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

Michael Leasure; Utkars Jain; Adam Butchy; Jeremy Otten; Veronica A. Covalesky; Daniel McCormick; Gary S. Mintz

doi:10.1016/j.cjca.2021.08.005

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

Journal article

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

Michael Leasure, Utkars Jain, Adam Butchy, Jeremy Otten, Veronica A. Covalesky, Daniel McCormick and Gary S. Mintz

Canadian journal of cardiology, v 37(11), pp 1715-1724

01 Nov 2021

DOI: https://doi.org/10.1016/j.cjca.2021.08.005

PMID: 34419615

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Abstract

Cardiac & Cardiovascular Systems

Cardiovascular System & Cardiology

Life Sciences & Biomedicine

Science & Technology

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.

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Title: Deep Learning Algorithm Predicts Angiographic Coronary Artery Disease in Stable Patients Using Only a Standard 12-Lead Electrocardiogram
Creators: Michael Leasure - HEARTio (United States, Pittsburgh)
Utkars Jain - HEARTio (United States, Pittsburgh)
Adam Butchy - HEARTio (United States, Pittsburgh)
Jeremy Otten - Drexel Univ, Dept Biomed Engn & Hlth Sci, Philadelphia, PA 19104 USA
Veronica A. Covalesky - Thomas Jefferson University Hospital
Daniel McCormick - Thomas Jefferson University Hospital
Gary S. Mintz - Cardiovascular Research Foundation
Publication Details: Canadian journal of cardiology, v 37(11), pp 1715-1724
Publisher: Elsevier
Number of pages: 10
Resource Type: Journal article
Language: English
Academic Unit: School of Biomedical Engineering, Science, and Health Systems
Web of Science ID: WOS:000751120500008
Scopus ID: 2-s2.0-85118777853
Other Identifier: 991021860670604721

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Collaboration types: Domestic collaboration
Web of Science research areas: Cardiac & Cardiovascular Systems

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

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