Predicting Incident Atrial Fibrillation after Stroke: A Scoping Review of Clinical Scores, Biomarkers, and AI-Enhanced Strategies
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".