Prediction of atrial fibrillation after a stroke event: a systematic review with meta-analysis.

Background: Detecting atrial fibrillation (AF) after stroke is a key component of secondary prevention, but indiscriminate prolonged cardiac monitoring is costly and burdensome. Multivariable prediction models could be used to inform patient selection.
Objective: To determine the performance of available models for predicting AF after a stroke.
Methods: We searched for studies of multivariable models that were derived, validated and/or augmented for prediction of AF in patients with a stroke, using Medline and Embase from inception through 20/09/2024. Discrimination measures for tools with c-statistic data from ≥3 cohorts were pooled by Bayesian meta-analysis, with heterogeneity assessed through a 95% prediction interval (PI). The risk of bias was assessed using the Prediction Model Risk Of Bias tool (PROBAST).
Results: We included 75 studies with 58 prediction models. 66% had a high risk of bias. Fifteen multivariable models were eligible for meta-analysis. Three models showed excellent discrimination: SAFE (c-statistic 0.856, 95% CI 0.796-0.916), SURF (0.815, 95% CI 0.728-0.893), and iPAB (0.888, 95% CI 0.824-0.957). Excluding high-bias studies, only SAFE showed excellent discrimination (0.856, 95% CI 0.800-0.915). No model showed excellent discrimination when limited to external validation or studies with ≥100 AF events. No clinical impact studies were found.
Conclusion: Three of the fifty-eight identified multivariable prediction models for AF after stroke demonstrated excellent statistical performance on meta-analysis. However, prospective validation is required to understand the effectiveness of these models in clinical practice before they can be recommended for inclusion in clinical guidelines.
(Copyright © 2025. Published by Elsevier Inc.)