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

Abstract

BACKGROUND:

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; HEARTio Inc, Pittsburgh, PA) to predict the presence, location, and severity of coronary artery lesions in an unselected stable patient population.

METHODS:

A cohort of 1659 stable outpatients was 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 2 analyses. The primary classification was no to 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.

RESULTS:

In the primary analysis, 22 patients 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 left anterior descending coronary artery of 18%, the left circumflex coronary artery of 19%, the right coronary artery of 18%, and the left main coronary artery of 8%.

CONCLUSIONS:

This study strongly suggests that it is possible to use an artificial intelligence algorithm to determine the presence and severity of CAD in stable patients, using data from a 12-lead electrocardiogram.

• Authors: Michael Leasure, Utkars Jain, Adam Butchy, Jeremy Otten, Veronica A Covalesky, Daniel McCormick, Gary S Mintz
• Journal: The Canadian journal of cardiology (Can J Cardiol) Vol. 37 Issue 11 Pg. 1715-1724 (11 2021) ISSN: 1916-7075 [Electronic] England
• PMID: 34419615 (Publication Type: Journal Article)
• Copyright: Copyright © 2021 Canadian Cardiovascular Society. Published by Elsevier Inc. All rights reserved.

Topics

• Aged
• Algorithms
• Artificial Intelligence
• Coronary Angiography
• Coronary Artery Disease (diagnosis, physiopathology)
• Coronary Vessels (diagnostic imaging)
• Deep Learning
• Electrocardiography (methods)
• Female
• Follow-Up Studies
• Fractional Flow Reserve, Myocardial (physiology)
• Humans
• Male
• Middle Aged
• Predictive Value of Tests
• Retrospective Studies
• Severity of Illness Index