PDF(Pubmed)
Abstract：
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
摘要：
■深度学习方法最近在从心电图（ECG）波形检测左心室收缩功能障碍（LVSD）方面取得了成功。尽管他们的准确度很高，它们很难在临床环境中广泛解释和应用。在这项研究中,我们着手确定基于标准ECG测量的更简单的模型是否能够以与深度学习模型相似的准确度检测LVSD.
■使用40994个匹配的12导联心电图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越来越高的模型,以基于ECG波形和导出的测量值检测LVSD.训练数据是从斯坦福大学医学中心获得的。外部验证数据从哥伦比亚医学中心和英国生物库获得。斯坦福数据集包括40994个匹配的心电图和超声心动图，其中9.72%有LVSD。使用555离散的随机森林模型，自动测量获得了0.92（0.91-0.93）的接收器操作特性曲线下的面积（AUC），类似于AUC为0.94(0.93-0.94)的深度学习波形模型。基于五个测量的逻辑回归模型实现了高性能[AUC为0.86(0.85-0.87)]，接近深度学习模型，优于N末端激素原脑钠肽（NT-proBNP）。最后,我们发现更简单的模型更易于跨站点移植，有两个独立的实验，外部网站。
■我们的研究证明了简单心电图模型的价值，这些模型的性能几乎与深度学习模型相同。同时更容易实现和解释。
■使用40994个匹配的12导联心电图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越来越高的模型,以基于ECG波形和导出的测量值检测LVSD.训练数据是从斯坦福大学医学中心获得的。外部验证数据从哥伦比亚医学中心和英国生物库获得。斯坦福数据集包括40994个匹配的心电图和超声心动图，其中9.72%有LVSD。使用555离散的随机森林模型，自动测量获得了0.92（0.91-0.93）的接收器操作特性曲线下的面积（AUC），类似于AUC为0.94(0.93-0.94)的深度学习波形模型。基于五个测量的逻辑回归模型实现了高性能[AUC为0.86(0.85-0.87)]，接近深度学习模型，优于N末端激素原脑钠肽（NT-proBNP）。最后,我们发现更简单的模型更易于跨站点移植，有两个独立的实验，外部网站。
■我们的研究证明了简单心电图模型的价值，这些模型的性能几乎与深度学习模型相同。同时更容易实现和解释。
