Deep Neural Networks Can Predict New-Onset Atrial Fibrillation From the 12-Lead Electrocardiogram and Help Identify Those at Risk of AF-Related Stroke

Circulation Manuscript
Authors Sushravya Raghunath, John M. Pfeifer, Alvaro E. Ulloa-Cerna, Arun Nemani, Tanner Carbonati, Linyuan Jing, David P. vanMaanen, Dustin N. Hartzel, Jeffrey A. Ruhl, Braxton F. Lagerman, Daniel B. Rocha, Nathan J. Stoudt, Gargi Schneider, Kipp W. Johnson, Noah Zimmerman, Joseph B. Leader, H. Lester Kirchner, Christoph J. Griessenauer, Ashraf Hafez, Christopher W. Good, Brandon K. Fornwalt, and Christopher M. Haggerty

BACKGROUND: Atrial fibrillation (AF) is associated with substantial morbidity, especially when it goes undetected. If new-onset AF could be predicted, targeted screening could be used to find it early. We hypothesized that a deep neural network could predict new-onset AF from the resting 12-lead ECG and that this prediction may help identify those at risk of AF-related stroke.

METHODS: We used 1.6 M resting 12-lead digital ECG traces from 430,000 patients collected from 1984 to 2019. Deep neural networks
were trained to predict new-onset AF (within 1 year) in patients without a history of AF. Performance was evaluated using areas under the receiver operating characteristic curve and precision-recall curve. We performed an incidence-free survival analysis for a period of 30 years following the ECG stratified by model predictions. To simulate real-world deployment, we trained a separate model using all ECGs before 2010 and evaluated model performance on a test set of ECGs from 2010 through 2014 that were linked to our stroke registry. We identified the patients at risk for AFrelated stroke among those predicted to be high risk for AF by the model at different prediction thresholds.

RESULTS: The area under the receiver operating characteristic curve and area under the precision-recall curve were 0.85 and 0.22, respectively, for predicting new-onset AF within 1 year of an ECG. The hazard ratio for the predicted high- versus low-risk groups over a 30-year span was 7.2 (95% CI, 6.9–7.6). In a simulated deployment scenario, the model predicted new-onset AF at 1 year with a sensitivity of 69% and specificity of 81%. The number needed to screen to find 1 new case of AF was 9. This model predicted patients at high risk for new onset AF in 62% of all patients who experienced an AF-related stroke within 3 years of the index ECG.

CONCLUSIONS: Deep learning can predict new-onset AF from the 12-lead ECG in patients with no previous history of AF. This prediction may help identify patients at risk for AF-related strokes.