Randomized clinical trials (RCTs) are essential to guide medical practice; however, their generalizability to a given population is often uncertain. We developed a statistically informed Generative Adversarial Network (GAN) model, RCT-Twin-GAN, that leverages relationships between covariates and outcomes and generates a digital twin of an RCT (RCT-Twin) conditioned on covariate distributions from a second patient population. We used RCT-Twin-GAN to reproduce treatment effect outcomes of the Systolic Blood Pressure Intervention Trial (SPRINT) and the Action to Control Cardiovascular Risk in Diabetes (ACCORD) Blood Pressure Trial, which tested the same intervention but had different treatment effect results. To demonstrate treatment effect estimates of each RCT conditioned on the other RCT patient population, we evaluated the cardiovascular event-free survival of SPRINT digital twins conditioned on the ACCORD cohort and vice versa (SPRINT-conditioned ACCORD twins). The conditioned digital twins were balanced by the intervention arm (mean absolute standardized mean difference (MASMD) of covariates between treatment arms 0.019 (SD 0.018), and the conditioned covariates of the SPRINT-Twin on ACCORD were more similar to ACCORD than a sprint (MASMD 0.0082 SD 0.016 vs. 0.46 SD 0.20). Most importantly, across iterations, SPRINT conditioned ACCORD-Twin datasets reproduced the overall non-significant effect size seen in ACCORD (5-year cardiovascular outcome hazard ratio (95% confidence interval) of 0.88 (0.73-1.06) in ACCORD vs median 0.87 (0.68-1.13) in the SPRINT conditioned ACCORD-Twin), while the ACCORD conditioned SPRINT-Twins reproduced the significant effect size seen in SPRINT (0.75 (0.64-0.89) vs median 0.79 (0.72-0.86)) in ACCORD conditioned SPRINT-Twin). Finally, we describe the translation of this approach to real-world populations by conditioning the trials on an electronic health record population. Therefore, RCT-Twin-GAN simulates the direct translation of RCT-derived treatment effects across various patient populations with varying covariate distributions.

The authors Dr. Thangaraj, Mr. Shankar, Dr. Oikonomou, and Dr. Khera are coinventors of a provisional patent related to the current work (63/606,203). Dr. Oikonomou is a co-inventor of the U.S. Patent Applications 63/508,315 63/177,117, a cofounder of Evidence2Health (with Dr. Khera), and has previously served as a consultant to Caristo Diagnostics Ltd (outside the present work). Dr. Nadkarni is a founder of Renalytix, Pensieve, and Verici and provides consultancy services to AstraZeneca, Reata, Renalytix, Siemens Healthineer, and Variant Bio, and serves a scientific advisory board member for Renalytix and Pensieve. He also has equity in Renalytix, Pensieve, and Verici. Dr. Mortazavi reported receiving grants from the National Institute of Biomedical Imaging and Bioengineering, National Heart, Lung, and Blood Institute, US Food and Drug Administration, and the US Department of Defense Advanced Research Projects Agency outside the submitted work. In addition, B.J.M. has a pending patent on predictive models using electronic health records (US20180315507A1). Dr. Khera is an Associate Editor of JAMA. He receives support from the National Heart, Lung, and Blood Institute of the National Institutes of Health (under awards R01HL167858 and K23HL153775) and the Doris Duke Charitable Foundation (under award 2022060). He also receives research support, through Yale, from Bristol-Myers Squibb, Novo Nordisk, and BridgeBio. He is a coinventor of U.S. Pending Patent Applications 63/562,335, 63/177,117, 63/428,569, 63/346,610, 63/484,426, 63/508,315, and 63/606,203. He is a co-founder of Ensight-AI, Inc. and Evidence2Health, health platforms to improve cardiovascular diagnosis and evidence-based cardiovascular care.

The study is supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health (R01HL167858). Dr. Thangaraj and Dr. Oikonomou are also supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health (5T32HL155000-03 and 1F32HL170592-01, respectively).

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The IRB of Yale University waived ethical approval and granted a waiver of consent for this work since this was a retrospective study with minimal risks to subjects.

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