AI Article Synopsis
- This study examines the effectiveness of model predictive control (MPC) in a wearable artificial pancreas (AP) for managing postprandial glucose levels in type 1 diabetes patients in an outpatient setting.
- Six participants were tested over 42 hours, comparing sensor-augmented pump therapy with closed-loop MPC, showing significant improvement in glucose control during meals and overnight.
- The findings indicate that MPC strategies are promising for outpatient glucose management, suggesting further research through randomized crossover studies.
Article 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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| http://dx.doi.org/10.2337/dc13-1631 | DOI Listing |
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