Simulation-driven optimization of insulin therapy profiles in a commercial hybrid closed-loop system

• Published in: Computer methods and programs in biomedicine Vol. 242; p. 107830
• Main Authors: Diaz C., Jenny L., Villa-Tamayo, María F., Moscoso-Vasquez, Marcela, Colmegna, Patricio
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2023
• Subjects: Blood glucose control; Decision support systems; Diabetes mellitus; Insulin delivery therapy; Optimal insulin dosing; Decision support systems; Optimal insulin dosing; Insulin delivery therapy; Blood glucose control; Diabetes mellitus
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2023.107830

Automated insulin delivery (AID) has represented a breakthrough in managing type 1 diabetes (T1D), showing safe and effective glucose control extensively across the board. However, metabolic variability still poses a challenge to commercial hybrid closed-loop (HCL) solutions, whose performance depends on customizable insulin therapy profiles. In this work, we propose an Identification-Replay-Optimization (IRO) approach to optimize gradually and safely such profiles for the Control-IQ AID algorithm. Closed-loop data are generated using the full adult cohort of the UVA/Padova T1D simulation platform in diverse glycemic scenarios. For each subject, daily records are processed and used to estimate a personalized model of the underlying insulin-glucose dynamics. Every two weeks, all identified models are integrated into an optimization procedure where daily basal and bolus profiles are adjusted so as to minimize the risks for hypo- and hyperglycemia. The proposed strategy is tested under different scenarios of metabolic and behavioral variability in order to evaluate the efficacy and convergence of the proposed strategy. Finally, glycemic metrics between cycles are compared using paired t-tests with p<0.05 as the significance threshold. Simulations reveal that the proposed IRO approach was able to improve glucose control over time by safely mitigating the risks for both hypo- and hyperglycemia. Furthermore, smaller changes were recommended at each cycle, indicating convergence when simulation conditions were maintained. The use of reliable simulation-driven tools capable of accurately reproducing field-collected data and predicting changes can substantially shorten the process of optimizing insulin therapy, adjusting it to metabolic changes and leading to improved glucose control. •An Identification-Reproduction-Optimization (IRO) approach is proposed to gradually and safely optimize insulin therapy profiles for Control-IQ.•Use of reliable simulations to reproduce real-world data and shorten the insulin treatment optimization process.•User-specific diabetes records are used to generate virtual images that account for non-modeled phenomena.•The IRO method improved glucose control over time by reducing the risks of hypo- and hyperglycemia.