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Published on: February 26, 2013
Prediction of atrial fibrillation after a stroke event: A systematic review with meta-analysis
Anna Helbitz1, Mohammad Haris2, Tanina Younsi3
1Faculty of Medicine and Health, University of Leeds, Leeds, United Kingdom.
Insights
Predicting atrial fibrillation (AF) after stroke is crucial for secondary prevention. This study evaluated multivariable models, finding three (SAFE, SURF, iPAB) showed excellent performance, though further validation is needed.
Area of Science:
- Cardiology
- Neurology
- Medical Informatics
Background:
- Detecting atrial fibrillation (AF) post-stroke is vital for secondary prevention.
- Prolonged cardiac monitoring is often costly and burdensome.
- Multivariable prediction models can optimize patient selection for monitoring.
Purpose of the Study:
- To assess the performance of existing multivariable models for predicting AF in stroke patients.
- To identify reliable tools for guiding secondary stroke prevention strategies.
Main Methods:
- Systematic search of MEDLINE and Embase for studies on AF prediction models in stroke patients.
- Bayesian meta-analysis of discrimination measures (C statistic) from eligible models.
- Assessment of risk of bias using the PROBAST tool.
Main Results:
- 75 studies identified 58 unique prediction models; 66% had high bias.
- Three models (SAFE, SURF, iPAB) demonstrated excellent discrimination in meta-analysis.
- Only SAFE maintained excellent discrimination when high-bias studies were excluded; no models excelled in external validation or large AF event cohorts.
Conclusions:
- Three multivariable models show excellent statistical performance for AF prediction post-stroke.
- Prospective validation is essential to confirm clinical utility and guide guideline recommendations.
- Further research is needed to establish the real-world effectiveness of these AF prediction models.
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.
