Deep Learning Algorithm Predicts Angiographic Coronary Artery Disease in Stable Patients Using Only a Standard 12-lead Electrocardiogram.

Researchers

Journal

Modalities

Models

Abstract

Current electrocardiogram analysis algorithms cannot predict the presence of coronary artery disease (CAD), especially in stable patients. This study assessed the ability of an artificial intelligence algorithm (ECGio) to predict the presence, location, and severity of coronary artery lesions in an unselected stable patient population.A cohort of 1659 stable outpatients were randomly divided into training (86%) and validation (14%) subsets, maintaining population characteristics. ECGio was trained and validated using electrocardiograms paired with retrospectively-collected angiograms. Coronary artery lesions were classified in two analyses. The primary classification was no/mild (<30% diameter stenosis [DS]) vs moderate (30-70% DS) vs severe (≥70% DS) CAD. The secondary classification was yes/no based on ≥50% DS in any vessel.In the primary analysis, 22 had no angiographic CAD and were grouped Mild CAD (56 patients, DS <30%), 31 had Moderate CAD (DS 30-70%), and 113 had severe CAD (DS ≥70%). Weighted average sensitivity was 93.2% and weighted average specificity was 96.4%. In the secondary analysis, 93 had significant CAD; and 128 did not. There was sensitivity of 93.1% and specificity of 85.6% in determining the presence of clinically significant disease (≥50% DS) in any vessel. ECGio was able to predict stenosis with average vessel error in the LAD of 18%, the LCX of 19%, the RCA of 18%, and the LM of 8%.This study strongly suggests that it is possible to utilize an artificial intelligence algorithm to determine the presence and severity of CAD in stable patients using data from a 12-lead electrocardiogram.Copyright © 2021. Published by Elsevier Inc.