A Performance-Based Adaptation Index for Automated Insulin Delivery Systems

Automated insulin delivery (AID) algorithms can benefit from tuning of their aggressiveness to meet individual needs, as insulin requirements vary among and within users. We introduce the Performance-Based Adaptation Index (PAI), a tool designed to enable automatic adjustment of an AID system aggressiveness based on continuous glucose monitoring (CGM) metrics.

PAI integrates two CGM-based metrics-one for hypoglycemia and another for hyperglycemia exposure-over a previous time window into a single index (${\alpha }_{\theta }$). We propose two methods to compute ${\alpha }_{\theta }$: one based on time in range (TIR, 70-180 mg/dL), and the other on glycemic risk indices. Using ${\alpha }_{\theta }$, we developed a multiplicative strategy to adjust the AID system's aggressiveness, accounting for situations where ${\alpha }_{\theta }$ cannot be reliably calculated. The feasibility of this method was assessed in-silico using the UVA/Padova Type 1 Diabetes Simulator and our full closed-loop algorithm (UVA-model predictive control (MPC)) across five scenarios: optimal tuning (baseline), conservative and aggressive tunings, and temporary and permanent changes in insulin needs. Glycemic outcomes were evaluated from the simulated glucose traces.

Negligible performance variations were observed in the baseline scenario. For the conservative scenario, adjusting ${\alpha }_{\theta }$ improved TIR (35.1% vs 71.8%) and increased total daily insulin (32.1 U vs 41.2 U). Conversely, for the aggressive scenario, it reduced hypoglycemia exposure (TBR: 2.6% vs 1.4%) and overall insulin usage (45.6 U vs 43.0 U).

In-silico results demonstrated the safety and efficacy of using PAI to automatically tune the UVA-MPC controller, achieving TIR values above 70% under fully closed-loop conditions and across various physiological states. Clinical validation of these results is warranted.