Subclinical atrial fibrillation (AF) is one of the pathogeneses of embolic stroke. Detection of occult AF and providing proper anticoagulant treatment is an important way to prevent stroke recurrence. The purpose of this study was to determine whether an artificial intelligence (AI) model can assess occult AF using 24-h Holter monitoring during normal sinus rhythm.
This study is a retrospective cohort study that included those who underwent Holter monitoring. The primary outcome was identifying patients with AF analyzed with an AI model using 24-h Holter monitoring without AF documentation. We trained the AI using a Holter monitor, including supraventricular ectopy (SVE) events (setting 1) and excluding SVE events (setting 2). Additionally, we performed comparisons using the SVE burden recorded in Holter annotation data.
The area under the receiver operating characteristics curve (AUROC) of setting 1 was 0.85 (0.83–0.87) and that of setting 2 was 0.84 (0.82–0.86). The AUROC of the SVE burden with Holter annotation data was 0.73. According to the diurnal period, the AUROCs for daytime were 0.83 (0.78–0.88) for setting 1 and 0.83 (0.78–0.88) for setting 2, respectively, while those for nighttime were 0.85 (0.82–0.88) for setting 1 and 0.85 (0.80–0.90) for setting 2.
We have demonstrated that an AI can identify occult paroxysmal AF using 24-h continuous ambulatory Holter monitoring during sinus rhythm. The performance of our AI model outperformed the use of SVE burden in the Holter exam to identify paroxysmal AF. According to the diurnal period, nighttime recordings showed more favorable performance compared to daytime recordings.