JMIR Publications

ABSTRACT

Background:

Despite accumulating research on artificial intelligence (AI)-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 AI-based quantitative ECG (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-segment elevated myocardial infarction, left ventricular dysfunction) for predicting obstructive CAD confirmed by invasive angiography were 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 65%), 497 (69%) had obstructive CAD. QCG scores for ACS and myocardial injury were independently associated with obstructive CAD (odds ratio [95% confidence interval]: 1.09 [1.03-1.17] and 1.08 [1.02-1.16] per 10-point increase, respectively), but did not significantly improve prediction performance over that with clinical features. However, our new QCG score demonstrated better prediction performance for obstructive CAD (area under the receiver operating characteristic curve [AUROC] 0.802) than the original QCG scores, with incremental predictive value in combination with clinical features (AUROC 0.827 vs. 0.730; p<0.001).

Conclusions:

QCG scores developed for acute conditions show limited performance in identifying obstructive CAD in stable angina. However, improvement in the QCG analyzer, via training on comprehensive ECG signals in patients with stable angina, is feasible.