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Hypothesis: Closed-loop control systems for an artificial pancreas using multi-parametric model predictive control can be developed and evaluated safely in patients with Type 1 Diabetes Mellitus (T1DM) to control blood glucose concentrations.
This study seeks to combine real-time continuous glucose sensing with automated insulin delivery in a closed-loop system that will achieve euglycemia in patients with T1DM. The end result of this line of research will be an artificial pancreas that will provide around-the-clock glucose regulation through controlled insulin delivery in response to detected patterns of change in glucose levels.
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The goal of the JDRF Artificial Pancreas Project is to produce an autonomous artificial pancreas that can safely and effectively regulate glycemia in people with type 1 diabetes mellitus. In our work, this fully automated closed-loop system combines a subcutaneous continuous glucose monitor (CGM) and a continuous subcutaneous insulin infusion (CSII) pump with a sophisticated control algorithm. This is a proof-of-concept study to demonstrate that the controller could bring the patient back to a relatively normal glucose concentration after an unannounced meal and from mild hyperglycemia. Once the system is initiated, all insulin delivery is calculated automatically. There was no outside intervention either by the subject or medical personnel. An artificial pancreas system that aims at replicating normal beta-cell function by using the subcutaneous-subcutaneous (sc-sc) route needs to address inherent delays in both glucose sensing and insulin delivery. Our strategic approach is that a closed-loop system should operate safely without any knowledge of meals or other disturbances. We have developed the Artificial Pancreas System (APS©) and used it to clinically evaluate a control strategy that allows efficient glycemic control without any a priori meal information. The Artificial Pancreas device uses the Artificial Pancreas System (APS©) platform with the OmniPod insulin pump, the DexCom SEVEN PLUS CGM and a multi-parametric model predictive control algorithm (mpMPC) with an insulin-on-board (IOB) safety constraint.
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19 participants in 1 patient group
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Data sourced from clinicaltrials.gov
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