Peer-Reviewed Publication
Circulation2026;153(22):1694-1706.June 2, 2026Journal Article
Adaptive AI for Cardiovascular Event Adjudication: Cardiovascular Event Adjudication Across Different Definitions in the ODYSSEY OUTCOMES and EUCLID Trials.
Sreekanth Vemulapalli1,2, Karla Peña Guerra3, Daniel Wojdyla1, W Schuyler Jones1,2, Kenneth W Mahaffey4, Robert A Harrington5, Philippe Gabriel Steg6, Gregory G Schwartz7, Manesh R Patel1,2, Renato D Lopes1,2, Ricardo Henao1,8,3
1Duke Clinical Research Institute, Durham, NC (S.V., D.W., W.S.J., M.R.P., R.D.L., R.H.).
2Departments of Medicine (S.V., W.S.J., M.R.P., R.D.L.), Duke University School of Medicine, Durham, NC.
3Department of Biostatistics and Bioinformatics, Duke University, Durham, NC (K.P.G., R.H.).
4Stanford Center for Clinical Research, Stanford University School of Medicine, Palo Alto, CA (K.W.M.).
5Weill Cornell Medicine, New York, NY (R.A.H.).
6Université Paris-Cité, AP-HP and INSERMU_1148, Paris, France (P.G.S.).
7Cardiology Section, Rocky Mountain Regional VA Medical Center and University of Colorado School of Medicine, Aurora, CO (G.G.S.).
8Electrical and Computer Engineering (R.H.), Duke University School of Medicine, Durham, NC.
Abstract
BACKGROUND: Clinical end point classification (CEC) is the gold standard for cardiovascular end point measurement in clinical trials but adds time and cost. We developed and validated an artificial intelligence (AI) algorithm (adaptive AI for CEC [ADAPT-CEC]) that adjudicates multiple cardiovascular end points and adapts to new definitions.
METHODS: ADAPT-CEC was derived from myocardial infarctio…
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