Abstract / Summary
The 12-lead ECG is central to the evaluation of patients with suspected coronary artery disease (CAD) but has limited diagnostic accuracy for identifying significant obstructive disease prior to elective coronary angiography. We previously developed an artificial intelligence–enhanced ECG (AI-ECG) model for detecting acute coronary occlusion. Whether this model generalizes to identifying angiographically significant CAD in symptomatic patients undergoing elective evaluation remains unknown. In this prospective cohort study, symptomatic outpatients undergoing elective coronary angiography at a tertiary center received standard 12-lead ECGs before the procedure. ECGs were analyzed offline using a previously validated AI-ECG model originally developed for acute coronary occlusion. Patients were categorized as low or intermediate-to-high risk using predefined thresholds. The primary outcome was angiographically significant CAD (≥70% stenosis in a major epicardial vessel or ≥50% stenosis of the left main coronary artery). Among 363 patients (age 58.9 ± 11 years; 55.1% male), 36.1% had angiographically significant CAD. The AI-ECG classified 56% as low risk and 44% as intermediate-to-high risk. After adjustment for conventional clinical characteristics, intermediate-to-high risk remained independently associated with significant CAD (OR 3.12, 95% CI 1.88–5.20; p < 0.001). Model discrimination was good (AUROC 0.79, 95% CI 0.74–0.84), with 60% precision at 80% recall. In summary, the ECG-SMART AI score demonstrated good diagnostic performance for identifying angiographically significant CAD in symptomatic patients undergoing elective coronary angiography. These findings support the generalizability of a previously developed AI-ECG model beyond acute coronary occlusion and suggest that AI-enhanced ECG analysis may complement existing clinical assessment by providing additional noninvasive information to refine pretest probability during elective evaluation.