Kovatchev, Boris

Sharing research code in an open access version-controlled repository offers significant benefits for both science as a whole and for individual researchers. In this article, we focus on this practice, which is fully aligned with the NIH’s Gold Standard Science (GSS) program as well as FAIR (findable, accessible, interoperable, reusable) and TRUST (transparency, responsibility, user focus, sustainability, technology) principles. Gold Standard Science supports open science by emphasizing transparency, reproducibility, and the use of best practices that enable others to verify and extend research. Pairing a research article’s cited data snapshot with a versioned, environment-specific code release, deposited in a companion code repository, ensures that, upon submission to a medical journal, readers and reviewers can directly verify results. An executable and updatable companion code repository complements, rather than replaces, established research data repositories. When code underlying medical research results is made openly available, then other scientists can inspect, run, and validate analyses. These activities enhance reproducibility, which is a core aim of GSS. Shared code also facilitates collaborative innovation by allowing researchers to extend the utility of the code to new datasets and applications. For researchers, code sharing can increase visibility, credibility, and citation impact. Demonstrating transparency through shared executable and updatable code builds trust with journal readers, peer reviewers, funders, and peers. Shared code in an open access repository signals adherence to high standards of scientific integrity and attracts opportunities for collaboration. A researcher who shares code receives recognition as a leader in reproducible, trustworthy research consistent with NIH’s GSS principles.

The development of automated insulin delivery systems has seen tremendous improvements from individual components to interoperable system combinations of devices and new drugs besides insulin. The components have become progressively smaller, more accurate, and more user friendly. This article summarizes the history of the artificial pancreas from the earliest concepts to fully functional systems to research into further improvements in the future. The authors include many of the developers of this technology who received research support from the National Institute of Diabetes and Digestive and Kidney Diseases at various stages to develop these systems.

The development of automated insulin delivery systems has seen tremendous improvements from individual components to interoperable system combinations of devices and new drugs besides insulin. The components have become progressively smaller, more accurate, and more user friendly. This article summarizes the history of the artificial pancreas from the earliest concepts to fully functional systems to research into further improvements in the future. The authors include many of the developers of this technology who received research support from the National Institute of Diabetes and Digestive and Kidney Diseases at various stages to develop these systems.

Background: A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data. Methods: We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low–glucose and low-glucose hypoglycemia; very high–glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation. Results: The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals. Conclusion: The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.

Objective: To analyze insulin delivery and glycemic metrics throughout the menstrual cycle for women with type 1 diabetes using closed loop control (CLC) insulin delivery. Methods: Menstruating women using a CLC system in a clinical trial were invited to record their menstrual cycles through a cycle-tracking application. Sixteen participants provided data for this secondary analysis over three or more complete cycles. Insulin delivery and continuous glucose monitoring (CGM) data were analyzed in relation to reported cycle phases. Results: Insulin delivery and CGM metrics remained consistent during cycle phases. Intraparticipant variability of CGM metrics and weight-based insulin delivery did not change through cycle phases. Conclusions: For this sample of menstruating women with type 1 diabetes using a CLC system, insulin delivery and glycemic metrics remained stable throughout menstrual cycle phases. Additional studies in this population are needed, particularly among women who report variable glycemic control during their cycles. Trial Registration: NCT03591354.

Background: Closed-loop control (CLC) has been shown to improve glucose time in range and other glucose metrics; however, randomized trials >3 months comparing CLC with sensor-augmented pump (SAP) therapy are limited. We recently reported glucose control outcomes from the 6-month international Diabetes Closed-Loop (iDCL) trial; we now report patient-reported outcomes (PROs) in this iDCL trial. Methods: Participants were randomized 2:1 to CLC ( N = 112) versus SAP ( N = 56) and completed questionnaires, including Hypoglycemia Fear Survey, Diabetes Distress Scale (DDS), Hypoglycemia Awareness, Hypoglycemia Confidence, Hyperglycemia Avoidance, and Positive Expectancies of CLC (INSPIRE) at baseline, 3, and 6 months. CLC participants also completed Diabetes Technology Expectations and Acceptance and System Usability Scale (SUS). Results: The Hypoglycemia Fear Survey Behavior subscale improved significantly after 6 months of CLC compared with SAP. DDS did not differ except for powerless subscale scores, which worsened at 3 months in SAP. Whereas Hypoglycemia Awareness and Hyperglycemia Avoidance did not differ between groups, CLC participants showed a tendency toward improved confidence in managing hypoglycemia. The INSPIRE questionnaire showed favorable scores in the CLC group for teens and parents, with a similar trend for adults. At baseline and 6 months, CLC participants had high positive expectations for the device with Diabetes Technology Acceptance and SUS showing high benefit and low burden scores. Conclusion: CLC improved some PROs compared with SAP. Participants reported high benefit and low burden with CLC. Clinical Trial Identifier: NCT03563313.

Objective: To assess the efficacy and safety of closed-loop control (CLC) insulin delivery system in adolescents and young adults with type 1 diabetes. Research Design and Methods: Prespecified subanalysis of outcomes in adolescents and young adults aged 14–24 years old with type 1 diabetes in a previously published 6-month multicenter randomized trial. Participants were randomly assigned 2:1 to CLC (Tandem Control-IQ) or sensor augmented pump (SAP, various pumps+Dexcom G6 CGM) and followed for 6 months. Results: Mean age of the 63 participants was 17 years, median type 1 diabetes duration was 7 years, and mean baseline HbA1c was 8.1%. All 63 completed the trial. Time in range (TIR) increased by 13% with CLC versus decreasing by 1% with SAP (adjusted treatment group difference = +13% [+3.1 h/day]; 95% confidence interval [CI] 9–16, P < 0.001), which largely reflected a reduction in time >180 mg/dL (adjusted difference −12% [−2.9 h/day], P < 0.001). Time <70 mg/dL decreased by 1.6% with CLC versus 0.3% with SAP (adjusted difference −0.7% [−10 min/day], 95% CI −1.0% to −0.2%, P = 0.002). CLC use averaged 89% of the time for 6 months. The mean adjusted difference in HbA1c after 6 months was 0.30% in CLC versus SAP (95% CI −0.67 to +0.08, P = 0.13). There was one diabetic ketoacidosis episode in the CLC group. Conclusions: CLC use for 6 months was substantial and associated with improved TIR and reduced hypoglycemia in adolescents and young adults with type 1 diabetes. Thus, CLC has the potential to improve glycemic outcomes in this challenging age group. The clinical trial was registered with ClinicalTrials.gov (NCT03563313).

Background: Data are limited on the need for and benefits of pump setting optimization with automated insulin delivery. We examined clinical management of a closed-loop control (CLC) system and its relationship to glycemic outcomes. Materials and Methods: We analyzed personal parameter adjustments in 168 participants in a 6-month multicenter trial of CLC with Control-IQ versus sensor-augmented pump (SAP) therapy. Preset parameters (BR = basal rates, CF = correction factors, CR = carbohydrate ratios) were optimized at randomization, 2 and 13 weeks, for safety issues, participant concerns, or initiation by participants' usual diabetes care team. Time in range (TIR 70–180 mg/dL) was compared in the week before and after parameter changes. Results: In 607 encounters for parameter changes, there were fewer adjustments for CLC than SAP (3.4 vs. 4.1/participant). Adjustments involved BR (CLC 69%, SAP 80%), CR (CLC 68%, SAP 50%), CF (CLC 44%, SAP 41%), and overnight parameters (CLC 62%, SAP 75%). TIR before and after adjustments was 71.2% and 71.3% for CLC and 61.0% and 62.9% for SAP. The highest baseline HbA 1c CLC subgroup had the largest TIR improvement (51.2% vs. 57.7%). When a CR was made more aggressive in the CLC group, postprandial time >180 mg/dL was 43.1% before the change and 36.0% after the change. The median postprandial time <70 mg/dL before making CR less aggressive was 1.8%, and after the change was 0.7%. Conclusions: No difference in TIR was detected with parameter changes overall, but they may have an effect in higher HbA 1c subgroups or following user-directed boluses, suggesting that changes may matter more in suboptimal control or during discrete periods of the day. Clinical Trials Registration number: NCT03563313.

OBJECTIVE Limited information is available about glycemic outcomes with a closed-loop control (CLC) system compared with a predictive low-glucose suspend (PLGS) system. RESEARCH DESIGN AND METHODS After 6 months of use of a CLC system in a randomized trial, 109 participants with type 1 diabetes (age range, 14–72 years; mean HbA1c, 7.1% [54 mmol/mol]) were randomly assigned to CLC (N = 54, Control-IQ) or PLGS (N = 55, Basal-IQ) groups for 3 months. The primary outcome was continuous glucose monitor (CGM)-measured time in range (TIR) for 70–180 mg/dL. Baseline CGM metrics were computed from the last 3 months of the preceding study. RESULTS All 109 participants completed the study. Mean ± SD TIR was 71.1 ± 11.2% at baseline and 67.6 ± 12.6% using intention-to-treat analysis (69.1 ± 12.2% using per-protocol analysis excluding periods of study-wide suspension of device use) over 13 weeks on CLC vs. 70.0 ± 13.6% and 60.4 ± 17.1% on PLGS (difference = 5.9%; 95% CI 3.6%, 8.3%; P &lt; 0.001). Time &gt;180 mg/dL was lower in the CLC group than PLGS group (difference = −6.0%; 95% CI −8.4%, −3.7%; P &lt; 0.001) while time &lt;54 mg/dL was similar (0.04%; 95% CI −0.05%, 0.13%; P = 0.41). HbA1c after 13 weeks was lower on CLC than PLGS (7.2% [55 mmol/mol] vs. 7.5% [56 mmol/mol], difference −0.34% [−3.7 mmol/mol]; 95% CI −0.57% [−6.2 mmol/mol], −0.11% [1.2 mmol/mol]; P = 0.0035). CONCLUSIONS Following 6 months of CLC, switching to PLGS reduced TIR and increased HbA1c toward their pre-CLC values, while hypoglycemia remained similarly reduced with both CLC and PLGS.

OBJECTIVE Assess the efficacy of inControl AP, a mobile closed-loop control (CLC) system. RESEARCH DESIGN AND METHODS This protocol, NCT02985866, is a 3-month parallel-group, multicenter, randomized unblinded trial designed to compare mobile CLC with sensor-augmented pump (SAP) therapy. Eligibility criteria were type 1 diabetes for at least 1 year, use of insulin pumps for at least 6 months, age ≥14 years, and baseline HbA1c &lt;10.5% (91 mmol/mol). The study was designed to assess two coprimary outcomes: superiority of CLC over SAP in continuous glucose monitor (CGM)–measured time below 3.9 mmol/L and noninferiority in CGM-measured time above 10 mmol/L. RESULTS Between November 2017 and May 2018, 127 participants were randomly assigned 1:1 to CLC (n = 65) versus SAP (n = 62); 125 participants completed the study. CGM time below 3.9 mmol/L was 5.0% at baseline and 2.4% during follow-up in the CLC group vs. 4.7% and 4.0%, respectively, in the SAP group (mean difference −1.7% [95% CI −2.4, −1.0]; P &lt; 0.0001 for superiority). CGM time above 10 mmol/L was 40% at baseline and 34% during follow-up in the CLC group vs. 43% and 39%, respectively, in the SAP group (mean difference −3.0% [95% CI −6.1, 0.1]; P &lt; 0.0001 for noninferiority). One severe hypoglycemic event occurred in the CLC group, which was unrelated to the study device. CONCLUSIONS In meeting its coprimary end points, superiority of CLC over SAP in CGM-measured time below 3.9 mmol/L and noninferiority in CGM-measured time above 10 mmol/L, the study has demonstrated that mobile CLC is feasible and could offer certain usability advantages over embedded systems, provided the connectivity between system components is stable.