Incorporating Sparse and Quantized Carbohydrates Suggestions in Model Predictive Control for Artificial Pancreas in Type 1 Diabetes

People with type 1 diabetes (T1D) face the challenge of administering exogenous insulin to maintain blood glucose (BG) levels in a safe physiological range, so as to avoid (possibly severe) complications. By automatizing insulin infusion, the artificial pancreas (AP) assists patients in this challenge. While insulin can decrease BG, having another input inducing glucose increase could further improve BG control. Here, we develop a model predictive control (MPC) algorithm that, in addition to insulin infusion, also provides suggestions of carbohydrates (CHOs) as a second, glucose-increasing, control input. Since CHO consumption has to be manually actuated, great care is paid in limiting the extra burden that may be caused to patients. By resorting to a mixed logical-dynamical MPC formulation, CHO intake is designed to be sparse in time and quantized. The algorithm is validated on the UVa/Padua T1D simulator, a well-established large-scale model of T1D metabolism, accepted by Food and Drug Administration (FDA). Compared with an insulin-only MPC, the new algorithm ensures increased time spent in the safe physiological range in 75% of patients. The improvement is limited for those already well controlled by the state-of-art strategy but relevant for the others: the 25th percentile of this metric is increased from 74.75% to 79.06% in the population. This is achieved while simultaneously decreasing time spent in hypoglycemia (from 0.5% to 0.12% in median) and with limited manual interventions (2.86 per day in median).

Automated summary

This article introduces a new model predictive control algorithm that improves blood glucose control by providing suggestions of carbohydrate intake while administering insulin. The algorithm significantly increases the time spent in the safe physiological range and reduces the occurrence of hypoglycemia, with minimal manual interventions.