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 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.