First Use of Model Predictive Control in Outpatient Wearable Artificial Pancreas

Abstract

OBJECTIVEInpatient studies suggest that model predictive control (MPC) is one of the most promising algorithms for artificial pancreas (AP). So far, outpatient trials have used hypo/hyperglycemia-mitigation or medical-expert systems. In this study, we report the first wearable AP outpatient study based on MPC and investigate specifically its ability to control postprandial glucose, one of the major challenges in glucose control. RESEARCH DESIGN AND METHODSA new modular MPC algorithm has been designed focusing on meal control. Six type 1 diabetes mellitus patients underwent 42-h experiments: sensoraugmented pump therapy in the first 14 h (open-loop) and closed-loop in the remaining 28 h. RESULTS MPC CONCLUSIONSThis outpatient study confirms inpatient evidence of suitability of MPC-based strategies for AP. These encouraging results pave the way to randomized crossover outpatient studies.

A Novel, Stable, Aqueous Glucagon Formulation Using Ferulic Acid as an Excipient

“…While some investigators use insulin-only systems, [1][2][3][4][5] others have reported that, in addition to insulin, the automated delivery of glucagon when glucose is declining and approaching hypoglycemic levels reduces the frequency and duration of hypoglycemia. [6][7][8][9][10] However, glucagon is an unstable peptide and forms amyloid fibrils when aged in acidic aqueous solutions.…”

Closed-Loop System in the Management of Diabetes: Past, Present, and Future

“…The CL group had more time spent in target range (94.8% vs. 68.2%) and less time spent in hypoglycemia (1.25 vs. 11.9%) during the day with satisfactory overnight control. 67 Exercise-induced hypoglycemia is a challenge in control-torange CL. Breton et al 68 conducted a feasibility study where information on heart rate was added in the MPC algorithm; in a randomized crossover trial in 12 subjects with T1DM, heart rate information to control-to-range reduced the significant decline in blood glucose level and yielded fewer hypoglycemic events during exercise.…”

Controlling blood glucose variability under uncertainty using reinforcement learning and Gaussian processes

“…In Dassau et al [25], a fully automated multi-parametric model predictive control algorithm with insulin on-board was experimentally tested with encouraging results. The first wearable AP outpatient study using a meal-informed MPC strategy was reported by Del Favero et al [26] aiming to investigate the ability to control postprandial glucose. Despite promising results were obtained in short-term studies for a single meal (dinner), long-term randomized studies with numerous meals are needed to prove superiority of MPC over the commonly used bolus calculator.…”