Adaptive AI for Cardiovascular Event Adjudication Across Trials

30 Mar 20264 min read0 viewsJournal Feed

GIST (Key Takeaways)

ADAPT-CEC is an AI-based clinical endpoint adjudication (CEA) tool designed to classify multiple cardiovascular events and adapt to new endpoint definitions without rederivation from scratch. Derived on MI, stroke, and heart failure from ODYSSEY OUTCOMES and externally validated in EUCLID for MI, stroke, bleeding, and CV death after adapting with a limited set of EUCLID suspected events, it was evaluated against human adjudication and alternative AI methods.
In EUCLID, among 13,885 suspected primary endpoints, ADAPT-CEC correctly classified 86.4% of all endpoints and 99.4% of non-endpoints; a hybrid approach—with the bottom 30% of AI-prediction certainty subjected to human adjudication—achieved 95.6% overall accuracy and highest F1 metrics across components (CV death, MI, stroke, bleeding). By contrast, GPT-4.0 alone classified 76.3% of endpoints with 99.8% non-endpoint accuracy, and the AI-hybrid approach outperformed both AI-alone methods for several measures.
The EUCLID treatment effect was similar across adjudication strategies (HRs near 1.0). The authors conclude that brief adaptation enables a single AI model to reproduce primary outcomes in a new trial and that a human-in-the-loop hybrid approach enhances performance.

Clinical Editorial

Circulation published a clinical update in Cardiology on 30 Mar 2026.

The item focuses on Adaptive AI for Cardiovascular Event Adjudication: Cardiovascular Event Adjudication Across Different Definitions in the ODYSSEY OUTCOMES and EUCLID Trials.

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Circulation

Adaptive AI for Cardiovascular Event Adjudication Across Trials

GIST (Key Takeaways)

Clinical Editorial

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Adaptive AI for Cardiovascular Event Adjudication Across Trials

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Clinical Editorial

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