Comparing DOACs with warfarin in AF patients with chronic kidney disease or valvular disease: A systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Objective To analyze the safety and efficacy of different direct oral anticoagulant agents (DOACs) compared to warfarin in patients with concomitant atrial fibrillation (AF) and valvular disease or concomitant AF and chronic kidney disease (CKD). Methods We conducted literature searches in MEDLINE, Embase, and EBM Reviews to examine randomized-controlled trials (RCTs) and non-RCTs that included the aforementioned patient populations treated with warfarin or DOAC (rivaroxaban, dabigatran, apixaban, or edoxaban) and assessed outcomes of bleeding, stroke, or systemic/arterial thromboembolism. Meta-analysis was performed for eligible studies using the Mantel-Haenszel method random-effects model. Results 3,172 studies were screened and 154 studies were selected after two levels of screening. Meta-analysis showed that, in patients with concomitant AF and CKD, DOAC was associated with reduced bleeding in non-RCTs (OR 0.65, 95% Cl [0.49, 0.86], p=0.003), particularly in more severe CKD (eGFR < 60mL/min/1.73m 2 ). Apixaban in particular was associated with reduced bleeding (OR 0.52, 95% Cl [0.44, 0.63], p<0.00001) and stroke incidence (OR 0.60, 95% Cl [0.41, 0.87], p=0.007). In patients with concomitant AF and valvular disease, DOAC was associated with reduced bleeding (OR 0.75, 95% CI [0.57, 0.97], p=0.03) and stroke incidence (OR 0.66, 95% CI [0.47, 0.93], p=0.02) in non-RCTs. Conclusion Our study studied populations that are typically excluded from large-scale anticoagulation studies and our findings suggest that DOACs may be superior to warfarin both in the prevention of thromboembolic event and in the reduction of bleeding risks in patients with concomitant CKD or valvular disease.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.043 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".