In Silico Analysis of an Exercise-Safe Artificial Pancreas With Multistage Model Predictive Control and Insulin Safety System

Abstract

Background: Maintaining glycemic equilibrium can be challenging for people living with type 1 diabetes (T1D) as many factors (eg, length, type, duration, insulin on board, stress, and training) will impact the metabolic changes triggered by physical activity potentially leading to both hypoglycemia and hyperglycemia. Therefore, and despite the noted health benefits, many individuals with T1D do not exercise as much as their healthy peers. While technology advances have improved glucose control during and immediately after exercise, it remains one of the key limitations of artificial pancreas (AP) systems, largely because stopping insulin at the onset of exercise may not be enough to prevent impending, exercise-induced hypoglycemia. Methods: A hybrid AP algorithm with subject-specific exercise behavior recognition and anticipatory action is designed to prevent hypoglycemic events during and after moderate-intensity exercise. Our approach relies on a number of key innovations, namely, an activity informed premeal bolus calculator, personalized exercise pattern recognition, and a multistage model predictive control (MS-MPC) strategy that can transition between reactive and anticipatory modes. This AP design was evaluated on 100 in silico subjects from the most up-to-date FDA-accepted UVA/Padova metabolic simulator, emulating an outpatient clinical trial setting. Results with a baseline controller, a regular MPC (rMPC), are also included for comparison purposes. Results: In silico experiments reveal that the proposed MS-MPC strategy markedly reduces the number of exercise-related hypoglycemic events (8 vs 68). Conclusion: An anticipatory mode for insulin administration of a monohormonal AP controller reduces the occurrence of hypoglycemia during moderate-intensity exercise.

Impact of Accelerating Insulin on an Artificial Pancreas System Without Meal Announcement: An In Silico Examination

“…To assess the impact of faster insulins on the performance of an AP, we consider an originally hybrid MPC law as a baseline. This control strategy has been published by the authors elsewhere, 33 and a summary of its formulation is provided in the Appendix.…”

Dynamic of Glucose Homeostasis in Virtual Patients: A Comparison between Different Behaviors

“…This type of research seeks to ensure the feasibility of in-vivo experiments from those previously developed. In several works, in-silico studies based on a population of analyzed VP are proposed, in Garcia-Tirado et al [ 18 ] predictive control techniques of multistage models for artificial pancreas (AP) systems based on 100 VP with T1DM are developed, in Rahmanian et al [ 19 ], a controller is developed to regulate blood glucose levels in patients with T1DM, where they relied on in-silico experimentation. In Nath et al [ 20 ] according to 100 VP with T1DM nonlinear observers based on glucose control are designed.…”

“…Several papers present mathematical models describing variations in the dynamics of glucose homeostasis according to physical activity for experimental development in-silico. In Alkhateeb et al [ 26 ], studies on minimum order mathematical models for the development of VP that consider variations of the glycemic profile in patients with T1DM under food intakes as well as physical activity are presented; a simulator for the development of VP with T1DM under conditions of physical activity, insulin administration and food intakes is shown in Kartono et al [ 27 ], Resalat et al [ 28 ], Garcia-Tirado et al [ 18 ] and Moser et al [ 29 ] research concerning the effects of physical activity on glucose homeostasis. Therefore, it is possible to observe that most of the attention of metabolism studies from the mathematical point of view are focused on conditions of diabetes mellitus, especially the case of type 1, being this a reduced sector of the population with metabolic diseases.…”

Using an Online Disturbance Rejection and Anticipation System to Reduce Hyperglycemia in a Fully Closed-Loop Artificial Pancreas System

“…Several simulation and clinical experiments conducted by our group have shown how anticipatory profiles integrated into AID systems can reduce the unwanted effects of glycemic disturbances. Simulation experiments have demonstrated that multistage model predictive controllers (MS-MPC) informed by disturbance profiles to anticipate moderate exercise’s effects can reduce hypoglycemia, 9 -11 results that were later confirmed in a randomized crossover clinical trial with 15 adult participants: 12 There were fewer hypoglycemic events (9 vs 33), and the percent time where BG was <70 mg/dL (time below range [TBR]) was 1.3% lower while the participants used the MS-MPC system compared with a well-tuned standard MPC. The overall reduction in hypoglycemia resulted in no significant increase in TAR.…”