Why the study?
Cardiovascular risk scoring systems rely on traditional risk variables rather than cardiac structure and function, motivating an AI approach to predict mortality using standard 12-lead ECG data.
Does an AI model using a single 12-lead ECG predict long-term mortality comparably to the ESC-SCORE in patients with suspected chronic coronary syndrome?
Population
720 patients scheduled for invasive coronary angiography for suspected chronic coronary syndrome
Comparison
ECG-based AI model vs ESC-SCORE
Design
Registry-based cohort study
Key result
An AI model using a single 12-lead ECG predicted long-term mortality with an AUROC of 0.606 (vs 0.584 for ESC-SCORE) in primary prevention and 0.612 (vs 0.658) in secondary prevention.
Authors
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AI-ECG modestly outperforms ESC-SCORE for mortality in primary prevention but underperforms in secondary; extends ECG-AI risk tools while needing validation.
Cohort (n=720)
Does an AI model using a single 12-lead ECG predict long-term mortality comparably to the ESC-SCORE in patients with suspected chronic coronary syndrome?
Effect estimate: AUROC 0.606 vs 0.584 (primary prevention); 0.612 vs 0.658 (secondary prevention)
Wegener et al. (2021) conducted a cohort in Suspected chronic coronary syndrome (n=720). AI model based on a single 12-lead ECG vs. ESC-SCORE was evaluated on Long-term mortality (AUROC 0.606 vs 0.584 (primary prevention); 0.612 vs 0.658 (secondary prevention)). An AI model using a single 12-lead ECG predicted long-term mortality with an AUROC of 0.606 (vs 0.584 for ESC-SCORE) in primary prevention and 0.612 (vs 0.658) in secondary prevention.