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Background: Computer simulators of human metabolism are powerful tools to design and validate new diabetes treatments. However, these platforms are often limited in the diversity of behaviors and glycemic conditions they can reproduce. Replay methodologies leverage field-collected data to create ad-hoc simulation environments representative of real-life conditions. After formal validations of our method in prior publications, we demonstrate its capacity to reproduce a recent clinical trial. Methods: Using the replay methodology, an ensemble of replay simulators was generated using data from a randomized crossover clinical trial comparing hybrid closed loop (HCL) and fully closed loop (FCL) control modalities in automated insulin delivery (AID), creating 64 subject/modality pairs. Each virtual subject was exposed to the alternate AID modality to compare the simulated vs observed glycemic outcomes. Equivalence tests were performed for time in, below, and above range (TIR, TBR, TAR) and glucose indexes (LBGI, HBGI) considering equivalence margins corresponding to clinical significance. Results: TIR, TAR, LBGI, and HBGI showed statistical and clinical equivalence between the original and the simulated data, TBR failed the equivalence test. For example, in HCL mode, simulated TIR was 84.89% vs. an observed 84.31% (p=0.0170, CI -3.96,2.79), and for FCL mode, TIR was 76.58% versus 77.41% (p=0.0222, CI -2.54,4.20). Conclusion: Clinical trial data confirms the prior in-silico validation of the UVA replay method in predicting the glycemic impact of modified insulin treatments. This in-vivo demonstration justifies the application of the replay method to the personalization and adaptation of treatment strategies in people with T1D.
Villa-Tamayo et al. (Thu,) studied this question.