Hughes John Weston, Somani Sulaiman, Elias Pierre, Tooley James, Rogers Albert J, Poterucha Timothy, Haggerty Christopher M, Salerno Michael, Ouyang David, Ashley Euan, Zou James, Perez Marco V
Department of Computer Science, Stanford University, 353 Jane Stanford Way, Stanford, CA 94305, USA.
Department of Medicine, Stanford University, 1265 Pasteur Dr, Stanford, CA 94305, USA.
Eur Heart J Digit Health. 2024 Apr 25;5(4):427-434. doi: 10.1093/ehjdh/ztae034. eCollection 2024 Jul.
Deep learning methods have recently gained success in detecting left ventricular systolic dysfunction (LVSD) from electrocardiogram (ECG) waveforms. Despite their high level of accuracy, they are difficult to interpret and deploy broadly in the clinical setting. In this study, we set out to determine whether simpler models based on standard ECG measurements could detect LVSD with similar accuracy to that of deep learning models.
Using an observational data set of 40 994 matched 12-lead ECGs and transthoracic echocardiograms, we trained a range of models with increasing complexity to detect LVSD based on ECG waveforms and derived measurements. The training data were acquired from the Stanford University Medical Center. External validation data were acquired from the Columbia Medical Center and the UK Biobank. The Stanford data set consisted of 40 994 matched ECGs and echocardiograms, of which 9.72% had LVSD. A random forest model using 555 discrete, automated measurements achieved an area under the receiver operator characteristic curve (AUC) of 0.92 (0.91-0.93), similar to a deep learning waveform model with an AUC of 0.94 (0.93-0.94). A logistic regression model based on five measurements achieved high performance [AUC of 0.86 (0.85-0.87)], close to a deep learning model and better than N-terminal prohormone brain natriuretic peptide (NT-proBNP). Finally, we found that simpler models were more portable across sites, with experiments at two independent, external sites.
Our study demonstrates the value of simple electrocardiographic models that perform nearly as well as deep learning models, while being much easier to implement and interpret.
深度学习方法最近在从心电图（ECG）波形中检测左心室收缩功能障碍（LVSD）方面取得了成功。尽管其准确性很高，但它们难以解释且难以在临床环境中广泛应用。在本研究中，我们着手确定基于标准ECG测量的更简单模型是否能以与深度学习模型相似的准确性检测LVSD。
使用包含40994对匹配的12导联ECG和经胸超声心动图的观察数据集，我们训练了一系列复杂度不断增加的模型，以基于ECG波形和派生测量来检测LVSD。训练数据来自斯坦福大学医学中心。外部验证数据来自哥伦比亚医学中心和英国生物银行。斯坦福数据集由40994对匹配的ECG和超声心动图组成，其中9.72%有LVSD。使用555个离散自动测量值的随机森林模型在受试者工作特征曲线（AUC）下的面积为0.92（0.91 - 0.93），与AUC为0.94（0.93 - 0.94）的深度学习波形模型相似。基于五项测量的逻辑回归模型具有高性能[AUC为0.86（0.85 - 0.87）]，接近深度学习模型且优于N末端脑钠肽前体（NT - proBNP）。最后，我们发现更简单的模型在不同地点之间更具可移植性，并在两个独立的外部地点进行了实验。
我们的研究证明了简单心电图模型的价值，其性能与深度学习模型相近，同时更易于实施和解释。
