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

Abstract

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 selection of patients.

Objective: This study aimed 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, or augmented for prediction of AF in patients with a stroke, using MEDLINE and Embase from inception through September 20, 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. The risk of bias was assessed with the Prediction model Risk Of Bias Assessment 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% confidence interval [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 58 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.

Keywords: AF; Atrial fibrillation; Prediction; Prevention; Stroke.