Predicting Recurrence and Outcomes After Stressor‐Associated Atrial Fibrillation Using ECG‐Based Deep Learning

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Abstract
Background: Stressor‐associated atrial fibrillation (AF) refers to new‐onset AF that occurs with a reversible, acute stressor. Identifying individuals at highest risk for AF recurrence is essential to guide management. Although clinical factors have shown limited value, the utility of contemporary artificial intelligence (AI)‐enabled models using the 12‐lead ECG to estimate recurrence risk remains unknown. Methods: We retrospectively analyzed consecutive primary care and cardiology patients with stressor‐associated AF occurring during hospitalization. We quantified the cumulative incidence of recurrence accounting for death as a competing risk. We investigated the relationship between time‐varying recurrence and a composite end point of AF‐related adverse events (stroke, heart failure, all‐cause death) using Cox models. We then developed and validated a penalized regression model to predict recurrence using clinical factors, stressor type, and AF risk estimates from a previously validated ECG‐based AI model. Results: We analyzed 3371 patients with stressor‐associated AF (mean age, 69±12 years; 40% women). Over a median of 3.7 years (interquartile range, 1.8–7.2), the 10‐year cumulative incidence of AF recurrence was 41% (95% CI, 39–44). AF recurrence was strongly associated with AF‐related adverse events (hazard ratio, 2.24 [95% CI, 1.81–2.76]). A model incorporating clinical factors, stressor type, and ECG‐based AI model AF risk estimates (clinical–AI) discriminated AF recurrence (area under the receiver operating characteristic curve, 0.768 [95% CI, 0.707–0.830]) favorably compared with clinical features (area under the receiver operating characteristic curve, 0.707 [95% CI, 0.642–0.772]; P <0.05). Conclusions: AF recurrence rates following stressor‐associated AF are considerable and are associated with substantially higher risk of adverse cardiovascular events. Models incorporating ECG‐based AI risk estimates may prioritize individuals for intensive monitoring and preventive interventions.

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