AI-based atrial fibrillation risk prediction from single-lead sinus rhythm ECGs identified high-risk patients who had a 2.1-fold higher risk of ischemic stroke or systemic embolism at 4 years.
Cohort (n=11,024)
Yes
Does deep learning-based prediction of high atrial fibrillation risk from single-lead sinus rhythm ECGs identify patients at increased risk of ischemic stroke or systemic embolism?
AI-based prediction of atrial fibrillation from single-lead sinus rhythm ECGs can identify patients at significantly increased risk of future ischemic stroke or systemic embolism.
Effect estimate: 2.1-fold higher risk at 4 years
Abstract Background and aims Atrial fibrillation (AF) is a major cause of ischemic stroke. Previous studies have shown that Artificial Intelligence (AI) can predict incident AF from single-lead sinus rhythm ECGs by detecting electrical signatures of atrial disease. However, the clinical actionability of early AF detection remains debated. In this registry-based study, we tested the hypothesis that stroke incidence differs between patients predicted at high- versus low-risk of AF by a previously published deep learning (DL) model. Methods A French national database was used to construct a retrospective cohort of 11,024 patients who underwent ambulatory ECG monitoring without detected AF (Table 1). Patients were followed up to 5 years (mean follow-up 1.6 years) for ischemic stroke or systemic embolism. The high- versus low-risk threshold was determined on an independent cohort. The cumulative stroke incidence was compared between groups. Results Patients classified as high AF risk by the model (age: 70.7 ± 13 yo; sex: 46.8 % female) exhibited a 1.7-fold and 2.1-fold higher risk of ischemic stroke or systemic embolism at 1 and 4 years, respectively (Figure 1). Saliency maps highlight ECG regions contributing most strongly to AF risk prediction (Figure 2). Conclusions AI-based AF risk prediction from single-lead sinus rhythm ECGs identifies patients at significantly increased risk of ischemic stroke. These findings support prospective studies evaluating anticoagulant therapy for primary stroke prevention in patients in sinus rhythm with high predicted AF-related stroke risk. Conflict of interest Thomas Proudhon: Philips employee; Amine Keldhouni: Philips employee; Clémence Blanc: nothing to disclose; Baptiste Lefebvre: Philips employee; Laurent Fiorina: medical expert for Philips. Table 1 - belongs to Methods Figure 1 - belongs to Results Figure 2 - belongs to Conclusions
Proudhon et al. (2026) conducted a cohort in Atrial fibrillation risk / Ischemic stroke (n=11,024). Deep learning model for AF risk prediction (High risk) vs. Low predicted AF risk was evaluated on Ischemic stroke or systemic embolism (2.1-fold higher risk at 4 years). AI-based atrial fibrillation risk prediction from single-lead sinus rhythm ECGs identified high-risk patients who had a 2.1-fold higher risk of ischemic stroke or systemic embolism at 4 years.