María F Villa-Tamayo, Patricio Colmegna, Marc D Breton
{"title":"Validation of the UVA Simulation Replay Methodology Using Clinical Data: Reproducing A Randomized Clinical Trial.","authors":"María F Villa-Tamayo, Patricio Colmegna, Marc D Breton","doi":"10.1089/dia.2023.0595","DOIUrl":null,"url":null,"abstract":"BACKGROUND\nComputer 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.\n\n\nMETHODS\nUsing 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.\n\n\nRESULTS\nTIR, 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]).\n\n\nCONCLUSION\nClinical 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.","PeriodicalId":5,"journal":{"name":"ACS Applied Materials & Interfaces","volume":"1 8","pages":""},"PeriodicalIF":8.3000,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Materials & Interfaces","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1089/dia.2023.0595","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
ACS Applied Materials & Interfaces is a leading interdisciplinary journal that brings together chemists, engineers, physicists, and biologists to explore the development and utilization of newly-discovered materials and interfacial processes for specific applications. Our journal has experienced remarkable growth since its establishment in 2009, both in terms of the number of articles published and the impact of the research showcased. We are proud to foster a truly global community, with the majority of published articles originating from outside the United States, reflecting the rapid growth of applied research worldwide.