Utilizing 12-lead electrocardiogram and machine learning to retrospectively estimate and prospectively predict atrial fibrillation and stroke risk

Background: The stroke risk in patients with subclinical atrial fibrillation (AF) is underestimated. By identifying patients at high risk of embolic stroke, health-care professionals can make more informed decisions regarding anticoagulation treatment to prevent stroke. The main aim of this study was to forecast the risk of AF both retrospectively and prospectively. Methods: The research used a dataset of patients who had received a standard 12-lead electrocardiogram (ECG) at the seven branches of Chang Gung Memorial Hospital between October 2007 and December 2019. Using convolutional neural network (CNN) ECG models, the study classified the risk of AF development both retrospectively and prospectively in 1,776,968 patients by analyzing their 12-lead ECG. The study also examined the risk of stroke, hospitalization for heart failure (HF), myocardial infarction (MI), and death among patients with predicted AF versus that of those with normal sinus rhythm. Results: The CNN models could be used to accurately diagnose AF, assess the risk of past AF episodes, and predict the risk of future AF episodes with high accuracy, as shown by areas under the receiver operating characteristic curve of 0.99, 0.86, and 0.85, respectively. Patients who were estimated to have had past AF or predicted to have future AF were at a higher risk of developing stroke, HF hospitalization, MI, and mortality. The ECGs of patients with predicted AF tended to exhibit lower R-wave amplitudes and flattened T waves. Additionally, we observed that the QRS complexes in leads V1, aVL, and aVR were highly weighted in predicting AF in the CNN models. Conclusions: The CNN models were effective for estimating the past and future risk of AF by analyzing 12-lead ECG. Patients with predicted AF had a higher risk of developing stroke, hospitalization for HF, MI, and death. By using this AF prediction model, physicians may be able to identify patients who should be screened for AF and taking action to prevent stroke and manage cardiovascular risk.