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Clinical Trial
. 2014;37(5):1212-5.
doi: 10.2337/dc13-1631.

First use of model predictive control in outpatient wearable artificial pancreas

Simone Del Favero  1 , Daniela Bruttomesso, Federico Di Palma, Giordano Lanzola, Roberto Visentin, Alessio Filippi, Rachele Scotton, Chiara Toffanin, Mirko Messori, Stefania Scarpellini, Patrick Keith-Hynes, Boris P Kovatchev, J Hans Devries, Eric Renard, Lalo Magni, Angelo Avogaro, Claudio Cobelli; AP@home Consortium
Affiliations
Clinical Trial

First use of model predictive control in outpatient wearable artificial pancreas

Simone Del Favero et al. Diabetes Care. 2014.

Abstract

Objective: Inpatient 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 methods: A new modular MPC algorithm has been designed focusing on meal control. Six type 1 diabetes mellitus patients underwent 42-h experiments: sensor-augmented pump therapy in the first 14 h (open-loop) and closed-loop in the remaining 28 h.

Results: MPC showed satisfactory dinner control versus open-loop: time-in-target (70-180 mg/dL) 94.83 vs. 68.2% and time-in-hypo 1.25 vs. 11.9%. Overnight control was also satisfactory: time-in-target 89.4 vs. 85.0% and time-in-hypo: 0.00 vs. 8.19%.

Conclusions: This outpatient study confirms inpatient evidence of suitability of MPC-based strategies for AP. These encouraging results pave the way to randomized crossover outpatient studies.

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