Rayhan Lal, United States of America
Stanford Internal Medicine & PediatricsModerator of 1 Session
Presenter of 3 Presentations
COMPARING DIY FULL CLOSED-LOOP PERFORMANCE IN PIGS WITH STREPTOZOCIN-INDUCED DIABETES
Abstract
Background and Aims
Do-It-Yourself (DIY) algorithms for closed-loop insulin delivery are increasingly popular but infrequently studied in humans, outside of observational studies, due to lack of regulatory approval. We therefore conducted studies in pigs comparing AndroidAPS and Loop without meal announcement, leveraging faster insulin pharmacokinetics inherent to swine.
Methods
Pigs with streptozocin-induced diabetes were started on AndroidAPS running oref1 (with super-microbolus enabled) and Loop (with integral retrospective correction enabled). Insulin dosing including basal rate, insulin-to-carbohydrate ratio (ICR) and insulin sensitivity factor (ISF) were determined clinically prior to closed-loop initiation. Insulin pharmacokinetics were derived by ELISA and observation of glucose dynamics. Basal rate testing was conducted overnight without insulin-on-board (IOB) or carbs-on-board (COB) and rates were titrated to maintain glucose. ISF was calculated by administering 1 unit insulin under hyperglycemic conditions with no IOB or COB. ICR was calculated and then titrated such that post-meal blood sugar matched pre-meal.
Results
6 pigs were started on AndroidAPS followed by Loop. Insulin pharmacokinetics are more rapid in pigs with peak serum concentrations within 20-25 minutes and near complete absorption by 2 hours, modeled in both systems. In total, there were 23 days of AndroidAPS and 18 days of Loop data. Time-in-Range (70-180mg/dL) was significantly greater (p < 0.001) with AndroidAPS (63.7 ± 13.4%) versus Loop (40.5 ± 17.2%).
Conclusions
For unannounced meals, Time-in-Range was greater with AndroidAPS than with Loop. oref1 with super-microbolus is designed for unannounced meals, whereas Loop is a model predictive controller with short-term adaptation more dependent on meal data.
EFFICACY OF REAL-TIME MEAL DETECTION AND REMINDERS ON APPLE WATCH
Abstract
Background and Aims
Klue uses an Apple watch to detect hand motions indicative of eating or drinking. We hypothesized that real-time meal reminders delivered by Klue could decrease missed meal boluses and hemoglobin A1c (HbA1c) in adolescents and young adults with diabetes and 4 missed meal boluses in the previous 2 weeks.
Methods
This was a randomized, crossover, unmasked clinical study. Participants using continuous glucose monitor (CGM) with an insulin pump were randomized to either Klue for the first 6 weeks or standard care. There were 3 definitions for a meal occurrence: (1) Klue detection, (2) CGM rate of change of 2mg/dL/min for 20 minutes after cubic spline smoothing and (3) boluses for carbohydrates. Boluses were classified as premeal if ≤30 minutes prior to a CGM event, late if after CGM event but within 2 hours, and missed if no bolus within 2 hours.
Results
17 participants (mean age 17.7±4.6 years) were enrolled and 8 were randomized to start with Klue. The patients on Klue had a HbA1c decrease of 0.5% compared to the usual care arm (p=0.004). There were significantly fewer missed meal boluses (p < 0.00001) with Klue utilization. On average, Klue detected a missed meal bolus 18 minutes prior to a significant CGM rate of change and there was 1 Klue false-positive every 2-3 days.
Conclusions
Automated meal reminders improved HbA1c and reduced the number of late meal boluses. In addition to improving compliance, this technology has the potential to provide more rapid meal announcements to a closed-loop insulin delivery system.