Key result
AI-based quantitative ECG score improves obstructive CAD prediction in stable angina over clinical features alone.
Why the study?
Although AI-based ECG algorithms for predicting ACS have been researched, their application in stable angina is not well evaluated.
Does a new AI-based quantitative electrocardiography score improve the prediction of obstructive CAD in patients with stable angina compared to existing scores and clinical features?
Observational (n=723)
No
Does a new AI-based quantitative electrocardiography score improve the prediction of obstructive CAD in patients with stable angina compared to existing scores and clinical features?
Effect estimate: OR 1.09 (95% CI 1.03-1.17)
p-value: p=<.001
A newly developed AI-based ECG score specifically trained for stable angina significantly improves the prediction of obstructive CAD compared to clinical features alone or AI scores developed for acute conditions.
May enhance CAD prediction in stable angina when added to clinical features; leaves open prospective validation and outcome impact.
BACKGROUND Despite accumulating research on artificial intelligence–based electrocardiography (ECG) algorithms for predicting acute coronary syndrome (ACS), their application in stable angina is not well evaluated. OBJECTIVE We evaluated the utility of an existing artificial intelligence–based quantitative electrocardiography (QCG) analyzer in stable angina and developed a new ECG biomarker more suitable for stable angina. METHODS This single-center study comprised consecutive patients with stable angina. The independent and incremental value of QCG scores for coronary artery disease (CAD)–related conditions (ACS, myocardial injury, critical status, ST-elevation myocardial infarction, and left ventricular dysfunction) for predicting obstructive CAD confirmed by invasive angiography was examined. Additionally, ECG signals extracted by the QCG analyzer were used as input to develop a new QCG score. RESULTS Among 723 patients with stable angina (median age 68 years; male: 470/723, 65%), 497 (69%) had obstructive CAD. QCG scores for ACS and myocardial injury were independently associated with obstructive CAD (odds ratio [OR] 1.09, 95% CI 1.03-1.17 and OR 1.08, 95% CI 1.02-1.16 per 10-point increase, respectively) but did not significantly improve prediction performance compared to clinical features. However, our new QCG score demonstrated better prediction performance for obstructive CAD (area under the receiver operating characteristic curve 0.802) than the original QCG scores, with incremental predictive value in combination with clinical features (area under the receiver operating characteristic curve 0.827 vs 0.730; P<.001). CONCLUSIONS QCG scores developed for acute conditions show limited performance in identifying obstructive CAD in stable angina. However, improvement in the QCG analyzer, through training on comprehensive ECG signals in patients with stable angina, is feasible.
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Park et al. (2022) conducted an observational in stable angina (n=723). Artificial intelligence-based quantitative electrocardiography (QCG) analyzer vs. Clinical features was evaluated on Obstructive coronary artery disease confirmed by invasive angiography (OR 1.09, 95% CI 1.03-1.17, p=<.001). A new AI-based quantitative ECG score improved the prediction of obstructive CAD in stable angina when combined with clinical features compared to clinical features alone (AUC 0.827 vs 0.730; P<.001).
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