AI-Assisted Workflow for Occult Atrial Fibrillation Detection After Ischemic Stroke: A Prospective Randomized Trial
AI-Assisted Workflow for Occult Atrial Fibrillation Detection After Ischemic Stroke: A Prospective Randomized Trial
We hypothesize that an AI-guided AF risk stratification approach, particularly when combined with intensified rhythm monitoring using wearable devices and extended ECG patches, will significantly increase AF detection rates compared with standard care. By enabling earlier identification of patients who may benefit from anticoagulation therapy, this strategy has the potential to improve clinical outcomes while minimizing unnecessary exposure to anticoagulant-related bleeding risks. Ultimately, this trial seeks to provide robust clinical evidence supporting the integration of AI-assisted ECG analysis into routine post-stroke care, advancing precision medicine and optimizing resource allocation for patients with ischemic stroke.
This study is designed as a multicenter, prospective, randomized controlled trial enrolling adult patients hospitalized for acute ischemic stroke who demonstrate sinus rhythm on admission 12-lead ECG. Participants will be randomized to either an AI-assisted care group or a usual-care control group, and further allocated to active or standard rhythm monitoring strategies. The primary endpoint is the incidence of newly diagnosed AF or atrial flutter within six months, defined by electrocardiographic documentation. Secondary and exploratory endpoints include the rate of oral anticoagulant initiation, AF burden metrics, recurrent embolic events, bleeding complications, major adverse cardiovascular events, cognitive outcomes, and all-cause mortality.
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