Meta‐analysis evaluating apixaban in patients with atrial fibrillation and <scp>end‐stage</scp> renal disease requiring dialysis
Bibliographic record
Abstract
Abstract Background Warfarin is considered the primary oral anticoagulant for patients with atrial fibrillation and end‐stage renal disease (ESRD) requiring dialysis. Although warfarin can offer significant stroke prevention in this population, the accompanying major bleeding risks make warfarin nearly prohibitive. Apixaban was shown to be superior to warfarin in preventing stroke or systemic embolism, with a lower risk of bleeding and mortality in a large, randomized trial of individuals with mostly normal renal function but none with ESRD. Methods We systematically reviewed evidence comparing apixaban versus warfarin for atrial fibrillation in this population, and evaluated outcomes of stroke or systemic embolism, and major bleeding using random‐effects models. The main safety outcome was major bleeding, and the main effectiveness outcome was stroke or systemic embolism. Results We found five observational studies of 10 036 patients (2638 receiving apixaban, and 7398 receiving warfarin) meeting inclusion criteria. Pooled analysis demonstrated a significant reduction in major bleeding with apixaban as compared to warfarin (odds ratio [OR] 0.51, 95% confidence interval [CI] 0.42–0.61; p < .0001). Apixaban was also associated with a reduction in intracranial bleeding (OR 0.58, 95% CI 0.37–0.92; p = .02) and in gastrointestinal bleeding (OR 0.61, 95% CI 0.51–0.73; p < .0001). Furthermore, apixaban was associated with a reduction in stroke/systemic embolism (OR 0.64, 95% CI 0.50–0.82; p < .0001). Conclusion Apixaban was associated with superior outcomes and reduced adverse events compared to warfarin in observational studies of patients with atrial fibrillation on dialysis. Randomized controlled studies are needed to confirm these findings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.047 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".