and SEG.

SEG action boundaries were consistent across scenarios, regardless of whether the patient was type 1 or type 2 or using insulin or not. No significant differences were noted between responses of adult/pediatric or 4 types of clinicians. Although small specific differences in risk boundaries between US and non-US clinicians were noted, the panel felt they did not justify separate grids for these 2 types of clinicians. The data points of the SEG were classified in 15 zones according to their assigned level of risk, which allowed for comparisons with the classic CEG and PEG. Modeled glucose monitor data with realistic self-monitoring of blood glucose errors derived from meter testing experiments plotted on the SEG when compared to the data plotted on the CEG and PEG produced risk estimates that were more granular and reflective of a continuously increasing risk scale.

The SEG is a modern metric for clinical risk assessments of BG monitor errors that assigns a unique risk score to each monitor data point when compared to a reference value. The SEG allows the clinical accuracy of a BG monitor to be portrayed in many ways, including as the percentages of data points falling into custom-defined risk zones. For modeled data the SEG, compared with the CEG and PEG, allows greater precision for quantifying risk, especially when the risks are low. This tool will be useful to allow regulators and manufacturers to monitor and evaluate glucose monitor performance in their surveillance programs.

The surveillance error grid (SEG) analysis is a tool for analysis and visualization of blood glucose monitoring (BGM) errors, based on the opinions of 206 diabetes clinicians who rated 4 distinct treatment scenarios. Resulting from this large-scale inquiry is a matrix of 337 561 risk ratings, 1 for each pair of (reference, BGM) readings ranging from 20 to 580 mg/dl. The computation of the SEG is therefore complex and in need of automation.

The SEG software introduced in this article automates the task of assigning a degree of risk to each data point for a set of measured and reference blood glucose values so that the data can be distributed into 8 risk zones. The software’s 2 main purposes are to (1) distribute a set of BG Monitor data into 8 risk zones ranging from none to extreme and (2) present the data in a color coded display to promote visualization. Besides aggregating the data into 8 zones corresponding to levels of risk, the SEG computes the number and percentage of data pairs in each zone and the number/percentage of data pairs above/below the diagonal line in each zone, which are associated with BGM errors creating risks for hypo- or hyperglycemia, respectively.

To illustrate the action of the SEG software we first present computer-simulated data stratified along error levels defined by ISO 15197:2013. This allows the SEG to be linked to this established standard. Further illustration of the SEG procedure is done with a series of previously published data, which reflect the performance of BGM devices and test strips under various environmental conditions.

We conclude that the SEG software is a useful addition to the SEG analysis presented in this journal, developed to assess the magnitude of clinical risk from analytically inaccurate data in a variety of high-impact situations such as intensive care and disaster settings.

The Hypoglycemia-Hyperglycemia Minimizer (HHM) System aims to mitigate glucose excursions by preemptively modulating insulin delivery based on continuous glucose monitor (CGM) measurements. The “aggressiveness factor” is a key parameter in the HHM System algorithm, affecting how readily the system adjusts insulin infusion in response to changing CGM levels.

Twenty adults with type 1 diabetes were studied in closed-loop in a clinical research center for approximately 26 hours. This analysis focused on the effect of the aggressiveness factor on the insulin dosing characteristics of the algorithm and, to a lesser extent, on the glucose control results observed.

As the aggressiveness factor increased from conservative to medium to aggressive: the maximum observed insulin dose delivered by the algorithm—which is designed to give doses that are corrective in nature every 5 minutes—increased (1.00 vs 1.15 vs 2.20 U, respectively); tendency to adhere to the subject’s nominal basal dose decreased (61.9% vs 56.6% vs 53.4%); and readiness to decrease insulin below basal also increased (18.4% vs 19.4% vs 25.2%). Glucose analyses by both CGM and Yellow Springs Instruments (YSI) indicated that the aggressive setting of the algorithm resulted in the least time spent at levels >180 mg/dL, and the most time spent between 70-180 mg/dL. There was no severe hyperglycemia, diabetic ketoacidosis, or severe hypoglycemia for any of the aggressiveness values investigated.

These analyses underscore the importance of investigating the sensitivity of the HHM System to its key parameters, such as the aggressiveness factor, to guide future development decisions.

There is a perception that patients with diabetes struggle to produce sufficient blood to fill glucose test strips, including strips with 1-µL fill requirements. The purpose of this study was to determine the volume of blood expressed when these patients perform routine fingersticks using their own lancing device and sampling technique and to evaluate the relationship between blood volume and pain.

Sixty-four patients (type 1 or type 2 diabetes) performed 8 fingersticks using their own lancing device and preferred depth setting and lancing technique. Eight different commercially available lancing systems were used (8 patients/system). Blood volume and perceived pain were recorded after each fingerstick.

The mean blood volume across all patients was 3.1 µL (512 fingersticks), with 97% of patients expressing a mean of ≥1.0 µL of blood. There was no correlation between pain response and the volume of blood expressed. Nearly all patients agreed that they could easily and comfortably obtain a 1-µL blood sample, and most patients actually preferred a larger drop size to ease sampling and avoid wasting strips.

These results provide evidence across 8 lancing systems that challenge the current perceptions that patients with diabetes struggle to produce sufficient blood samples to fill most test strips, including those with 1-µL fill requirements, and that obtaining larger volumes of blood is more painful. These results are consistent with the previous literature suggesting that patients derive no real benefits from very low strip volumes and generally prefer a blood drop size that enables them to confidently fill their test strip.

The effectiveness and safety of continuous glucose monitors (CGMs) is dependent on their accuracy and reliability. The objective of this study was to compare 3 CGMs in adult and pediatric subjects with type 1 diabetes under closed-loop blood-glucose (BG) control. Twenty-four subjects (12 adults) with type 1 diabetes each participated in one 48-hour closed-loop BG control experiment.

Venous plasma glucose (PG) measurements obtained every 15 minutes (4657 values) were paired in time with corresponding CGM glucose (CGMG) measurements obtained from 3 CGMs (FreeStyle Navigator, Abbott Diabetes Care; G4 Platinum, Dexcom; Enlite, Medtronic) worn simultaneously by each subject.

The Navigator and G4 Platinum (G4) had the best overall accuracy, with an aggregate mean absolute relative difference (MARD) of all paired points of 12.3 ± 12.1% and 10.8 ± 9.9%, respectively. Both had lower MARDs of all paired points than Enlite (17.9 ± 15.8%, P < .005). Very large errors (MARD > 50%) were less common with the G4 (0.5%) than with the Enlite (4.3%, P = .0001) while the number of very large errors with the Navigator (1.4%) was intermediate between the G4 and Enlite (P = .1 and P = .06, respectively). The average MARD for experiments in adolescent subjects were lower than in adult subjects for the Navigator and G4, while there was no difference for Enlite. All 3 devices had similar reliability.

A comprehensive head-to-head-to-head comparison of 3 CGMs revealed marked differences in both accuracy and precision. The Navigator and G4 were found to outperform the Enlite in these areas.

The purpose of this study was to investigate the effect of using a 1-point calibration approach instead of a 2-point calibration approach on the accuracy of a continuous glucose monitoring (CGM) algorithm.

A previously published real-time CGM algorithm was compared with its updated version, which used a 1-point calibration instead of a 2-point calibration. In addition, the contribution of the corrective intercept (CI) to the calibration performance was assessed. Finally, the sensor background current was estimated real-time and retrospectively. The study was performed on 132 type 1 diabetes patients.

Replacing the 2-point calibration with the 1-point calibration improved the CGM accuracy, with the greatest improvement achieved in hypoglycemia (18.4% median absolute relative differences [MARD] in hypoglycemia for the 2-point calibration, and 12.1% MARD in hypoglycemia for the 1-point calibration). Using 1-point calibration increased the percentage of sensor readings in zone A+B of the Clarke error grid analysis (EGA) in the full glycemic range, and also enhanced hypoglycemia sensitivity. Exclusion of CI from calibration reduced hypoglycemia accuracy, while slightly increased euglycemia accuracy. Both real-time and retrospective estimation of the sensor background current suggest that the background current can be considered zero in the calibration of the SCGM1 sensor.

The sensor readings calibrated with the 1-point calibration approach indicated to have higher accuracy than those calibrated with the 2-point calibration approach.

The objective was to develop an analysis methodology for generating diabetes therapy decision guidance using continuous glucose (CG) data.

The novel Likelihood of Low Glucose (LLG) methodology, which exploits the relationship between glucose median, glucose variability, and hypoglycemia risk, is mathematically based and can be implemented in computer software. Using JDRF Continuous Glucose Monitoring Clinical Trial data, CG values for all participants were divided into 4-week periods starting at the first available sensor reading. The safety and sensitivity performance regarding hypoglycemia guidance “stoplights” were compared between the LLG method and one based on 10th percentile (P₁₀) values.

Examining 13 932 hypoglycemia guidance outputs, the safety performance of the LLG method ranged from 0.5% to 5.4% incorrect “green” indicators, compared with 0.9% to 6.0% for P₁₀ value of 110 mg/dL. Guidance with lower P₁₀ values yielded higher rates of incorrect indicators, such as 11.7% to 38% at 80 mg/dL. When evaluated only for periods of higher glucose (median above 155 mg/dL), the safety performance of the LLG method was superior to the P₁₀ method. Sensitivity performance of correct “red” indicators of the LLG method had an in sample rate of 88.3% and an out of sample rate of 59.6%, comparable with the P₁₀ method up to about 80 mg/dL.

To aid in therapeutic decision making, we developed an algorithm-supported report that graphically highlights low glucose risk and increased variability. When tested with clinical data, the proposed method demonstrated equivalent or superior safety and sensitivity performance.

Hypoglycemia is a common and serious side effect of insulin therapy in patients with diabetes. Early detection and prediction of hypoglycemia may improve treatment and avoidance of serious complications. Continuous glucose monitoring (CGM) has previously been used for detection of hypoglycemia, but with a modest accuracy. Therefore, our aim was to investigate whether a novel algorithm that adds information of the complex dynamic/pattern of heart rate variability (HRV) could improve the accuracy of hypoglycemia as detected by a CGM device.

Data from 10 patients with type 1 diabetes studied during insulin-induced hypoglycemia were obtained. Blood glucose samples were used as reference. HRV patterns and CGM data were combined in a mathematical prediction algorithm. Detection of hypoglycemic periods, performed by the algorithm, was treated as a pattern recognition problem and features/patterns derived from HRV and CGM prior to each blood glucose sample were used to decide if that particular point in time was below the hypoglycemic threshold of 3.9 mmol/L.

A total of 903 samples were analyzed by the novel algorithm, which yielded a sensitivity of 79% and a specificity of 99%. The algorithm was able to detect 16/16 hypoglycemic events with no false positives and had a lead time of 22 minutes as compared to the CGM device.

Detection accuracy and lead time were significantly improved by the novel algorithm compared to that of CGM alone.

In 2008 a Nordic collaboration was established between the quality registries in Denmark, Iceland, Norway, and Sweden to improve quality of care for children with diabetes. This study aimed to describe those registries and confirm that the registry variables are comparable. Selected variables were used to demonstrate outcome measurements.

The organization of the registries and methodology are described. Cross-sectional data for patients between birth and 14.9 years with type 1 diabetes mellitus in 2009 (n = 6523) from 89 centers were analyzed. Variables were age, gender, and diabetic ketoacidosis at onset, together with age, gender, HbA1c, insulin regimen, and severe hypoglycemia at follow-up in 2009.

All 4 registries use a standardized registration at the onset of diabetes and at follow-up, conducted at the local pediatric diabetes centers. Methods for measuring HbA1c varied as did methods of registration for factors such as hypoglycemia. No differences were found between the outcomes of the clinical variables at onset. Significant variations were found at follow-up for mean HbA1c, the proportion of children with HbA1c < 57 mmol/mol (NGSP/DCCT 7.4%), (range 15-31%), the proportion with insulin pumps (range 34-55%), and the numbers with severe hypoglycemia (range 5.6-8.3/100 patient years).

In this large unselected population from 4 Nordic countries, a high proportion did not reach their treatment target, indicating a need to improve the quality of pediatric diabetes care. International collaboration is needed to develop and harmonize quality indicators and offers possibilities to study large geographic populations, identify problems, and share knowledge.

The purpose of this article is to describe challenges associated with successful use of continuous glucose monitoring (CGM) by young children with type 1 diabetes (T1D) and to detail the techniques and products used to improve the duration of sensor wear.

The DirecNet Study Group conducted 2 studies in 169 children with T1D between the ages of 1 and 9 years who were instructed to wear a CGM device daily. Problems related to skin irritation and sensor adhesiveness in these young children presented challenges to daily use of the CGM. Study coordinators instituted a variety of techniques using commercially available products to attempt to overcome these problems.

Three primary factors that contributed to reduced CGM use were identified: the limited body surface area in smaller children, ambient temperature and humidity, as well as the type and duration of physical activity. Using supplemental products to minimize the impact of these factors resulted in improved adherence and reduced skin irritation.

Achieving satisfactory adhesion of the CGM sensor and transmitter may involve finding the right supplemental product or combination of products through trial and error. Optimizing adhesion and minimizing skin irritation can significantly improve duration of use and tolerability of CGM devices by young children.

Today most research on pen needle design revolves around pain perception statements through clinical trials, but these are both costly, timely, and require high sample sizes. The purpose of this study was to test if tissue damage, caused by different types of needles, can be assessed by evaluating skin blood perfusion response around needle insertion sites.

Three common sized pen needles of 28G, 30G, and 32G as well as hooked 32G needles, were inserted into the neck skin of pigs and then removed. Laser Speckle Contrast Analysis was used to measure skin blood perfusion for 20 minutes after the insertions. Seven pigs were included in the study and a total of 118 randomized needle insertions were conducted. Histology was made of tissue samples inserted with 18G, 28G, and 32G needles, and stained to quantify red and white blood cell response.

Based on area under curve, calculated for each individual blood perfusion recording and grouped according to needle type, skin blood perfusion response relates to needle diameter. The response was significantly higher after insertions with 28G and hooked 32G needles than with 30G (P < .05) and 32G (P < .01) needles. Histology results were not significant, but there was a trend of an increased response with increasing needle diameter.

Skin blood perfusion response to pen needle insertions rank according to needle diameter, and the tissue response caused by hooked 32G needles corresponds to that of 28G needles. The relation between needle diameter and trauma when analyzing histology was also suggested.

Insulin pump therapy may be offered to patients with type 2 diabetes that is not controlled by multiple daily injections. Patients with type 2 diabetes may suffer from unrecognized cognitive disabilities, which may compromise the use of a pump device.

To predict patient autonomy, we evaluated 39 patients with type 2 diabetes from our database (n = 143) after continuous subcutaneous insulin infusion (CSII) initiation using (1) an autonomy questionnaire evaluating the patient’s cognitive and operative capacities for CSII utilization, (2) the Montreal Cognitive Assessment (MOCA) for the detection of mild cognitive disabilities, (3) the Hospital Anxiety and Depression Scale (HADS) for the detection of anxiety and depression, and (4) the Diabetes Treatment Satisfaction Questionnaire (DTSQ). Patients were selected to constitute 3 groups matched for age, with different degrees of autonomy at discharge after the initial training program: complete (n = 13), partial (n = 13), or no autonomy (n = 13).

The satisfaction level with the pump device was high. At the last follow-up visit, only 23% of patients did not reach complete autonomy. The autonomy score correlated fairly with the MOCA score (R = 0.771, P < .001). A receiver operating characteristic (ROC) analysis showed that at a cut-off score of 24, the MOCA identified autonomous versus dependent patients at long-term follow-up (area under the ROC curve [AUC], 0.893; sensitivity, 81%; specificity, 81%). The HADS correlated negatively with the autonomy score, and the sociocultural level also influenced autonomy with pump utilization.

Patients with type 2 diabetes with partial autonomy at discharge may progress to complete autonomy. The MOCA and HADS may help predict a patient’s ability to manage with a pump device.

This study evaluated a novel technology for improving accuracy of self-monitoring of blood glucose (SMBG). The technology calibrates each and every test by measuring the response from a predetermined amount of glucose present in the sample chamber of each test strip.

SMBG test strips were modified to include a lid coated with a fast dissolving formulation containing glucose. These test strips were characterized for hematocrit (Hct) and temperature induced error response to develop a calibration algorithm. The modified test strips were used in a clinical evaluation involving fingerstick blood samples from 160 subjects.

Experiments involving Hct and temperature induced errors show that the technology generates a signal characteristic of the error conditions in any particular test, but independent of glucose concentration, allowing a correction algorithm to be derived. The approach substantially reduced Hct and temperature derived errors. Clinical evaluation using fingerstick blood directly applied to prototype strips showed the error (measured as MARD) was reduced from 11.1 to 5.9% by the on-strip correction approach and the number of outliers reduced by approximately 90%.

This technology could improve the accuracy and precision of glucose monitoring systems and so reduce decision errors particularly in clinical situations where hematocrit and temperature may be significant confounders.

Accurate calculation and adjustment of insulin doses is integral to maintaining glycemic control in insulin treated patients. Difficulties with insulin dose calculations may lead to poor adherence to blood glucose monitoring and insulin treatment regimes, resulting in poor metabolic control. The main objective of this study was to evaluate ease of use and user preference of a high specification touch screen blood glucose meter, which has an in-built insulin calculator, compared to patients’ usual method of testing blood glucose and deciding insulin doses.

Patients with diabetes on a multiple daily injection insulin regime used the Test Meter without the insulin calculator and 1 of 3 comparator meters, each for a 7-day period. They then used the Test Meter with the in-built calculator for 10 days. Patients completed an ease of use questionnaire after each 7-day period, a preference questionnaire after the second 7-day period, and a questionnaire comparing the Test Meter with their usual method after the final 10-day period.

Of 164 patients who completed the study, 76% stated a preference for the Test Meter as a diabetes management tool compared to their usual method. A small number of patients preferred familiar methods and/or calculating insulin doses themselves. The log book function of meters was important to most patients.

The Test Meter system with in-built insulin calculator supports people to better manage their diabetes and increases their confidence. Patients have different needs and preferences which should be acknowledged and supported in a patient centered health service.

This study demonstrated the novel application of a “machine-intelligent” mathematical structure, combining differential game theory and Lyapunov-based control theory, to the artificial pancreas to handle dynamic uncertainties.

Realistic type 1 diabetes (T1D) models from the literature were combined into a composite system. Using a mixture of “black box” simulations and actual data from diabetic medical histories, realistic sets of diabetic time series were constructed for blood glucose (BG), interstitial fluid glucose, infused insulin, meal estimates, and sometimes plasma insulin assays. The problem of underdetermined parameters was side stepped by applying a variant of a genetic algorithm to partial information, whereby multiple candidate-personalized models were constructed and then rigorously tested using further data. These formed a “dynamic envelope” of trajectories in state space, where each trajectory was generated by a hypothesis on the hidden T1D system dynamics. This dynamic envelope was then culled to a reduced form to cover observed dynamic behavior. A machine-intelligent autonomous algorithm then implemented game theory to construct real-time insulin infusion strategies, based on the flow of these trajectories through state space and their interactions with hypoglycemic or near-hyperglycemic states.

This technique was tested on 2 simulated participants over a total of fifty-five 24-hour days, with no hypoglycemic or hyperglycemic events, despite significant uncertainties from using actual diabetic meal histories with 10-minute warnings. In the main case studies, BG was steered within the desired target set for 99.8% of a 16-hour daily assessment period. Tests confirmed algorithm robustness for ±25% carbohydrate error. For over 99% of the overall 55-day simulation period, either formal controller stability was achieved to the desired target or else the trajectory was within the desired target.

These results suggest that this is a stable, high-confidence way to generate closed-loop insulin infusion strategies.

The pathogenesis of type 2 diabetes is characterized by insulin resistance and insulin secretory dysfunction. Few existing metabolic tests measure both characteristics, and no such tests are inexpensive enough to enable widespread use.

A hierarchical approach uses 2 down-sampled tests in the dynamic insulin sensitivity and secretion test (DISST) family to first determine insulin sensitivity (SI) using 4 glucose measurements. Second the insulin secretion is determined for only participants with reduced SI using 3 C-peptide measurements from the original test. The hierarchical approach is assessed via its ability to classify 214 individual test responses of 71 females with an elevated risk of type 2 diabetes into 5 bins with equivalence to the fully sampled DISST.

Using an arbitrary SI cut-off, 102 test responses were reassayed for C-peptide and unique insulin secretion characteristics estimated. The hierarchical approach correctly classified 84.5% of the test responses and 94.4% of the responses of individuals with increased fasting glucose.

The hierarchical approach is a low-cost methodology for measuring key characteristics of type 2 diabetes. Thus the approach could provide an economical approach to studying the pathogenesis of type 2 diabetes, or in early risk screening. As the higher cost test uses the same clinical protocol as the low-cost test, the cost of the additional information is limited to the assay cost of C-peptide, and no additional procedures or callbacks are required.

It is hypothesized that early detection of reduced insulin sensitivity (SI) could prompt intervention that may reduce the considerable financial strain type 2 diabetes mellitus (T2DM) places on global health care. Reduction of the cost of already inexpensive SI metrics such as the Matsuda and HOMA indexes would enable more widespread, economically feasible use of these metrics for screening. The goal of this research was to determine a means of reducing the number of insulin samples and therefore the cost required to provide an accurate Matsuda Index value.

The Dynamic Insulin Sensitivity and Secretion Test (DISST) model was used with the glucose and basal insulin measurements from an Oral Glucose Tolerance Test (OGTT) to predict patient insulin responses. The insulin response to the OGTT was determined via population based regression analysis that incorporated the 60-minute glucose and basal insulin values.

The proposed method derived accurate and precise Matsuda Indices as compared to the fully sampled Matsuda (R = .95) using only the basal assay insulin-level data and 4 glucose measurements. Using a model employing the basal insulin also allows for determination of the 1-day HOMA value.

The DISST model was successfully modified to allow for the accurate prediction an individual’s insulin response to the OGTT. In turn, this enabled highly accurate and precise estimation of a Matsuda Index using only the glucose and basal insulin assays. As insulin assays account for the majority of the cost of the Matsuda Index, this model offers a significant reduction in assay cost.

Pharmacokinetic/pharmacodynamic (PK/PD) studies of human regular U-500 insulin (U-500R) at high doses commonly used in clinical practice (>100 units) have not been performed. The current analysis applied PK/PD modeling/simulation to fit the data and simulate single-dose and steady-state PK/PD of U-500R high-dose regimens.

Data from 3 single-dose euglycemic clamp studies in healthy obese and normal-weight patients, and normal-weight patients with type 1 diabetes were used to build the model. The model was sequential (PK inputs fed into PD component). PK was described using a 1-compartment model with first-order absorption and elimination. The model estimated separate absorption rate constants for U-500R and human regular U-100 insulin. The PD component used an effect compartment model, parameterized in terms of maximum pharmacologic effect (E_max) and concentration to achieve 50% of E_max.

The model described the data well. Steady-state PK for once-daily (QD), twice-daily (BID), or thrice-daily (TID) administration appeared to be reached 24 hours after the first dose. At steady-state, QD dosing showed the greatest fluctuations in PK/PD. BID dosing showed a gradual increase in insulin action with each dose and a fairly stable basal insulin effect. For TID dosing, activity was maintained throughout the dosing interval.

PK/PD modeling/simulation of high U-500R doses supports BID or TID administration with an extended duration of activity relative to QD. TID dosing may provide slightly better full-day insulin effect. Additional PK/PD studies and randomized controlled trials of U-500R are needed to validate model predictions in patients with insulin-resistant diabetes requiring high-dose insulin.

With the increasing prevalence of systems allowing automated, real-time transmission of blood glucose data there is a need for pattern recognition techniques that can inform of deleterious patterns in glycemic control when people test. We evaluated the utility of pattern identification with a novel pattern identification system named Vigilant™ and compared it to standard pattern identification methods in diabetes.

To characterize the importance of an identified pattern we evaluated the relative risk of future hypoglycemic and hyperglycemic events in diurnal periods following identification of a pattern in a data set of 536 patients with diabetes. We evaluated events 2 days, 7 days, 30 days, and 61-90 days from pattern identification, across diabetes types and cohorts of glycemic control, and also compared the system to 6 pattern identification methods consisting of deleterious event counts and percentages over 5-, 14-, and 30-day windows.

Episodes of hypoglycemia, hyperglycemia, severe hypoglycemia, and severe hyperglycemia were 120%, 46%, 123%, and 76% more likely after pattern identification, respectively, compared to periods when no pattern was identified. The system was also significantly more predictive of deleterious events than other pattern identification methods evaluated, and was persistently predictive up to 3 months after pattern identification.

The system identified patterns that are significantly predictive of deleterious glycemic events, and more so relative to many pattern identification methods used in diabetes management today. Further study will inform how improved pattern identification can lead to improved glycemic control.

We have previously shown that intravascular microdialysis in a central vein is an accurate method for continuous glucose monitoring in patients undergoing cardiac surgery. However, no hypoglycemia occurred in our earlier studies, prompting further evaluation of the accuracy of intravascular microdialysis in the hypoglycemic range. Thus, this animal study was performed.

A porcine model was developed; hypoglycemia was induced using insulin injections. The pigs were monitored with intravascular microdialysis integrated in a triple-lumen central venous catheter. As reference, venous blood gas samples were taken every 5 minutes and analyzed in a blood gas analyzer. Ethical permission for the animal experiments was obtained from the Stockholm Regional Ethical Committee, reference no N397/09.

A total of 213 paired samples were obtained for analysis, and 126 (59.2%) of these were in the hypoglycemic range (<74 mg/dl). Using Clarke error grid analysis, 100% of the paired samples were in region AB and 99% in region A. The ISO standard (ISO15197) was met. Bland–Altman analysis showed bias (mean difference) ± limits of agreement was −0.18 ± 16.2 mg/dl. No influence from glucose infusions was seen. The microdialysis monitoring system was found to be very responsive in rapid changes in blood glucose concentration.

This study shows that intravascular microdialysis in a central vein is an accurate method for continuous glucose monitoring in hypoglycemia in a porcine experimental model. Furthermore, the system was not influenced by glucose administration and was found to be responsive in rapid blood glucose fluctuations.

In the United States, more than 25 million adults have diabetes, 40% of diabetics have diabetic retinopathy, and diabetes is the leading cause of blindness in people 20 to 74 years of age. Clinical trials have shown that strict control of blood glucose level and other risk factors delays diabetic retinopathy onset, progression, and vision loss.

Patients with Type 1 or Type 2 diabetes mellitus, access to an Apple iPhone or iPad, and no psychological or medical condition that would interfere with the study participated in a nonrandomized clinical trial using SightBook™, a free mobile app that enables self-measurement of visual function and creates a password-protected web account for each patient.

Sixty patients enrolled in the clinical trial over a 6 month period. Twenty-six participants were men and 34 were women, with ages from 23 to 72 years (mean 45 ± 15) and diabetes duration of 1.5 to 50 years (mean 15.5 ± 11.5). Thirty-nine (65%) patients reported Type 1 diabetes and 21 (35%) patients reported Type 2 diabetes. Every patient established a personal web account on SightBook and invited participation of treating physicians; 51 (85%) patients completed the validated self-reported outcome assessments. Diabetologist examinations of 49 (82%) patients demonstrated systolic hypertension (≥140 mgHg) in 20% and hemoglobin A1c ≥ 7.0% in 56%. Ophthalmology examinations of 45 patients showed visual acuity in the worse-seeing eye of < 20/40 in 18% and diabetic retinopathy in 42% of patients.

This clinical trial used a mobile health app to incorporate diabetic patient self-measurement of vision and coordinate the diabetic patient, diabetologist, and ophthalmologist for control of diabetes and diabetic retinopathy risk factors.

This study aims to provide a better understanding of the ability of mobile health tools to offer glycemic control for patients with type 1 diabetes mellitus.

Data gained from research articles searched in PubMed, Ovid (Medline), and CINAHL from 2005 to 2013 focused on interventions introduced to a type 1 diabetic population. Articles were screened to identify interventions that examined mobile health tools effect on glycemic control using %A1C as a proxy. Fourteen articles were included in this study. Descriptive data, %A1C difference, and statistical significance, if available, were extracted for comparison.

Five major categories were identified across the spectrum of interventions, including “Internet,” “Mobile,” “Mobile and Internet,” “Phone,” and “Videoconference and phone.” Seven of the 14 articles reported statistically significant decreases in measured outcomes. Seven studies examine a single cohort, and 7 examined a double cohort. Eleven of the 14 authors (79%) reported success with their intervention. Twelve studies reported a decrease in %A1C values in their intervention groups.

Initial results for glycemic control through these tools appear promising, though inconclusive. Additional measures of mobile health tool efficacy should be assessed more directly. More rigorous study methods are also needed to improve the reliability of results.

The objective was to identify the presence of cardiovascular autonomic neuropathy (CAN) in a cohort of individuals with diabetes in outpatient clinics from 4 different parts of Denmark and to explore the difference between type 1 and type 2 diabetes in relation to CAN.

The DAN-Study is a Danish multicenter study focusing on diabetic autonomic neuropathy. Over a period of 12 months, 382 type 1 and 271 type 2 individuals with diabetes were tested for CAN. Patients were randomly recruited and tested during normal visits to outpatient clinics at 4 Danish hospitals. The presence of CAN was quantified by performing 3 cardiovascular reflex tests (response to standing, deep breathing, and valsalva). To describe possible associations, multivariate analysis with CAN as the dependent variable was performed.

The prevalence of CAN was higher among patients with type 2 diabetes (35%) compared to patients with type 1 diabetes (25%). Multivariate analysis revealed significant associations between CAN and different risk markers in the 2 populations. In type 1 diabetes patients CAN was associated with microalbuminuria (P < .001), macroalbuminuria (P = .011), simplex retinopathy (P < .001), proliferative retinopathy (P < .001), and peripheral neuropathy (P = .041). Among type 2 diabetes patients CAN was independently associated with high pulse pressure (P < .01), BMI (P = .006), and smoking (P = .025).

In this cross-sectional observational study CAN was independently associated with microvascular complication in type 1, whereas in type 2 CAN was associated with macrovascular risk factors.

Gastroparesis is a well-known diabetic complication. The pathogenesis is not fully understood. However, it is important to early diagnose these patients.

This study evaluated the plasma glucose response after a test meal, and gastrointestinal (GI) symptom severity in patients with clinical suspicion of diabetic gastroparesis, and assessed its usefulness to predict gastroparesis. In all, 83 subjects with insulin-treated diabetes mellitus (DM) type 1 and 2 were included; 53 subjects had gastroparesis and 30 had normal gastric emptying determined by gastric scintigraphy. GI symptom severity during the preceding 2 weeks was evaluated with a validated questionnaire. The test meal consisted of 100 g meat, 40 g pasta, 150 g carrot, and 5 g oil. The subjects ingested the meal under fasting conditions, and plasma glucose was followed during 180 minutes.

Patients with gastroparesis demonstrated a blunted plasma glucose response after a test meal versus patients with normal gastric emptying (P < .005), reflected by lower maximum increase in plasma glucose response and incremental area under the curve of the plasma glucose, but a similar time to the maximum plasma glucose level. All GI symptoms were more severe in patients with gastroparesis. GI symptom severity had the best discriminative value to identify patients with gastroparesis with an area under the receiver operating curve of 0.83 (optimal cutoff: sensitivity 87%, specificity 80%).

Patients with diabetic gastroparesis have a blunted postprandial plasma glucose response. Combining this information with the presence of GI symptoms can help clinicians identify diabetic patients with gastroparesis.

Vascular dysfunction due to hyperglycemia in individuals with diabetes is a factor contributing to distal symmetric polyneuropathy (DSPN). Reactive oxygen species reduce the bioavailability of nitric oxide (NO), a powerful vasodilator, resulting in reduced circulation and nerve ischemia. Increases in blood NO concentrations and circulation have been attributed to whole body vibration (WBV). The purpose of this study was to the determine the effects of low-frequency, low-amplitude WBV on whole blood NO concentrations and skin blood flow (SBF) in individuals with symptoms of DSPN.

Ten patients with diabetes and impaired sensory perception in the lower limbs participated in this crossover study. Each submitted to 2 treatment conditions, WBV and sham, with a 1-week washout period between. Blood draws for NO analysis and laser Doppler imager scans of SBF were performed before, immediately after, and following a 5-minute recovery of each of the treatments.

Low-frequency, low-amplitude WBV significantly increased SBF compared to the sham condition (F_2,18 = 5.82, P = .0115). Whole blood NO concentrations did not differ between the WBV and sham conditions immediately or 5 minutes after treatment (F_2,18 = 1.88, P = .1813).

These findings demonstrate that patients with diabetes respond to WBV with increased SBF compared to the sham condition. The implication is that WBV is a potential nonpharmacological therapy for neurovascular complications of diabetes.