Predicting Incident Atrial Fibrillation after Stroke: A Scoping Review of Clinical Scores, Biomarkers, and AI-Enhanced Strategies

BACKGROUND: Incident atrial fibrillation (AF) after ischemic stroke is frequently underdetected despite its implications for anticoagulation and prevention of recurrent events. Multiple strategies - clinical risk scores, serum biomarkers, imaging markers, digital electrocardiographic (ECG) monitoring, and artificial intelligence (AI)-based models - have been proposed to predict or detect post-stroke AF, but their comparative performance and applicability in routine practice remain uncertain. SUMMARY: We conducted a scoping review following PRISMA-ScR guidelines to map evidence on tools for predicting or detecting post-stroke AF in adults without known AF at baseline. We synthesized studies on clinical prediction scores, circulating biomarkers, imaging-derived markers, digital monitoring technologies, and AI-enhanced predictive models. Natriuretic peptides, particularly NT-proBNP and mid-regional pro-atrial natriuretic peptide, demonstrate the most consistent association with incident AF and may improve risk stratification. Imaging markers such as left atrial dimensions and radiomic features show potential but lack robust validation. Digital monitoring modalities - including handheld ECG devices, wearable patch monitors, smartwatches, and implantable loop recorders - differ substantially in diagnostic yield, cost, and feasibility across settings. AI-based approaches using electronic health record data or ECG signals achieve high discrimination in development cohorts but require prospective clinical evaluation. Based on the evidence landscape, we outline a tiered diagnostic pathway integrating clinical scores, biomarker-guided triage, and stepwise ECG monitoring adapted to resource availability. KEY MESSAGES: Optimal post-stroke AF detection requires a multimodal strategy rather than isolated tools. Natriuretic peptides are the most validated biomarkers. Digital and AI-driven tools may broaden detection capacity but need external validation. A tiered diagnostic pathway may optimize diagnostic yield and resource allocation.