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

Pablo M Marti-Castellote1, Samarra Badrouchi2, Brian Claggett2

  • 1Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA; Cardiovascular Disease Initiative, Broad Institute of Harvard University and Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

Journal of the American College of Cardiology
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PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) effectively adjudicates major adverse cardiovascular events (MACE), showing high agreement with physician committees. This AI system could streamline MACE assessment in clinical trials, reducing costs and improving reproducibility.

Keywords:
artificial intelligenceclinical trialsoutcome adjudication

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Area of Science:

  • Cardiovascular research
  • Medical artificial intelligence
  • Clinical trial methodology

Background:

  • Major adverse cardiovascular events (MACE), including cardiovascular death, myocardial infarction (MI), and stroke, are critical clinical outcomes.
  • Physician-led Clinical Events Committee (CEC) adjudication of MACE in global randomized trials is accurate but labor-intensive.
  • Artificial intelligence (AI) offers a potential solution for cost-effective and reproducible MACE adjudication.

Purpose of the Study:

  • To develop and validate an AI-based system for MACE adjudication.
  • To compare the AI system's performance against traditional CEC adjudication in a large global clinical trial.

Main Methods:

  • An AI system, "Auto-MACE," was developed using OpenAI's o1-mini language model for MACE adjudication.
  • A Clinical Longformer model was employed to assign confidence levels to AI adjudications.
  • Auto-MACE was validated against CEC adjudication in the PARADISE-MI trial involving 5,661 patients.

Main Results:

  • Auto-MACE achieved confident adjudication for 69% of deaths, 46% of MIs, and 81% of strokes.
  • Agreement between Auto-MACE and CEC was high for confident adjudications (97% for deaths, 89% for MIs, 88% for strokes).
  • Overall agreement across all events was 86% for deaths, 76% for MIs, and 84% for strokes, with similar treatment effect estimates.

Conclusions:

  • AI-based MACE adjudication demonstrates substantial agreement with CEC adjudication, particularly for cardiovascular death and stroke.
  • The AI system's confidence level is crucial for high accuracy.
  • Integrating AI for initial adjudication, with CEC review for uncertain cases, can optimize workload and maintain accuracy in clinical trials.