Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network

• Published in: Nature medicine Vol. 26; no. 6; pp. 886 - 891
• Main Authors: Raghunath, Sushravya, Ulloa Cerna, Alvaro E., Jing, Linyuan, vanMaanen, David P., Stough, Joshua, Hartzel, Dustin N., Leader, Joseph B., Kirchner, H. Lester, Stumpe, Martin C., Hafez, Ashraf, Nemani, Arun, Carbonati, Tanner, Johnson, Kipp W., Young, Katelyn, Good, Christopher W., Pfeifer, John M., Patel, Aalpen A., Delisle, Brian P., Alsaid, Amro, Beer, Dominik, Haggerty, Christopher M., Fornwalt, Brandon K.
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01-06-2020; Nature Publishing Group
• Subjects: 692/53/2422; 692/700/1750; Adult; Aged; Aged, 80 and over; Algorithms; Area Under Curve; Artificial neural networks; Biomedical and Life Sciences; Biomedicine; Cancer Research; Cardiologists; Cardiovascular diseases; Cause of Death; Deep Learning; EKG; Electric potential; Electrocardiogram; Electrocardiography; Female; Health aspects; Humans; Infectious Diseases; Letter; Machine learning; Male; Metabolic Diseases; Middle Aged; Molecular Medicine; Mortality; Neural networks; Neural Networks, Computer; Neurosciences; Patient outcomes; Patients; Physicians; Prognosis; Proportional Hazards Models; Retrospective Studies; Risk Assessment; ROC Curve; Voltage; United States
• ISSN: 1078-8956; 1546-170X
• DOI: 10.1038/s41591-020-0870-z

The electrocardiogram (ECG) is a widely used medical test, consisting of voltage versus time traces collected from surface recordings over the heart
1
. Here we hypothesized that a deep neural network (DNN) can predict an important future clinical event, 1-year all-cause mortality, from ECG voltage–time traces. By using ECGs collected over a 34-year period in a large regional health system, we trained a DNN with 1,169,662 12-lead resting ECGs obtained from 253,397 patients, in which 99,371 events occurred. The model achieved an area under the curve (AUC) of 0.88 on a held-out test set of 168,914 patients, in which 14,207 events occurred. Even within the large subset of patients (
n
 = 45,285) with ECGs interpreted as ‘normal’ by a physician, the performance of the model in predicting 1-year mortality remained high (AUC = 0.85). A blinded survey of cardiologists demonstrated that many of the discriminating features of these normal ECGs were not apparent to expert reviewers. Finally, a Cox proportional-hazard model revealed a hazard ratio of 9.5 (
P
 < 0.005) for the two predicted groups (dead versus alive 1 year after ECG) over a 25-year follow-up period. These results show that deep learning can add substantial prognostic information to the interpretation of 12-lead resting ECGs, even in cases that are interpreted as normal by physicians.
By using data from electrocardiograms, a deep learning algorithm outperforms traditional risk scores in predicting death over the course of the next year and identifies at-risk individuals with seemingly normal electrocardiograms.