Artificial Pancreas: Model Predictive Control Design from Clinical Experience

Abstract

Background: The objective of this research is to develop a new artificial pancreas that takes into account the experience accumulated during more than 5000 h of closed-loop control in several clinical research centers. The main objective is to reduce the mean glucose value without exacerbating hypo phenomena. Controller design and in silico testing were performed on a new virtual population of the University of Virginia/Padova simulator. Methods: A new sensor model was developed based on the Comparison of Two Artificial Pancreas Systems for Closed-Loop Blood Glucose Control versus Open-Loop Control in Patients with Type 1 Diabetes trial AP@home data. The Kalman filter incorporated in the controller has been tuned using plasma and pump insulin as well as plasma and continuous glucose monitoring measures collected in clinical research centers. New constraints describing clinical knowledge not incorporated in the simulator but very critical in real patients (e.g., pump shutoff) have been introduced. The proposed model predictive control (MPC) is characterized by a low computational burden and memory requirements, and it is ready for an embedded implementation. Results: The new MPC was tested with an intensive simulation study on the University of Virginia/Padova simulator equipped with a new virtual population. It was also used in some preliminary outpatient pilot trials. The obtained results are very promising in terms of mean glucose and number of patients in the critical zone of the control variability grid analysis. Conclusions: The proposed MPC improves on the performance of a previous controller already tested in several experiments in the AP@home and JDRF projects. This algorithm complemented with a safety supervision module is a significant step toward deploying artificial pancreases into outpatient environments for extended periods of time.

Artificial Pancreas: A Review of Fundamentals and Inpatient and Outpatient Studies

“…A less conservative implementation of the Range Control Module is based on a MPC regulator [33,44,45]. In this case, a control action aims to enforce tight glycemic control.…”

“…For the MPC regulator, controller aggressiveness is individualized for each subject by the upper module (Initialization Module) based on readily available patient characteristics, e.g. body weight, insulin-to-carbohydrate ratio, and basal insulin delivery [44,45]. …”

Event-Triggered Model Predictive Control for Embedded Artificial Pancreas Systems

“…Widely used u max ,k include mechanical constraints such as the maximum infusion rate of the pump, physiological constraints such as the insulin-on-board constraint, and safety constraints such as limitations on insulin infusion post-exercise, limits on glucose and insulin velocity; see [35], [41], [42]. …”

Continuous Glucose Monitoring, Future Products, and Update on Worldwide Artificial Pancreas Projects

“…35 Algorithms can be integrated or consist of separate module(s) for safety and glucose regulation. 36 Algorithm self-learning capabilities and integration of auxiliary sensors for detection of exercise might allow for individualized treatment and treatment adaption over time. 37 Various modes of CL operation can be discriminated, ranging from fully automated insulin administration systems (full-CL) requiring virtually no user input to hybrid-CL systems requiring frequent user input, for example, for meal intake or exercise announcement.…”