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

Abstract

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 AF-related 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.

Keywords: atrial fibrillation; atrial flutter; deep learning; neural network; prediction; stroke.

Figures

Figure 1.

Flow chart illustrating the study design and data summary.A, Data exclusions and data definition of proof-of-concept model. B, Data flow for deployment model. AF indicates atrial fibrillation.

Figure 2.

Illustration of model performance for all, normal, and abnormal subpopulations. The model performance is represented for proof-of-concept model as area under the receiver operating characteristic (left) and precision-recall curves (right). The bars represent the mean performance across the 5-fold cross-validation with error bars showing 95% CIs. The black circle represents the M0 model performance on the holdout set. The 3 bars represent model performance for extreme gradient boosting (XGBoost) model with age and sex as inputs (gray); DNN model with digital ECG traces as input (DNN-ECG; orange); and DNN model with digital ECG traces, age, and sex as inputs (DNN-ECG-AS; blue). AF indicates atrial fibrillation; and ROC, receiver operating characteristic.

Figure 3.

ROC curves, incidence-free KM survival curves, and HRs in subpopulations for the 3 models evaluated on the holdout set. The 3 models are XGBoost model with age and sex only (blue); DNN model with ECG traces only (DNN-ECG; red); and DNN model with ECG traces, age, and sex (DNN-ECG-AS; black) for all ECGs in the holdout set. A, ROC curves with operating points marked for the 3 models. B, Incidence-free KM curves for the high- and low-risk groups for the operating point shown in A for a follow-up of 30 years. Note that curves corresponding to the low-risk group for all 3 models overlap. C, The plot of HR with 95% CIs for the 3 models in subpopulations defined by age groups, sex, and normal or abnormal ECG label. Note that there is no HR for age <50 years for the first model as there was no subject classified as high-risk for new-onset atrial fibrillation by the model for that subpopulation. DNN indicates deep neural network; HR, hazard ratio; KM, Kaplan-Meier; and ROC, receiver operating characteristic.

Figure 4.

Incidence-free KM survival curves within the holdout set for subpopulations defined by sex, and age groups. The top row shows the KM curves for subpopulations in age groups: <50 years, 50 to 65 years, and ≥65 years for men (left) and women (right). The bottom row shows the KM curves for the model-predicted (model M0 trained with ECG traces, age, and sex; DNN-ECG-AS) low-risk groups and high-risk groups for new-onset atrial fibrillation for each age group for men and women. It also reflects relative hazards between age groups. The horizontal dotted gray line represents incidence-free proportion of 50%, and vertical lines represent the median survival time for the respective curves. DNN indicates deep neural network; and KM, Kaplan-Meier.

Figure 5.

Illustration of model sensitivity to detect patients at risk of AF-related strokes as a function of the proportion of the population flagged as high risk to develop new-onset AF. Colored curves denote patients with strokes occurring within 1 (blue), 2 (orange), and 3 (green) years after ECG in the deployment test set. Gray dotted lines represent the corresponding optimal operating thresholds from Table II in the Data Supplement. AF indicates atrial fibrillation.