Simple Models Versus Deep Learning in Detecting Low Ejection Fraction From The Electrocardiogram

Abstract

Deep learning methods have recently gained success in detecting left ventricular systolic dysfunction (LVSD) from electrocardiogram waveforms. Despite their impressive accuracy, they are difficult to interpret and deploy broadly in the clinical setting. We set out to determine whether simpler models based on standard electrocardiogram measurements could detect LVSD with similar accuracy to deep learning models. Using an observational dataset of 40,994 matched 12-lead electrocardiograms (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 was acquired from Stanford University Medical Center. External validation data was acquired from Columbia Medical Center and the UK Biobank. The Stanford dataset consisted of 40,994 matched ECGs and echocardiograms of which 9.72% had LVSD. A random forest model using 555 discrete, automated measurements achieves 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 5 measurements achieves high performance (AUC of 0.86 (0.85-0.87)), close to a deep learning model and better than NT-proBNP. Finally, we find that simpler models are more portable across sites, with experiments at two independent, external sites. Our study demonstrates the value of simple electrocardiographic models which perform nearly as well as deep learning models while being much easier to implement and interpret.