Key result
SAFE model predicts post-stroke AF with excellent discrimination and a ~0.86 C-statistic.
Why the study?
Detecting AF after stroke is essential for secondary prevention, but prolonged cardiac monitoring is costly and burdensome, creating a need to evaluate multivariable prediction models to select patients.
What is the performance of available multivariable models for predicting atrial fibrillation after a stroke?
Meta-Analysis
What is the performance of available multivariable models for predicting atrial fibrillation after a stroke?
While SAFE, SURF, and iPAB models show excellent statistical discrimination for predicting AF after stroke, prospective validation and clinical impact studies are needed before guideline inclusion.
Answers are grounded in this paper and cite their sources.
Supports post-stroke AF stratification with SAFE; leaves open prospective validation before guideline use.
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.
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Helbitz et al. (2025) conducted a meta-analysis in Atrial fibrillation after stroke. Multivariable prediction models (SAFE, SURF, iPAB) was evaluated on Discrimination for predicting atrial fibrillation after a stroke (C statistic 0.856, 95% CI 0.796-0.916). The SAFE multivariable prediction model for atrial fibrillation after stroke demonstrated excellent discrimination (C statistic 0.856; 95% CI 0.796-0.916), even after excluding high-bias studies.
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