Abstract

Initial Food and Drug Administration-approved artificial pancreas (AP) systems will be hybrid closed-loop systems that require prandial meal announcements and will not eliminate the burden of premeal insulin dosing. Multiple model probabilistic predictive control (MMPPC) is a fully closed-loop system that uses probabilistic estimation of meals to allow for automated meal detection. In this study, we describe the safety and performance of the MMPPC system with announced and unannounced meals in a supervised hotel setting.The Android phone-based AP system with remote monitoring was tested for 72?h in six adults and four adolescents across three clinical sites with daily exercise and meal challenges involving both three announced (manual bolus by patient) and six unannounced (no bolus by patient) meals. Safety criteria were predefined. Controller aggressiveness was adapted daily based on prior hypoglycemic events.Mean 24-h continuous glucose monitor (CGM) was 157.4?±?14.4?mg/dL, with 63.6?±?9.2% of readings between 70 and 180?mg/dL, 2.9?±?2.3% of readings <70?mg/dL, and 9.0?±?3.9% of readings >250?mg/dL. Moderate hyperglycemia was relatively common with 24.6?±?6.2% of readings between 180 and 250?mg/dL, primarily within 3?h after a meal. Overnight mean CGM was 139.6?±?27.6?mg/dL, with 77.9?±?16.4% between 70 and 180?mg/dL, 3.0?±?4.5% <70?mg/dL, 17.1?±?14.9% between 180 and 250?mg/dL, and 2.0?±?4.5%> 250?mg/dL. Postprandial hyperglycemia was more common for unannounced meals compared with announced meals (4-h postmeal CGM 197.8?±?44.1 vs. 140.6?±?35.0?mg/dL; P?

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View details for PubMedCentralID PMC5963546