In-Silico Validation of Parameter Optimization Strategies for Automated Insulin Delivery Systems using the UVA Replay Simulation Technology

Automated insulin delivery (AID) systems have shown significant potential in managing type 1 diabetes (T1D), yet personalizing therapy parameters remains challenging. This study advances the optimization of AID therapy profiles through an updated decision support system (DSS) leveraging the University of Virginia Replay Simulator (UVA-RS). The DSS employs a personalized glucose-insulin dynamics model to simulate glucose response to therapy adjustments and an optimization algorithm to determine therapy parameters that improves overall glycemic control. We evaluated the system’s performance through three in-silico scenarios, focusing on recommendation reliability, constraint impact, robustness to metabolic and behavioral variability, and performance over five-month simulated use. Results indicate improved therapy personalization and glycemic control, supporting the potential for DSS to enhance AID system efficacy.