Journal of Diabetes Science and Technology

Continuous glucose monitoring (CGM) systems are increasingly becoming essential components in type 1 diabetes mellitus (T1DM) management. Current CGM technology requires frequent calibration to ensure accurate sensor performance. The accuracy of these systems is of great importance since medical decisions are made based on monitored glucose values and trends.

In this work, we introduce a calibration strategy that is augmented with a weekly updating feature. During the life cycle of the sensor, the calibration mechanism periodically estimates the parameters of a calibration model to fit self-monitoring blood glucose (SMBG) measurements. At the end of each week of use, an optimization problem that minimizes the sum of squared residuals between past reference and predicted blood glucose values is solved remotely to identify personalized calibration parameters. The newly identified parameters are used to initialize the calibration mechanism of the following week.

The proposed method was evaluated using two sets of clinical data both consisting of 6 weeks of Dexcom G4 Platinum CGM data on 10 adults with T1DM (over 10 000 hours of CGM use), with seven SMBG data points per day measured by each subject in an unsupervised outpatient setting. Updating the calibration parameters using the history of calibration data indicated a positive trend of improving CGM performance.

Although not statistically significant, the updating framework showed a relative improvement of CGM accuracy compared to the non-updating, static calibration method. The use of information collected for longer periods is expected to improve the performance of the sensor over time.

The objective was to explore psychosocial experiences of closed loop technology for adults, children, and adolescents with type 1 diabetes and their parents taking part in two multicenter, free-living, randomized crossover home studies.

Participants using insulin pump therapy were randomized to either 12 weeks of automated closed-loop glucose control, then 12 weeks of sensor augmented insulin pump therapy (open loop), or vice versa. Closed loop was used for 24 hours by adults and overnight only by children and adolescents. Participants completed the Diabetes Technology Questionnaire (DTQ) periodically and shared their views in semistructured interviews. This analysis characterizes the impact of the technology, positive and negative aspects of living with the device, alongside participants’ expectations, hopes, and anxieties.

Participants were 32 adults, age 38.6 ± 9.6 years, 55% male, and 26 children, mean age 12 years (range 6-18 years), 54% male. DTQ results indicated moderately favorable impact of, and satisfaction with, both open and closed loop interventions, but little evidence of a comparative advantage of either. Key positive themes included perceived improved blood glucose control, improved general well-being, particularly on waking, improved sleep, reduced burden of diabetes, and visibility of data. Key negative themes included having to carry around the equipment and dislike of the pump and second cannula (ie, sensor) inserted.

Overall, participants reported a positive experience of the closed loop technology. Results are consistent with previous research with size of equipment continuing to be a problem. Progress is being made in the usability of the closed-loop system.

Closed-loop (CL) systems aims to outperform usual treatments in blood glucose control and continuous glucose monitors (CGM) are a key component in such systems. Meals represents one of the main disturbances in blood glucose control, and postprandial period (PP) is a challenging situation for both CL system and CGM accuracy.

We performed an extensive analysis of sensor’s performance by numerical accuracy and precision during PP, as well as its influence in blood glucose control under CL therapy.

During PP the mean absolute relative difference (MARD) for both sensors presented lower accuracy in the hypoglycemic range (19.4 ± 12.8%) than in other ranges (12.2 ± 8.6% in euglycemic range and 9.3 ± 9.3% in hyperglycemic range). The overall MARD was 12.1 ± 8.2%. We have also observed lower MARD for rates of change between 0 and 2 mg/dl. In CL therapy, the 10 trials with the best sensor spent less time in hypoglycemia (PG < 70 mg/dl) than the 10 trials with the worst sensors (2 ± 7 minutes vs 32 ± 38 minutes, respectively).

In terms of accuracy, our results resemble to previously reported. Furthermore, our results showed that sensors with the lowest MARD spent less time in hypoglycemic range, indicating that the performance of CL algorithm to control PP was related to sensor accuracy.

Glucose values of continuous glucose monitoring (CGM) have time delays compared with plasma glucose (PG) values. The artificial pancreas (STG-55, Nikkiso, Japan) (AP), which measures venous blood glucose directly, also has a time delay because of the long tubing lines from sampling vessel to the glucose sensor. We investigate accuracy and time delay of CGM and AP in comparison with PG values during 2-step glucose clamp study.

Seven patients with type 2 diabetes and 2 healthy volunteers were included in this study. CGM (Enlite sensor, Medtronic, Northridge, CA, USA) was attached on the day before the experiment. Hyperglycemic (200 mg/dL) clamp was performed for 90 minutes, followed by euglycemic (100 mg/dL) hyperinsulinemic (100 μU/mL) clamp for 90-120 minutes using AP. CGM sensor glucose was calibrated just before and after the clamp study. AP and CGM values were compared with PG values.

AP values were significantly lower than PG values at 5, 30 minute during hyperglycemic clamp. In comparison, CGM value at 0 minute was significantly higher, and its following values were almost significantly lower than PG values. The time delay of AP and CGM values to reach maximum glucose levels were 5.0 ± 22.3 (NS) and 28.6 ± 32.5 (P < .05) min, respectively. Mean absolute rate difference of CGM was significantly higher than AP (24.0 ± 7.6 vs 15.3 ± 4.6, P < .05) during glucose rising period (0-45 min); however, there were no significant differences during other periods.

Both CGM and AP failed to follow plasma glucose values during nonphysiologically rapid glucose rising, but indicated accurate values during physiological glucose change.

Currently, no consensus exists on a model describing endogenous glucose production (EGP) as a function of glucagon concentrations. Reliable simulations to determine the glucagon dose preventing or treating hypoglycemia or to tune a dual-hormone artificial pancreas control algorithm need a validated glucoregulatory model including the effect of glucagon.

Eight type 1 diabetes (T1D) patients each received a subcutaneous (SC) bolus of insulin on four study days to induce mild hypoglycemia followed by a SC bolus of saline or 100, 200, or 300 µg of glucagon. Blood samples were analyzed for concentrations of glucagon, insulin, and glucose. We fitted pharmacokinetic (PK) models to insulin and glucagon data using maximum likelihood and maximum a posteriori estimation methods. Similarly, we fitted a pharmacodynamic (PD) model to glucose data. The PD model included multiplicative effects of insulin and glucagon on EGP. Bias and precision of PD model test fits were assessed by mean predictive error (MPE) and mean absolute predictive error (MAPE).

Assuming constant variables in a subject across nonoutlier visits and using thresholds of ±15% MPE and 20% MAPE, we accepted at least one and at most three PD model test fits in each of the seven subjects. Thus, we successfully validated the PD model by leave-one-out cross-validation in seven out of eight T1D patients.

The PD model accurately simulates glucose excursions based on plasma insulin and glucagon concentrations. The reported PK/PD model including equations and fitted parameters allows for in silico experiments that may help improve diabetes treatment involving glucagon for prevention of hypoglycemia.

Blood glucose (BG) regulation is a long-term task for people with diabetes. In recent years, more and more researchers have attempted to achieve automated regulation of BG using automatic control algorithms, called the artificial pancreas (AP) system. In clinical practice, it is equally important to guarantee the treatment effect and reduce the treatment costs. The main motivation of this study is to reduce the cure burden.

The dynamic R-parameter economic model predictive control (R-EMPC) is chosen to regulate the delivery rates of exogenous hormones (insulin and glucagon). It uses particle swarm optimization (PSO) to optimize the economic cost function and the switching logic between insulin delivery and glucagon delivery is designed based on switching control theory.

The proposed method is first tested on the standard subject; the result is compared with the switching PID and the switching MPC. The effect of the dynamic R-parameter on improving the control performance is illustrated by comparing the results of the EMPC and the R-EMPC. Finally, the robustness tests on meal change (size and timing), hormone sensitivity (insulin and glucagon), and subject variability are performed. All results show that the proposed method can improve the control performance and reduce the economic costs.

The simulation results verify the effectiveness of the proposed algorithm on improving the tracking performance, enhancing robustness, and reducing economic costs. The method proposed in this study owns great worth in practical application.

Linear empirical dynamic models have been widely used for glucose prediction. The extension of the concept of seasonality, characteristic of other domains, is explored here for the improvement of prediction accuracy.

Twenty time series of 8-hour postprandial periods (PP) for a same 60g-carbohydrate meal were collected from a closed-loop controller validation study. A single concatenated time series was produced representing a collection of data from similar scenarios, resulting in seasonality. Variability in the resulting time series was representative of worst-case intrasubject variability. Following a leave-one-out cross-validation, seasonal and nonseasonal autoregressive integrated moving average models (SARIMA and ARIMA) were built to analyze the effect of seasonality in the model prediction accuracy. Further improvement achieved from the inclusion of insulin infusion rate as exogenous variable was also analyzed. Prediction horizons (PHs) from 30 to 300 min were considered.

SARIMA outperformed ARIMA revealing a significant role of seasonality. For a 5-h PH, average MAPE was reduced in 26.62%. Considering individual runs, the improvement ranged from 6.3% to 54.52%. In the best-performing case this reduction amounted to 29.45%. The benefit of seasonality was consistent among different PHs, although lower PHs benefited more, with MAPE reduction over 50% for PHs of 60 and 120 minutes, and over 40% for 180 min. Consideration of insulin infusion rate into the seasonal model further improved performance, with a 61.89% reduction in MAPE for 30-min PH and reductions over 20% for PHs over 180 min.

Seasonality improved model accuracy allowing for the extension of the PH significantly.

The physiologic delay in glucose diffusion from the blood to the interstitial fluid and instrumental factors contribute to the delay between changes in plasma glucose (PG) and measurements made by continuous glucose monitors (CGMs). This study compared the duration of this delay for three CGMs.

A total of 24 healthy adolescent and adult subjects with type 1 diabetes wore three CGM devices simultaneously for 48 hours: Dexcom G4 Platinum, Abbott Navigator, and Medtronic Enlite. The time delay between PG and CGM-estimated plasma glucose (CGMG) was estimated by comparing time-shifted CGMG with reference PG taken every 15 minutes.

The delay estimated by our approach was larger for the Navigator than for the G4 Platinum in adolescents (7.7 ± 1.1 versus 5.6 ± 0.9 min, P = .0396) and adults (10.9 ± 1.1 versus 8.1 ± 0.7 min, P = .0107). The delay was nominally longer for the Navigator than for the Enlite in both the adolescent (7.7 ± 1.1 versus 4.3 ± 1.0 min, P = .0728) and adult (10.9 ± 1.1 versus 8.3 ± 0.9 min, P = .111) populations, but these differences were not statistically significant. There was no difference in the delay between G4 Platinum and Enlite. Adolescents had shorter delays than adults for all three devices. There was a significant correlation between longer delay and increasing age for the G4 Platinum and Navigator.

There are differences in the estimated PG to CGMG time delays between CGM devices in the same subjects. The delay between PG and CGMG is smaller for adolescents than for adults. The PG–to–CGMG time delay is influenced by both instrument and host factors.

The objective was to determine the effectiveness of real-time continuous glucose monitoring (CGM) in adults ≥ 60 years of age with type 1 (T1D) or type 2 (T2D) diabetes using multiple daily insulin injections (MDI).

A multicenter, randomized trial was conducted in the United States and Canada in which 116 individuals ≥60 years (mean 67 ± 5 years) with T1D (n = 34) or T2D (n = 82) using MDI therapy were randomly assigned to either CGM (Dexcom™ G4 Platinum CGM System® with software 505; n = 63) or continued management with self-monitoring blood glucose (SMBG; n = 53). Median diabetes duration was 21 (14, 30) years and mean baseline HbA1c was 8.5 ± 0.6%. The primary outcome, HbA1c at 24 weeks, was obtained for 114 (98%) participants.

HbA1c reduction from baseline to 24 weeks was greater in the CGM group than Control group (−0.9 ± 0.7% versus −0.5 ± 0.7%, adjusted difference in mean change was −0.4 ± 0.1%, P < .001). CGM-measured time >250 mg/dL (P = .006) and glycemic variability (P = .02) were lower in the CGM group. Among the 61 in the CGM group completing the trial, 97% used CGM ≥ 6 days/week in month 6. There were no severe hypoglycemic or diabetic ketoacidosis events in either group.

In adults ≥ 60 years of age with T1D and T2D using MDI, CGM use was high and associated with improved HbA1c and reduced glycemic variability. Therefore, CGM should be considered for older adults with diabetes using MDI.

Patients with diabetes, especially pediatric ones, sometimes use continuous glucose monitoring (CGM) sensor in different positions from the approved ones. Here we compare the accuracy of Dexcom® G5 CGM sensor in three different sites: abdomen, gluteus (both approved) and arm (off-label).

Thirty youths, 5-9 years old, with type 1 diabetes (T1D) wore the sensor during a clinical trial where frequent self-monitoring of blood glucose (SMBG) measurements were obtained. Sensor was inserted in different sites according to the patient habit. Accuracy metrics include absolute relative difference (ARD) and absolute difference (AD) of CGM with respect to SMBG. The three sites were compared with ANOVA. If the test detected a difference, an additional pair-wise comparison was performed.

Overall, no accuracy difference was detected: the mean ARD was 13.3% (SD = 13.5%) for abdomen, 13.4% (12.9%) for arm and 12.9% (20.2%) for gluteus (P value = .83); the mean AD was 17.0 mg/dl (17.2 mg/dl) for abdomen, 17.2 mg/dl (17.1 mg/dl) for arm and 18.3 mg/dl (18.5 mg/dl) for gluteus (P value = .30). In hypo- and euglycemia ARD (P value = .87 and .15, respectively), and AD (P value = .68 and .37, respectively) were not statistically different. At variance, in hyperglycemia, a significant difference was detected between the two approved sites, abdomen and gluteus (ΔARD = −2.2% [CI = −4.2%, −0.1%], P value = .04), whereas the comparisons with the off-label location, arm-abdomen, and arm-gluteus were not significant.

These results suggest that the accuracy of the sensor placed on the arm was not significantly different with respect to the two approved insertion sites (abdomen and gluteus). Larger, randomized trials are needed to draw final conclusions.

A key approach in enabling people with diabetes to better manage their condition is through self-monitoring of blood glucose (SMBG). Any functional SMBG system should demonstrate clinical accuracy across a broad glucose range and be insensitive to hematocrit. Furthermore, it should be incumbent on the manufacturer to demonstrate that their product continues to meet clinical accuracy claims during product lifetime.

Test strips from a globally distributed SMBG product were sampled from randomly selected production batches as part of the manufacturer’s routine product evaluation process. Clinical accuracy was assessed within diabetes patients at 3 clinic sites against a standard reference method and evaluated against system accuracy in accordance with the ISO 15197:2015 standard (unchanged from ISO 15197:2013 in terms of performance specifications). Data were collected over 7 years (2010-2016) and comprised 73,600 individual glucose results. Overall clinic performance was assessed, as was accuracy at low and high glucose levels and extremes of hematocrit.

Across the 7-year surveillance period, overall test strip clinical accuracy was 97.8% versus the 95% ISO-defined minimum criterion with by-year values of 97.0-98.6%. Accuracy at the lowest (≤50 mg/dL) and highest (>400 mg/dL) ranges of glucose was 97.0% and 98.3% respectively. Within these low/high blood glucose subpopulations, accuracy at the lower and upper first percentile hematocrit ranges, was 98.9%, and 97.1% respectively.

This 7-year surveillance program showed the test strips to have excellent clinical accuracy at the outer ranges of subject blood glucose and hematocrit, based on assessment against the ISO 15197:2015 clinical accuracy criterion.

Cardiac autonomic reflex tests (CARTs) are time consuming and require patient cooperation for detecting cardiac autonomic neuropathy (CAN). Heart rate variability (HRV) analysis requires less patient cooperation and is quicker to complete. However the reliability of HRV results as a clinical tool, with respect to length of recording and accuracy of diagnosis is inconclusive. The current study investigated the reproducibility associated with varying length of recording for early CAN (eCAN) assessment.

Participants were 68 males, 72 females with average age of 55 for controls and 63 for early CAN. Inclusion criteria were that participants were medication free and presented with no comorbidities. ECGs of control and eCAN were recorded and heart rate changes analyzed with the fast Fourier transform (FFT) and Lomb-Scargle periodogram (LSP). Ten-second to 5-minute recordings were extracted from a 15-minute lead-II ECG and accuracy in assessment of eCAN determined.

The eCAN group was older (P < .001) and systolic blood pressure was higher (P < .01). HDL-cholesterol was also higher in the eCAN group (P < .05). HRV analysis showed that both FFT and LSP results were significantly different between eCAN and control down to a 10-second ECG length for low frequency (LSP: P = .013, FFT: P = .024) and high frequency (HF-LSP: P = .002, FFT: P = .002) power. eCAN assessment was optimal down to 90-second recordings with a sensitivity of 100% and specificity of 29.49%.

HRV is suitable for clinical practice from ECG recordings of more than 90 seconds with high accuracy and repeatability within a session for each participant.

We propose a methodology to analyze complex real-life glucose data in insulin pump users.

Patients with type 1 diabetes (T1D) on insulin pumps were recruited from an academic endocrinology practice. Glucose data, insulin bolus (IB) amounts, and self-reported alcohol consumption and exercise events were collected for 30 days. Rules were developed to retrospectively compare IB recommendations from the insulin pump bolus calculator (IPBC) against recommendations from a proposed decision aid (PDA) and for assessing the PDA’s recommendation for exercise and alcohol.

Data from 15 participants were analyzed. When considering instances where glucose was below target, the PDA recommended a smaller dose in 14%, but a larger dose in 13% and an equivalent IB in 73%. For glucose levels at target, the PDA suggested an equivalent IB in 58% compared to the subject’s IPBC, but higher doses in 20% and lower in 22%. In events where postprandial glucose was higher than target, the PDA suggested higher doses in 25%, lower doses in 13%, and equivalent doses in 62%. In 64% of all alcohol events the PDA would have provided appropriate advice. In 75% of exercise events, the PDA appropriately advised an IB, a carbohydrate snack, or neither.

This study provides a methodology to systematically analyze real-life data generated by insulin pumps and allowed a preliminary analysis of the performance of the PDA for insulin dosing. Further testing of the methodological approach in a broader diabetes population and prospective testing of the PDA are needed.

Patients with diabetes rely on blood glucose (BG) monitoring devices to manage their condition. As some self-monitoring devices are becoming more and more accurate, it becomes critical to understand the relationship between system accuracy and clinical outcomes, and the potential benefits of analytical accuracy.

We conducted a 30-day in-silico study in type 1 diabetes mellitus (T1DM) patients using continuous subcutaneous insulin infusion (CSII) therapy and a variety of BG meters, using the FDA-approved University of Virginia (UVA)/Padova Type 1 Simulator. We used simulated meter models derived from the published characteristics of 43 commercial meters. By controlling random events in each parallel run, we isolated the differences in clinical performance that are directly associated with the meter characteristics.

A meter’s systematic bias has a significant and inverse effect on HbA1c (P < .01), while also affecting the number of severe hypoglycemia events. On the other hand, error, defined as the fraction of measurements beyond 5% of the true value, is a predictor of severe hypoglycemia events (P < .01), but in the absence of bias has a nonsignificant effect on average glycemia (HbA1c). Both bias and error have significant effects on total daily insulin (TDI) and the number of necessary glucose measurements per day (P < .01). Furthermore, these relationships can be accurately modeled using linear regression on meter bias and error.

Two components of meter accuracy, bias and error, clearly affect clinical outcomes. While error has little effect on HbA1c, it tends to increase episodes of severe hypoglycemia. Meter bias has significant effects on all considered metrics: a positive systemic bias will reduce HbA1c, but increase the number of severe hypoglycemia attacks, TDI use, and number of fingersticks per day.

Standard management of type 1 diabetes (T1D) relies on blood glucose monitoring based on a range of technologies from self-monitoring of blood glucose (BGM) to continuous glucose monitoring (CGM). Even as CGM technology matures, patients utilize BGM for calibration and dosing. The question of how the accuracy of both technologies interact is still not well understood.

We use a recently developed data-driven simulation approach to characterize the relationship between CGM and BGM accuracy especially how BGM accuracy impacts CGM performance in four different use cases with increasing levels of reliance on twice daily calibrated CGM. Simulations are used to estimate clinical outcomes and isolate CGM and BGM accuracy characteristics that drive performance.

Our results indicate that meter (BGM) accuracy, and more specifically systematic positive or negative bias, has a significant effect on clinical performance (HbA1c and severe hypoglycemia events) in all use-cases generated for twice daily calibrated CGMs. Moreover, CGM sensor accuracy can amplify or mitigate, but not eliminate these effects.

As a system, BGM and CGM and their mode of use (use-case) interact to determine clinical outcomes. Clinical outcomes (eg, HbA1c, severe hypoglycemia, time in range) can be closely approximated by linear relationships with two BGM accuracy characteristics, namely error and bias. In turn, the coefficients of this linear relationship are determined by the use-case and by CGM accuracy (MARD).

Effective glucose control in the intensive care unit (ICU) setting has the potential to decrease morbidity and mortality rates and thereby decrease health care expenditures. To evaluate what constitutes effective glucose control, typically several metrics are reported, including time in range, time in mild and severe hypoglycemia, coefficient of variation, and others. To date, there is no one metric that combines all of these individual metrics to give a number indicative of overall performance. We proposed a composite metric that combines 5 commonly reported metrics, and we used this composite metric to compare 6 glucose controllers.

We evaluated the following controllers: Ideal Medical Technologies (IMT) artificial-intelligence-based controller, Yale protocol, Glucommander, Wintergerst et al PID controller, GRIP, and NICE-SUGAR. We evaluated each controller across 80 simulated patients, 4 clinically relevant exogenous dextrose infusions, and one nonclinical infusion as a test of the controller’s ability to handle difficult situations. This gave a total of 2400 5-day simulations, and 585 604 individual glucose values for analysis. We used a random walk sensor error model that gave a 10% MARD. For each controller, we calculated severe hypoglycemia (<40 mg/dL), mild hypoglycemia (40-69 mg/dL), normoglycemia (70-140 mg/dL), hyperglycemia (>140 mg/dL), and coefficient of variation (CV), as well as our novel controller metric.

For the controllers tested, we achieved the following median values for our novel controller scoring metric: IMT: 88.1, YALE: 46.7, GLUC: 47.2, PID: 50, GRIP: 48.2, NICE: 46.4.

The novel scoring metric employed in this study shows promise as a means for evaluating new and existing ICU-based glucose controllers, and it could be used in the future to compare results of glucose control studies in critical care. The IMT AI-based glucose controller demonstrated the most consistent performance results based on this new metric.

Self-monitoring of blood glucose (SMBG) is a key pillar of personal diabetes management. The objective of this observational study was to analyze diabetes self-management (DSM) and glycemic outcomes before and during system implementation in real-life settings of a blood glucose meter system with a color-coded display of glucose levels, which helps identify out-of-range levels.

A total of 193 insulin-treated diabetes patients (11% T1DM; 55% male, age 60 ± 4 years, mean diabetes duration 14 ± 9 years, HbA1c 8.68 ± 1.2%) were enrolled into the study. Both the Diabetes Self-Management Questionnaire (DSMQ) and glycemic control were analyzed at baseline and 3 and 6 months after study initiation.

DSMQ general perception improved significantly by the end of the study period (“Sum Scale,” P < .05). Moreover, after 6 months patient’s attitudes on self-care (Q16, P = .0046) and nutrition (“Dietary Control,” P = .004) showed significant improvements. Use of the blood glucose meter resulted in improved glycemic control, as shown by mean HbA1c levels, which decreased from 8.68 ± 1.2% at baseline to 8.13 ± 1.02% after 3 months (P < .0001) and to 7.9 ± 1.1% at 6 months (P < .0001). Both patients and diabetes educators agreed in the advantages of the color-coded indicator and on its helpfulness in assisting patients on their diabetes management, as drawn from the results of the self-reported satisfaction questionnaire.

This real-world study demonstrates that SMBG implemented via this new blood glucose meter not only leads to an improvement in metabolic control, but also is associated with a significant improvement in diabetes management.