Hsu Chu-Yu, Liu Pang-Yen, Liu Shu-Hsin, Kwon Younghoon, Lavie Carl J, Lin Gen-Min
Department of Medicine, Hualien Armed Forces General Hospital, Hualien City, Taiwan.
Department of Internal Medicine, Tri-Service General Hospital and National Defense Medical Center, Taipei City, Taiwan.
Front Cardiovasc Med. 2022 Mar 1;9:840585. doi: 10.3389/fcvm.2022.840585. eCollection 2022.
Left atrial enlargement (LAE) is associated with cardiovascular events. Machine learning for ECG parameters to predict LAE has been performed in middle- and old-aged individuals but has not been performed in young adults.
In a sample of 2,206 male adults aged 17-43 years, three machine learning classifiers, multilayer perceptron (MLP), logistic regression (LR), and support vector machine (SVM) for 26 ECG features with or without 6 biological features (age, body height, body weight, waist circumference, and systolic and diastolic blood pressure) were compared with the P wave duration of lead II, the traditional ECG criterion for LAE. The definition of LAE is based on an echocardiographic left atrial dimension > 4 cm in the parasternal long axis window.
The greatest area under the receiver operating characteristic curve is present in machine learning of the SVM for ECG only (77.87%) and of the MLP for all biological and ECG features (81.01%), both of which are superior to the P wave duration (62.19%). If the sensitivity is fixed to 70-75%, the specificity of the SVM for ECG only is up to 72.4%, and that of the MLP for all biological and ECG features is increased to 81.1%, both of which are higher than 48.8% by the P wave duration.
This study suggests that machine learning is a reliable method for ECG and biological features to predict LAE in young adults. The proposed MLP, LR, and SVM methods provide early detection of LAE in young adults and are helpful to take preventive action on cardiovascular diseases.
左心房扩大（LAE）与心血管事件相关。针对心电图参数预测LAE的机器学习已在中老年人中开展，但尚未在年轻成年人中进行。
在2206名年龄在17 - 43岁的男性成年人样本中，将三种机器学习分类器，即多层感知器（MLP）、逻辑回归（LR）和支持向量机（SVM）用于26项心电图特征（有或无6项生物学特征，即年龄、身高、体重、腰围、收缩压和舒张压），并与II导联P波时限（LAE的传统心电图标准）进行比较。LAE的定义基于胸骨旁长轴切面超声心动图测得的左心房内径>4 cm。
仅心电图特征的SVM机器学习（77.87%）以及所有生物学和心电图特征的MLP机器学习（81.01%）的受试者工作特征曲线下面积最大，两者均优于P波时限（62.19%）。若将敏感度固定在70 - 75%，仅心电图特征的SVM特异性高达72.4%，所有生物学和心电图特征的MLP特异性增至81.1%，两者均高于P波时限的48.8%。
本研究表明，机器学习是一种利用心电图和生物学特征预测年轻成年人LAE的可靠方法。所提出的MLP、LR和SVM方法有助于早期发现年轻成年人的LAE，并有助于对心血管疾病采取预防措施。
