Evaluating the Effectiveness of Statin Therapy in Reducing the Risk of Ischemic and Hemorrhagic Stroke Among Patients with Atrial Fibrillation: A Meta-Analysis of Randomized Controlled Trials and Observational Studies
Bibliographic record
Abstract
Background: Atrial fibrillation (AF) is a significant risk factor for both ischemic and hemorrhagic strokes. Although statins are widely recognized for their lipid-lowering and anti-inflammatory effects, their specific role in stroke prevention among AF patients remains uncertain. This meta-analysis aims to evaluate the effectiveness of statin therapy in reducing the risk of ischemic and hemorrhagic stroke in individuals with AF. Methods: A systematic search of PubMed, Embase, Cochrane Library, and Scopus was conducted for studies published between January 2000 and March 2024. Eligible studies included randomized controlled trials (RCTs) and observational cohorts that assessed stroke outcomes in AF patients receiving statins versus control. Data were extracted and pooled using a random-effects model. Risk ratios (RRs) and 95% confidence intervals (Cis) were calculated, and heterogeneity was assessed using the I² statistic. Risk of bias was evaluated using the Cochrane Risk of Bias tool and Newcastle-Ottawa Scale. Results: Four studies (three RCTs and one observational cohort) with a combined sample size of over 32,000 participants were included. The pooled analysis demonstrated a statistically significant reduction in stroke risk with statin use (RR: 0.76; 95% CI: 0.64–0.90; I² = 51%). No significant increase in hemorrhagic stroke risk was observed. Most included studies showed low to moderate risk of bias, and funnel plots indicated minimal publication bias.Conclusion: Statin therapy is associated with a significant reduction in overall stroke risk, particularly ischemic stroke, among patients with atrial fibrillation. These findings support the potential role of statins as an adjunctive preventive strategy in AF management. Further high-quality RCTs are warranted to confirm these results and explore optimal statin regimens.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.088 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".