Journal of diabetes science and technology · 2025
A Performance-Based Adaptation Index for Automated Insulin Delivery Systems.
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Diaz C Jenny L, Colmegna Patricio, Pryor Elliot, Breton Marc D
Аннотация
BACKGROUND: 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.
METHODS: PAI integrates two CGM-based metrics-one for hypoglycemia and another for hyperglycemia exposure-over a previous time window into a single index (αθ). We propose two methods to compute αθ: one based on time in range (TIR, 70-180 mg/dL), and the other on glycemic risk indices. Using αθ, we developed a multiplicative strategy to adjust the AID system's aggressiveness, accounting for situations where αθ 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.
RESULTS: Negligible performance variations were observed in the baseline scenario. For the conservative scenario, adjusting αθ 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).
CONCLUSION: 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.
METHODS: PAI integrates two CGM-based metrics-one for hypoglycemia and another for hyperglycemia exposure-over a previous time window into a single index (αθ). We propose two methods to compute αθ: one based on time in range (TIR, 70-180 mg/dL), and the other on glycemic risk indices. Using αθ, we developed a multiplicative strategy to adjust the AID system's aggressiveness, accounting for situations where αθ 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.
RESULTS: Negligible performance variations were observed in the baseline scenario. For the conservative scenario, adjusting αθ 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).
CONCLUSION: 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.