ArticleJournal of the American College of Cardiology2026
Using Artificial Intelligence to Adjudicate Major Adverse Cardiovascular Events in Clinical Trials.
Article in Journal of the American College of Cardiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- The Effect of Covariate Adjustment for Cox Regression in Cardiovascular Outcome Trials.Therapeutic innovation & regulatory science · 2026Article
- A Supervised Text-Embedded Transformer Matching Model to Detect Fall Injuries in Medicare Data.medRxiv : the preprint server for health sciences · 2026Article
- The Time to "Advance" Is Now: Pushing the Envelope, Shaping the Future.JACC. Advances · 2026Article
- Performance of Local Large Language Models for Adjudicating Heart Failure Hospitalizations.Circulation · 2026Article
- State of clinical AI in 2026.BMJ digital health & AI · 2026Review
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
backgroundMajor adverse cardiovascular events (MACE)-cardiovascular (CV) death, nonfatal myocardial infarction (MI), and nonfatal stroke-are highly relevant clinical outcomes. In global randomized trials, medical records review by a physician clinical events committee (CEC) is the conventional standard for adjudicating MACE but is labor intensive. Automated adjudication with the use of artificial intelligence (AI) could reduce cost and improve reproducibility.
objectivesIn this study, the authors sought to develop and validate an AI-based adjudication system for MACE and compare its performance with CEC adjudication in a large global randomized trial.
methodsWe developed an AI-based system ("Auto-MACE") that uses an iteratively refined prompt of the OpenAI o1-mini language model to adjudicate MACE events, and a Clinical Longformer model trained on adjudicated events to assign a confidence level. We validated Auto-MACE against CEC adjudication in the PARADISE-MI global clinical trial comparing sacubitril/valsartan and ramipril in 5,661 patients with MI complicated by systolic dysfunction or pulmonary congestion.
resultsAuto-MACE provided a confident adjudication in 315/455 deaths (69%), 301/659 potential MIs (46%), and 136/167 potential strokes (81%). Auto-MACE agreed with the CEC adjudication in 97%, 89%, and 88% of confident events, respectively. Among all events, Auto-MACE agreed with CEC adjudications in 86% of deaths, 76% of potential MIs, and 84% of potential strokes. The estimated effect of sacubitril/valsartan vs ramipril on composite MACE was similar with Auto-MACE adjudication (HR: 0.91; 95% CI: 0.78-1.07) and CEC adjudication (HR: 0.90; 95% CI: 0.77-1.05).
conclusionsAI-based adjudication of MACE showed high agreement with human CEC adjudication, especially for CV death and stroke, and where the model was confident. Initial AI-based adjudication with CEC review of uncertain events may reduce workload while maintaining accuracy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.