First use of model predictive control in outpatient wearable artificial pancreas

Simone del Favero; 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

doi:https://doi.org/10.2337/dc13-1631

First use of model predictive control in outpatient wearable artificial pancreas

Simone del Favero, 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

Research output: Contribution to journal › Article › Academic › peer-review

93 Citations (Scopus)

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

Original language	English
Pages (from-to)	1212-1215
Journal	Diabetes Care
Volume	37
Issue number	5
DOIs	https://doi.org/10.2337/dc13-1631
Publication status	Published - 2014

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del Favero, S., Bruttomesso, D., Di Palma, F., Lanzola, G., Visentin, R., Filippi, A., Scotton, R., Toffanin, C., Messori, M., Scarpellini, S., Keith-Hynes, P., Kovatchev, B. P., DeVries, J. H., Renard, E., Magni, L., Avogaro, A., & Cobelli, C. (2014). First use of model predictive control in outpatient wearable artificial pancreas. Diabetes Care, 37(5), 1212-1215. https://doi.org/10.2337/dc13-1631

@article{ddf7f9ffc31b4819a98118f03543d42f,

title = "First use of model predictive control in outpatient wearable artificial pancreas",

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",

author = "{del Favero}, Simone and Daniela Bruttomesso and {Di Palma}, Federico and Giordano Lanzola and Roberto Visentin and Alessio Filippi and Rachele Scotton and Chiara Toffanin and Mirko Messori and Stefania Scarpellini and Patrick Keith-Hynes and Kovatchev, {Boris P.} and DeVries, {J. Hans} and Eric Renard and Lalo Magni and Angelo Avogaro and Claudio Cobelli",

year = "2014",

doi = "https://doi.org/10.2337/dc13-1631",

language = "English",

volume = "37",

pages = "1212--1215",

journal = "Diabetes Care",

issn = "0149-5992",

publisher = "American Diabetes Association Inc.",

number = "5",

}

del Favero, S, Bruttomesso, D, Di Palma, F, Lanzola, G, Visentin, R, Filippi, A, Scotton, R, Toffanin, C, Messori, M, Scarpellini, S, Keith-Hynes, P, Kovatchev, BP, DeVries, JH, Renard, E, Magni, L, Avogaro, A & Cobelli, C 2014, 'First use of model predictive control in outpatient wearable artificial pancreas', Diabetes Care, vol. 37, no. 5, pp. 1212-1215. https://doi.org/10.2337/dc13-1631

TY - JOUR

T1 - First use of model predictive control in outpatient wearable artificial pancreas

AU - del Favero, Simone

AU - Bruttomesso, Daniela

AU - Di Palma, Federico

AU - Lanzola, Giordano

AU - Visentin, Roberto

AU - Filippi, Alessio

AU - Scotton, Rachele

AU - Toffanin, Chiara

AU - Messori, Mirko

AU - Scarpellini, Stefania

AU - Keith-Hynes, Patrick

AU - Kovatchev, Boris P.

AU - DeVries, J. Hans

AU - Renard, Eric

AU - Magni, Lalo

AU - Avogaro, Angelo

AU - Cobelli, Claudio

PY - 2014

Y1 - 2014

N2 - 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

AB - 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

U2 - https://doi.org/10.2337/dc13-1631

DO - https://doi.org/10.2337/dc13-1631

M3 - Article

C2 - 24757228

SN - 0149-5992

VL - 37

SP - 1212

EP - 1215

JO - Diabetes Care

JF - Diabetes Care

IS - 5

ER -