期刊
JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY
卷 80, 期 6, 页码 613-626出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.jacc.2022.05.029
关键词
aortic regurgitation; aortic stenosis; artificial intelligence; deep learning; mitral regurgitation; valvular heart disease
资金
- National Institutes of Health [2T32HL007854-21]
- National Institutes of Health/National Heart, Lung, and Blood Institute Award [R01HL148248]
- Pfizer
- Eidos Therapeutics
- Edwards LifeSciences
- Amyloidosis Foundation
- Glorney-Raisbeck Fellowship Award from the New York Academy of Medicine
- Abbott Structural
- Boston Scientific
- Gore and Associates
- General Electric
- Janssen Pharmaceuticals
- Myokardia
- Medtronic
- Abbott Vascular
- JenaValve
- Attralus
- Canon Medical Systems
- GE Healthcare
- Roche Medical Systems
- W. L. Gore and Associates
- XyloCor Therapeutics
- Abbott
- Edwards
This study developed ECG deep learning algorithms to accurately identify aortic stenosis, aortic regurgitation, and mitral regurgitation. The algorithms performed well in validation and modeling, and may serve as the basis for a valvular heart disease screening program.
BACKGROUND Valvular heart disease is an important contributor to cardiovascular morbidity and mortality and re-mains underdiagnosed. Deep learning analysis of electrocardiography (ECG) may be useful in detecting aortic stenosis (AS), aortic regurgitation (AR), and mitral regurgitation (MR). OBJECTIVES This study aimed to develop ECG deep learning algorithms to identify moderate or severe AS, AR, and MR alone and in combination. METHODS A total of 77,163 patients undergoing ECG within 1 year before echocardiography from 2005-2021 were identified and split into train (n = 43,165), validation (n = 12,950), and test sets (n = 21,048; 7.8% with any of AS, AR, or MR). Model performance was assessed using area under the receiver-operating characteristic (AU-ROC) and precision -recall curves. Outside validation was conducted on an independent data set. Test accuracy was modeled using different disease prevalence levels to simulate screening efficacy using the deep learning model. RESULTS The deep learning algorithm model accuracy was as follows: AS (AU-ROC: 0.88), AR (AU-ROC: 0.77), MR (AU -ROC: 0.83), and any of AS, AR, or MR (AU-ROC: 0.84; sensitivity 78%, specificity 73%) with similar accuracy in external validation. In screening program modeling, test characteristics were dependent on underlying prevalence and selected sensitivity levels. At a prevalence of 7.8%, the positive and negative predictive values were 20% and 97.6%, respectively. CONCLUSIONS Deep learning analysis of the ECG can accurately detect AS, AR, and MR in this multicenter cohort and may serve as the basis for the development of a valvular heart disease screening program. (C) 2022 The Authors. Published by Elsevier on behalf of the American College of Cardiology Foundation.
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