Using Artificial Intelligence to Adjudicate Major Adverse Cardiovascular Events in Clinical Trials

Clinical Snapshot

Key Findings

• P Value: Not reported for agreement statistics

• Effect Size: Overall agreement: 86% deaths, 76% potential MIs, 84% potential strokes

• Primary Outcome: Agreement between AI and CEC adjudication: 97% for CV death, 89% for MI, 88% for stroke (confident events)

• Nnt Or Sensitivity: AI provided confident adjudication in 69% of deaths, 46% of potential MIs, 81% of potential strokes

• Confidence Interval: Treatment effect HR with AI: 0.91 (95% CI: 0.78-1.07) vs CEC: 0.90 (95% CI: 0.77-1.05)

Clinical Application

Requires sophisticated AI infrastructure and ongoing model maintenance, but could significantly reduce CEC workload Relevant for Australian cardiovascular trials and registry studies. Could align with TGA requirements for clinical trial conduct while potentially reducing costs for pharmaceutical sponsors and CROs Cardiovascular clinical trials requiring MACE adjudication, particularly those with well-documented electronic health records

Abstract

BACKGROUND: Major 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. OBJECTIVES: In 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. METHODS: We 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. RESULTS: Auto-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). CONCLUSIONS: AI-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.

References

1. 1.Marti-Castellote, P. M., Badrouchi, S., Claggett, B., Xu, D., Maddah, M., Khurshid, S., Vardeny, O., Lewis, E. F., Jhund, P. S., Desai, A. S., Ellinor, P. T., Ho, J. E., McMurray, J. J. V., Pfeffer, M., Solomon, S. D., & Cunningham, J. W. (2026). Using artificial intelligence to adjudicate major adverse cardiovascular events in clinical trials. Journal of the American College of Cardiology. https://doi.org/10.1016/j.jacc.2025.10.055