Artificial intelligence-enabled mobile electrocardiograms for event prediction in paroxysmal atrial fibrillation

BACKGROUND Paroxysmal atrial fibrillation (AF) often eludes early diagnosis, resulting in significant morbidity and mortality. Artificial intelligence (AI) has been used to predict AF from sinus rhythm electrocardiograms (ECGs), but AF prediction using sinus rhythm mobile electrocardiograms (mECG) remains unexplored.OBJECTIVE The purpose of this study was to investigate the utility of AI to predict AF events prospectively and retrospectively using sinus rhythm mECG data.METHODS We trained a neural network to predict AF events from sinus rhythm mECGs obtained from users of the Alivecor KardiaMo-bile 6L device. We tested our model on sinus rhythm mECGs within & PLUSMN;0-2 days, & PLUSMN;3-7 days, and & PLUSMN;8-30 days from AF events to deter-mine the optimal screening window. Finally, we tested our model on mECGs from before an AF event to determine whether AF can be predicted prospectively.RESULTS We included 73,861 users with 267,614 mECGs (mean age 58.14 years; 35% women). Users with paroxysmal AF contributed 60.15% of mECGs. Model performance on the test set comprising control and study samples across all windows of interest showed an area under the curve (AUC) score of 0.760 (95% confidence inter-val [CI] 0.759-0.760), sensitivity of 0.703 (95% CI 0.700-0.705), specificity of 0.684 (95% CI 0.678-0.685), and accuracy of 69.4% (95% CI 0.692-0.700). Model performance was better on & PLUSMN;0-2 day samples (sensitivity 0.711; 95% CI 0.709-0.713) and worse on the & PLUSMN;8-30 day window (sensitivity 0.688; 95% CI 0.685- 0.690), with performance on the & PLUSMN;3-7 day window falling in be-tween (sensitivity 0.708; 95% CI 0.704-0.710).CONCLUSION Neural networks can predict AF using a widely scal-able and cost-effective mobile technology prospectively and retro-spectively.