Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center and NewYork-Presbyterian Hospital, New York, New York, USA.
Department of Biomedical Informatics, Columbia University, New York, New York, USA.
J Am Coll Cardiol. 2022 Aug 9;80(6):613-626. doi: 10.1016/j.jacc.2022.05.029.
Valvular heart disease is an important contributor to cardiovascular morbidity and mortality and remains underdiagnosed. Deep learning analysis of electrocardiography (ECG) may be useful in detecting aortic stenosis (AS), aortic regurgitation (AR), and mitral regurgitation (MR).
This study aimed to develop ECG deep learning algorithms to identify moderate or severe AS, AR, and MR alone and in combination.
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.
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.
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.
瓣膜性心脏病是心血管发病率和死亡率的重要原因，但仍未得到充分诊断。心电图（ECG）的深度学习分析可能有助于检测主动脉瓣狭窄（AS）、主动脉瓣反流（AR）和二尖瓣反流（MR）。
本研究旨在开发 ECG 深度学习算法，以单独和联合检测中重度 AS、AR 和 MR。
从 2005 年至 2021 年，共确定了 77163 例在超声心动图前 1 年内接受 ECG 检查的患者，并将其分为训练集（n=43165）、验证集（n=12950）和测试集（n=21048；7.8%有任何 AS、AR 或 MR）。使用受试者工作特征（AU-ROC）和精度-召回曲线评估模型性能。在独立数据集上进行外部验证。使用不同的疾病患病率水平对测试准确性进行建模，以使用深度学习模型模拟筛查效果。
深度学习算法模型的准确性如下：AS（AU-ROC：0.88）、AR（AU-ROC：0.77）、MR（AU-ROC：0.83）和任何 AS、AR 或 MR（AU-ROC：0.84；敏感性 78%，特异性 73%），外部验证的准确性相似。在筛查计划建模中，测试特征取决于基础患病率和选择的敏感性水平。在患病率为 7.8%的情况下，阳性和阴性预测值分别为 20%和 97.6%。
该多中心队列中，心电图的深度学习分析可以准确检测出 AS、AR 和 MR，可能为开发瓣膜性心脏病筛查计划提供依据。
