Arrhythmias & electrophysiology
Prediction of atrial fibrillation after a stroke event: A systematic review with meta-analysis
Authors' 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.
Study context
- Population
- Patients with a stroke; studies of multivariable models derived, validated, or augmented for prediction of atrial fibrillation (AF)studies included, with 58 prediction models, 15 models eligible for meta-analysis
- Participants
- 75 studies
- Comparison
- Discrimination of multivariable prediction models for AF after stroke
- Outcome
- Risk of bias
Methodological detailsbayesian meta-analysis, risk of bias assessed with PROBAST
- Bayesian meta-analysis
- Risk of bias assessed with PROBAST
Pooled result
As reported in the abstract
High risk of bias
66%
Further pooled results
C statistic
| Outcome | SAFE | SURF | iPAB |
|---|---|---|---|
| Discrimination (C statistic)Three models showed excellent discrimination | 0.856 (95% CI 0.796–0.916) | 0.815 (95% CI 0.728–0.893) | 0.888 (95% CI 0.824–0.957) |
| Discrimination excluding high-bias studies | 0.856 (95% CI 0.800–0.915) | — not stated in the abstract | — not stated in the abstract |
— not stated in the abstract
Discrimination excluding high-bias studies
- Only SAFE showed excellent discrimination
Discrimination limited to external validation or studies with ≥100 AF events
- No model showed excellent discrimination
Clinical impact studies
- No clinical impact studies were found
Original publication
Prediction of atrial fibrillation after a stroke event: A systematic review with meta-analysis. Heart Rhythm. Published 24 January 2025. doi:10.
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