Management of anticoagulation in patients with atrial fibrillation and renal dysfunction: A systematic review
Bibliographic record
Abstract
BACKGROUND: Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with significant morbidity and mortality, particularly in patients with concomitant renal dysfunction. Anticoagulation therapy reduces the risk of thromboembolic complications in AF but presents challenges in patients with renal impairment due to altered pharmacokinetics and increased bleeding risk. AIM: To support clinicians in navigating the complexities of anticoagulation in this high-risk population, ensuring optimal outcomes. METHODS: The present review followed PRISMA guidelines. Data extraction was conducted using a standardized template that captured key study characteristics: Population demographics, renal function metrics, anticoagulant dosing strategies, and primary and secondary outcomes. For quality assessment, we employed the Cochrane Risk of Bias 2.0 tool for randomized controlled trials. Observational studies were appraised using the Newcastle-Ottawa Scale. RESULTS: We analyze data from 16 studies to provide recommendations on optimal anticoagulation strategies, balancing thrombotic and bleeding risks. Current evidence supports the preferential use of apixaban in moderate chronic kidney disease and cautiously in end-stage renal disease, emphasizing the importance of individualized therapy. CONCLUSION: The management of anticoagulation in AF patients with renal dysfunction is challenging but critical for reducing stroke risk.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".