使用基于心电图的深度学习预测应激相关心房颤动后的复发和结局。
Haimovich, J.S., Friedman, S., Reeder, C., Dsouza, V., Sommers, T., Usuda, K., Kany, S., Benjamin, E.J., Lubitz, S.A., Maddah, M., Ellinor, P.T., & Khurshid, S. (2026). Predicting Recurrence and Outcomes After Stressor-Associated Atrial Fibrillation Using ECG-Based Deep Learning.. J Am Heart Assoc. https://doi.org/10.1161/JAHA.125.047146
Haimovich JS, Friedman S, Reeder C, Dsouza V, Sommers T, Usuda K, et al. Predicting Recurrence and Outcomes After Stressor-Associated Atrial Fibrillation Using ECG-Based Deep Learning.. J Am Heart Assoc. 2026; doi: 10.1161/JAHA.125.047146
Haimovich JS, Friedman S, Reeder C, et al. Predicting Recurrence and Outcomes After Stressor-Associated Atrial Fibrillation Using ECG-Based Deep Learning.[J]. J Am Heart Assoc. 2026. DOI: 10.1161/JAHA.125.047146.
@article{haimovich2026,
author = {Julian S Haimovich and Samuel Friedman and Christopher Reeder and Valentina Dsouza and Thomas Sommers and Keisuke Usuda and Shinwan Kany and Emelia J Benjamin and Steven A Lubitz and Mahnaz Maddah and Patrick T Ellinor and Shaan Khurshid},
title = {Predicting Recurrence and Outcomes After Stressor-Associated Atrial Fibrillation Using ECG-Based Deep Learning.},
journal = {J Am Heart Assoc},
year = {2026},
doi = {10.1161/JAHA.125.047146},
note = {PMID: 41859908},
}
TY - JOUR AU - Julian S Haimovich AU - Samuel Friedman AU - Christopher Reeder AU - Valentina Dsouza AU - Thomas Sommers AU - Keisuke Usuda AU - Shinwan Kany AU - Emelia J Benjamin AU - Steven A Lubitz AU - Mahnaz Maddah AU - Patrick T Ellinor AU - Shaan Khurshid TI - Predicting Recurrence and Outcomes After Stressor-Associated Atrial Fibrillation Using ECG-Based Deep Learning. T2 - J Am Heart Assoc PY - 2026 DO - 10.1161/JAHA.125.047146 AN - PMID:41859908 ER -
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. 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. 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]; 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.