Safety and Performance of the Omnipod Hybrid Closed-Loop System in Adults, Adolescents, and Children with Type 1 Diabetes Over 5 Days Under Free-Living Conditions.

Background: The objective of this study was to assess the safety and performance of the Omnipod® personalized model predictive control (MPC) algorithm in adults, adolescents, and children aged ≥6 years with type 1 diabetes (T1D) under free-living conditions using an investigational device. Materials and Methods: A 96-h hybrid closed-loop (HCL) study was conducted in a supervised hotel/rental home setting following a 7-day outpatient standard therapy (ST) phase. Eligible participants were aged 6-65 years with A1C <10.0% using insulin pump therapy or multiple daily injections. Meals during HCL were unrestricted, with boluses administered per usual routine. There was daily physical activity. The primary endpoints were percentage of time with sensor glucose <70 and ≥250 mg/dL. Results: Participants were 11 adults, 10 adolescents, and 15 children aged (mean ± standard deviation) 28.8 ± 7.9, 14.3 ± 1.3, and 9.9 ± 1.0 years, respectively. Percentage time ≥250 mg/dL during HCL was 4.5% ± 4.2%, 3.5% ± 5.0%, and 8.6% ± 8.8% per respective age group, a 1.6-, 3.4-, and 2.0-fold reduction compared to ST (P = 0.1, P = 0.02, and P = 0.03). Percentage time <70 mg/dL during HCL was 1.9% ± 1.3%, 2.5% ± 2.0%, and 2.2% ± 1.9%, a statistically significant decrease in adults when compared to ST (P = 0.005, P = 0.3, and P = 0.3). Percentage time 70-180 mg/dL increased during HCL compared to ST, reaching significance for adolescents and children: HCL 73.7% ± 7.5% vs. ST 68.0% ± 15.6% for adults (P = 0.08), HCL 79.0% ± 12.6% vs. ST 60.6% ± 13.4% for adolescents (P = 0.01), and HCL 69.2% ± 13.5% vs. ST 54.9% ± 12.9% for children (P = 0.003). Conclusions: The Omnipod personalized MPC algorithm was safe and performed well over 5 days and 4 nights of use by a cohort of participants ranging from youth aged ≥6 years to adults with T1D under supervised free-living conditions with challenges, including daily physical activity and unrestricted meals.

Performance of Omnipod Personalized Model Predictive Control Algorithm with Moderate Intensity Exercise in Adults with Type 1 Diabetes.

Background: The objective of this study was to assess the safety and performance of the Omnipod® personalized model predictive control (MPC) algorithm with variable glucose setpoints and moderate intensity exercise using an investigational device in adults with type 1 diabetes (T1D). Materials and Methods: A supervised 54-h hybrid closed-loop (HCL) study was conducted in a hotel setting after a 7-day outpatient standard treatment phase. Adults aged 18-65 years with T1D and HbA1c between 6.0% and 10.0% were eligible. Subjects completed two moderate intensity exercise sessions of >30 min duration on consecutive days: the first with the glucose set point increased from 130 to 150 mg/dL and the second with a temporary basal rate of 50%, both started 90 min pre-exercise. Primary endpoints were percentage time in hypoglycemia <70 mg/dL and hyperglycemia ≥250 mg/dL. Results: Twelve subjects participated in the study, with (mean ± standard deviation) age 36.5 ± 14.4 years, diabetes duration 21.7 ± 15.7 years, HbA1c 7.6% ± 1.1%, and total daily dose 0.60 ± 0.22 U/kg. Outcomes for the 54-h HCL period were mean glucose: 136 ± 14 mg/dL, percentage time <70 mg/dL: 1.4% ± 1.3%, 70-180 mg/dL: 85.1% ± 9.3%, and ≥250 mg/dL: 1.8% ± 2.4%. In the 12-h period after exercise start, percentage time <70 mg/dL was 1.4% ± 2.7% with the raised glucose set point and 1.6% ± 3.0% with reduced basal rate. The percentage time <70 mg/dL overnight was 0% ± 0% on both study nights. Conclusions: The Omnipod personalized MPC algorithm performed well and was safe during day and night use in response to variable glucose set points and with temporarily raised glucose set point or reduced basal rate 90 min in advance of moderate intensity exercise in adults with T1D.

Glycemic Variability Percentage: A Novel Method for Assessing Glycemic Variability from Continuous Glucose Monitor Data.

BACKGROUND: High levels of glycemic variability are still observed in most patients with diabetes with severe insulin deficiency. Glycemic variability may be an important risk factor for acute and chronic complications. Despite its clinical importance, there is no consensus on the optimum method for characterizing glycemic variability. METHOD: We developed a simple new metric, the glycemic variability percentage (GVP), to assess glycemic variability by analyzing the length of the continuous glucose monitoring (CGM) temporal trace normalized to the duration under evaluation. The GVP is similar to other recently proposed glycemic variability metrics, the distance traveled, and the mean absolute glucose (MAG) change. We compared results from distance traveled, MAG, GVP, standard deviation (SD), and coefficient of variation (CV) applied to simulated CGM traces accentuating the difference between amplitude and frequency of oscillations. The GVP metric was also applied to data from clinical studies for the Dexcom G4 Platinum CGM in subjects without diabetes, with type 2 diabetes, and with type 1 diabetes (adults, adolescents, and children). RESULTS: In contrast to other metrics, such as CV and SD, the distance traveled, MAG, and GVP all captured both the amplitude and frequency of glucose oscillations. The GVP metric was also able to differentiate between diabetic and nondiabetic subjects and between subjects with diabetes with low, moderate, and high glycemic variability based on interquartile analysis. CONCLUSION: A new metric for the assessment of glycemic variability has been shown to capture glycemic variability due to fluctuations in both the amplitude and frequency of glucose given by CGM data.

Safety and Feasibility of the OmniPod Hybrid Closed-Loop System in Adult, Adolescent, and Pediatric Patients with Type 1 Diabetes Using a Personalized Model Predictive Control Algorithm.

BACKGROUND: The safety and feasibility of the OmniPod personalized model predictive control (MPC) algorithm in adult, adolescent, and pediatric patients with type 1 diabetes were investigated. METHODS: This multicenter, observational trial included a 1-week outpatient sensor-augmented pump open-loop phase and a 36-h inpatient hybrid closed-loop (HCL) phase with announced meals ranging from 30 to 90 g of carbohydrates and limited physical activity. Patients aged 6-65 years with HbA1c between 6.0% and 10.0% were eligible. The investigational system included a modified version of OmniPod, the Dexcom G4 505 Share® AP System, and the personalized MPC algorithm running on a tablet computer. Primary endpoints included sensor glucose percentage of time in hypoglycemia <70 mg/dL and hyperglycemia >250 mg/dL. Additional glycemic targets were assessed. RESULTS: The percentage of time <70 mg/dL during the 36-h HCL phase was mean (standard deviation): 0.7 (1.7) in adults receiving 80% meal bolus (n = 24), and 0.7 (1.2) in adults (n = 10), 2.0 (2.4) in adolescents (n = 12), and 2.0 (2.6) in pediatrics (n = 12) receiving 100% meal bolus. The overall hypoglycemia rate was 0.49 events/24 h. The percentage of time >250 mg/dL was 8.0 (7.5), 3.6 (3.7), 4.9 (6.3), and 6.7 (5.6) in the study groups, respectively. Percentage of time in the target range of 70-180 mg/dL was 69.5 (14.4), 73.0 (15.0), 72.6 (15.5), and 70.1 (12.3), respectively. CONCLUSIONS: The OmniPod personalized MPC algorithm performed well and was safe during day and night use in adult, adolescent, and pediatric patients with type 1 diabetes. Longer term studies will assess the safety and performance of the algorithm under free living conditions with extended use.

Performance of the Omnipod Personalized Model Predictive Control Algorithm with Meal Bolus Challenges in Adults with Type 1 Diabetes.

BACKGROUND: This study assessed the safety and performance of the Omnipod® personalized model predictive control (MPC) algorithm using an investigational device in adults with type 1 diabetes in response to overestimated and missed meal boluses and extended boluses for high-fat meals. MATERIALS AND METHODS: A supervised 54-h hybrid closed-loop (HCL) study was conducted in a hotel setting after a 7-day outpatient open-loop run-in phase. Adults aged 18-65 years with type 1 diabetes and HbA1c 6.0%-10.0% were eligible. Primary endpoints were percentage time in hypoglycemia <70 mg/dL and hyperglycemia ≥250 mg/dL. Glycemic responses for 4 h to a 130% overestimated bolus and a missed meal bolus were compared with a 100% bolus for identical meals, respectively. The 12-h postprandial responses to a high-fat meal were compared using either a standard or extended bolus. RESULTS: Twelve subjects participated in the study, with (mean ± standard deviation): age 35.4 ± 14.1 years, diabetes duration 16.5 ± 9.3 years, HbA1c 7.7 ± 0.9%, and total daily dose 0.58 ± 0.19 U/kg. Outcomes for the 54-h HCL period were mean glucose 153 ± 15 mg/dL, percentage time <70 mg/dL [median (interquartile range)]: 0.0% (0.0-1.2%), 70-180 mg/dL: 76.1% ± 8.0%, and ≥250 mg/dL: 4.5% ± 3.6%. After both the 100% and 130% boluses, postprandial percentage time <70 mg/dL was 0.0% (0.0-0.0%) (P = 0.50). After the 100% and missed boluses, postprandial percentage time ≥250 mg/dL was 0.2% ± 0.6% and 10.3% ± 16.5%, respectively (P = 0.06). Postprandial percentages time ≥250 mg/dL and <70 mg/dL were similar with standard or extended boluses for a high-fat meal. CONCLUSIONS: The Omnipod personalized MPC algorithm performed well and was safe during day and night use in response to overestimated, missed, and extended meal boluses in adults with type 1 diabetes.

Application of Glycemic Variability Percentage: Implications for Continuous Glucose Monitor Utilization and Analysis of Artificial Pancreas Data.

BACKGROUND: The problem of glycemic variability has been widely acknowledged in patients with diabetes with severe insulin deficiency. In a companion article, we proposed a novel metric, the glycemic variability percentage (GVP), for assessing glycemic variability that accounts for both the amplitude and frequency of glycemic fluctuations. METHOD: We applied the new metric, the GVP, to a previously reported case of a subject using an earlier generation continuous glucose monitoring (CGM) device, in which successive periods of use were associated with an apparent decrease in glycemic variability. Results were compared with histogram distributions for the rate of change of glucose as well. The GVP was also applied to data from a published study of a bihormonal artificial pancreas system comparing results from open loop and closed loop in adolescents and in adults. RESULTS: The GVP was able to quantify the changes in glycemic variability during successive periods of CGM use. Application of the GVP to a published study of a bihormonal artificial pancreas found an increase in glycemic variability compared with other accepted metrics which suggested a decrease in glycemic variability. CONCLUSION: The GVP may be a clinically useful tool in characterizing the change in glycemic variability in subjects using CGM devices. Compared with metrics, such as the standard deviation, that focus solely on the amplitude of oscillations, the GVP, which measures both frequency and amplitude, may also be a more useful tool in assessing the true level of glycemic variability in artificial pancreas studies.

A Simple Composite Metric for the Assessment of Glycemic Status from Continuous Glucose Monitoring Data: Implications for Clinical Practice and the Artificial Pancreas.

BACKGROUND: The potential clinical benefits of continuous glucose monitoring (CGM) have been recognized for many years, but CGM is used by a small fraction of patients with diabetes. One obstacle to greater use of the technology is the lack of simplified tools for assessing glycemic control from CGM data without complicated visual displays of data. METHODS: We developed a simple new metric, the personal glycemic state (PGS), to assess glycemic control solely from continuous glucose monitoring data. PGS is a composite index that assesses four domains of glycemic control: mean glucose, glycemic variability, time in range and frequency and severity of hypoglycemia. The metric was applied to data from six clinical studies for the G4 Platinum continuous glucose monitoring system (Dexcom, San Diego, CA). The PGS was also applied to data from a study of artificial pancreas comparing results from open loop and closed loop in adolescents and in adults. RESULTS: The new metric for glycemic control, PGS, was able to characterize the quality of glycemic control in a wide range of study subjects with various mean glucose, minimal, moderate, and excessive glycemic variability and subjects on open loop versus closed loop control. CONCLUSION: A new composite metric for the assessment of glycemic control based on CGM data has been defined for use in assessing glycemic control in clinical practice and research settings. The new metric may help rapidly identify problems in glycemic control and may assist with optimizing diabetes therapy during time-constrained physician office visits.

Are Systematic Reviews and Meta-Analyses Appropriate Tools for Assessing Evolving Medical Device Technologies?

Systematic reviews and meta-analyses (SRMAs) provide unique insights into comparative effectiveness of diabetes treatments. However, use of these analyses may be inappropriate for assessing the value and utility of technologies that involve significant behavioral interventions and encompass rapidly evolving technologies such as real-time continuous glucose monitoring (RT-CGM). The rapid evolution of RT-CGM, compared with the time required for publication of clinical studies used in SRMAs, may preclude differentiation between past and current generations of devices. In addition, the effect of performance and usability differences between the various commercial devices on possible clinical outcomes associated with the devices are often not clearly discussed, and many of the RT-CGM studies assessed in SRMAs do not provide adequate information regarding whether and/or to what degree study subjects and clinicians were trained to use the RT-CGM and utilize the data to adjust therapy. Although numerous clinical studies have shown that the glycemic benefit of RT-CGM is related to the frequency and duration of use, a disproportionate number of RT-CGM studies included in recent SRMAs are based on the results of the intention-to-treat analyses and do not consider this fundamental behavioral component in their conclusions. Given these limitations, the generalizability of SRMA conclusions may be limited, and findings from these reports may significantly underestimate the potential glycemic benefit of current and future devices, posing challenges for coverage and reimbursement. We reviewed the potential limitations of the recent Cochrane Collaboration report on CGM, focusing on the 12 studies that assessed RT-CGM use in adults, children/adolescents or both.

Hypoglycemic Accuracy and Improved Low Glucose Alerts of the Latest Dexcom G4 Platinum Continuous Glucose Monitoring System.

OBJECTIVE: Accuracy of continuous glucose monitoring (CGM) devices in hypoglycemia has been a widely reported shortcoming of this technology. We report the accuracy in hypoglycemia of a new version of the Dexcom (San Diego, CA) G4 Platinum CGM system (software 505) and present results regarding the optimum setting of CGM hypoglycemic alerts. MATERIALS AND METHODS: CGM values were compared with YSI analyzer (YSI Life Sciences, Yellow Springs, OH) measurements every 15 min. We reviewed the accuracy of the CGM system in the hypoglycemic range using standard metrics. We analyzed the time required for the CGM system to detect biochemical hypoglycemia (70 mg/dL) compared with the YSI with alert settings at 70 mg/dL and 80 mg/dL. We also analyzed the time between the YSI value crossing 55 mg/dL, defined as the threshold for cognitive impairment due to hypoglycemia, and when the CGM system alerted for hypoglycemia. RESULTS: The mean absolute difference for a glucose level of less than 70 mg/dL was 6 mg/dL. Ninety-six percent of CGM values were within 20 mg/dL of the YSI values between 40 and 80 mg/dL. When the CGM hypoglycemic alert was set at 80 mg/dL, the device provided an alert for biochemical hypoglycemia within 10 min in 95% of instances and at least a 10-min advance warning before the cognitive impairment threshold in 91% of instances in the study. CONCLUSIONS: Use of an 80 mg/dL threshold setting for hypoglycemic alerts on the G4 Platinum (software 505) may provide patients with timely warning of hypoglycemia before the onset of cognitive impairment, enabling them to treat themselves for hypoglycemia with fast-acting carbohydrates and prevent neuroglycopenia associated with very low glucose levels.

Numerical simulation of the effect of rate of change of glucose on measurement error of continuous glucose monitors.

BACKGROUND: A 5-day in-patient study designed to assess the accuracy of the FreeStyle Navigator Continuous Glucose Monitoring System revealed that the level of accuracy of the continuous sensor measurements was dependent on the rate of glucose change. When the absolute rate of change was less than 1 mg*dl(-1)*min(-1) (75% of the time), the median absolute relative difference (ARD) was 8.5%, with 85% of all points falling within the A zone of the Clarke error grid. When the absolute rate of change was greater than 2 mg*dl(-1)*min(-1) (8% of the time), the median ARD was 17.5%, with 59% of all points falling within the Clarke A zone. METHOD: Numerical simulations were performed to investigate effects of the rate of change of glucose on sensor measurement error. This approach enabled physiologically relevant distributions of glucose values to be reordered to explore the effect of different glucose rate-of-change distributions on apparent sensor accuracy. RESULTS: The physiological lag between blood and interstitial fluid glucose levels is sufficient to account for the observed difference in sensor accuracy between periods of stable glucose and periods of rapidly changing glucose. CONCLUSIONS: The role of physiological lag on the apparent decrease in sensor accuracy at high glucose rates of change has implications for clinical study design, regulatory review of continuous glucose sensors, and development of performance standards for this new technology. This work demonstrates the difficulty in comparing accuracy measures between different clinical studies and highlights the need for studies to include both relevant glucose distributions and relevant glucose rate-of-change distributions.

Accuracy of the 5-day FreeStyle Navigator Continuous Glucose Monitoring System: comparison with frequent laboratory reference measurements.

OBJECTIVE: The purpose of this study was to compare the accuracy of measurements of glucose in interstitial fluid made with the FreeStyle Navigator Continuous Glucose Monitoring System with Yellow Springs Instrument laboratory reference measurements of venous blood glucose. RESEARCH DESIGN AND METHODS: Fifty-eight subjects with type 1 diabetes, aged 18-64 years, were enrolled in a multicenter, prospective, single-arm study. Each subject wore two sensors simultaneously, which were calibrated with capillary fingerstick measurements at 10, 12, 24, and 72 h after insertion. Measurements from the FreeStyle Navigator system were collected at 1-min intervals and compared with venous measurements taken once every 15 min for 50 h over the 5-day period of sensor wear in an in-patient clinical research center. Periods of high rates of change of glucose were induced by insulin and glucose challenges. RESULTS: Comparison of the FreeStyle Navigator measurements with the laboratory reference method (n = 20,362) gave mean and median absolute relative differences (ARDs) of 12.8 and 9.3%, respectively. The percentage in the clinically accurate Clarke error grid A zone was 81.7% and that in the in the benign error B zone was 16.7%. During low rates of change (< +/-1 mg x dl(-1) x min(-1)), the percentage in the A zone was higher (84.9%) and the mean and median ARDs were lower (11.7 and 8.5%, respectively). CONCLUSIONS: Measurements with the FreeStyle Navigator system were found to be consistent and accurate compared with venous measurements made using a laboratory reference method over 5 days of sensor wear (82.5% in the A zone on day 1 and 80.9% on day 5).

Rhenium bipyridine complexes for the recognition of glucose.

Bipyridine ligands containing pendant methyl, amino, and amino-boronic acid groups were synthesized. Coordination complexes of these ligands with rhenium were prepared straightforwardly and in good yield. The fluorescence behavior of the Re complexes was studied as a function of pH and exposure to various concentrations of glucose. The methyl bipyridine complex showed no change in fluorescence with pH, the amino derivative showed a rapid decrease from low pH to neutral, and the amino-boronate derivative showed little change from pH 4 to 10. Fluorescence quenching was observed at high pH as expected on the basis of a photoinduced electron transfer (PET) signaling mechanism. This behavior can be explained on the basis of the first oxidation and reduction potentials of these complexes. Glucose testing showed a significant dependence on the solvent system used. In pure methanol, the rhenium boronate complex exhibited a 55% fluorescence intensity increase upon increasing glucose concentration from 0 to 400 mg/dL. However, in 50 vol % methanol/phosphate buffered saline, none of the complexes showed significant response in the glucose range of physiological interest.