Safety and efficacy of direct oral anticoagulants in chronic kidney disease: a meta-analysis
Bibliographic record
Abstract
Background: Direct oral anticoagulants (DOACs) have emerged as the first-line therapy for venous thromboembolism and stroke prophylaxis in atrial fibrillation. As DOACs are partially excreted renally, their safety in patients with chronic kidney disease (CKD) is unclear. Objectives: To synthesize primary evidence on the safety profile of DOACs in patients with CKD. Methods: We searched MEDLINE and Embase from inception to June 2023 for randomized and nonrandomized cohort studies comparing DOACs with vitamin K antagonists (VKAs) in CKD patients. Screening and data collection were conducted in duplicate. The primary safety outcome was major bleeding, defined by International Society on Thrombosis and Haemostasis criteria, stratified by CKD severity. Meta-analysis was done using the Mantel-Haenszel random-effects model, presented as odds ratios (ORs) with corresponding 95% CIs. Results: = 66,898) were included. DOACs reduced major bleeding compared with VKAs in all subgroups (stage 4: OR, 0.73; 95% CI, 0.58, 0.93; stage 5/renal replacement therapy: OR, 0.70; 95% CI, 0.50, 0.98; stage unspecified: OR, 0.72; 95% CI, 0.63, 0.83). Apixaban and rivaroxaban both reduced major bleeding in stage 5/renal replacement therapy patients (apixaban: OR, 0.66; 95% CI, 0.52, 0.85; rivaroxaban: OR, 0.58; 95% CI, 0.35, 0.94). Conclusion: In this meta-analysis, DOACs reduced major bleeding compared with VKAs in stage 4, stage 5/renal replacement therapy, and CKD stage unspecified patients. Future analysis should evaluate the impact of specific DOACs and dosage on safety and efficacy in this population.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.062 |
| 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".