Comparisons between Novel Oral Anticoagulants and Vitamin K Antagonists in Patients with CKD
Bibliographic record
Abstract
Novel oral anticoagulants (NOACs) (rivaroxaban, dabigatran, apixaban) have been approved by international regulatory agencies to treat atrial fibrillation and venous thromboembolism in patients with kidney dysfunction. However, altered metabolism of these drugs in the setting of impaired kidney function may subject patients with CKD to alterations in their efficacy and a higher risk of bleeding. This article examined the efficacy and safety of the NOACs versus vitamin K antagonists (VKAs) for atrial fibrillation and venous thromboembolism in patients with CKD. A systematic review and meta-analyses of randomized controlled trials were conducted to estimate relative risk (RR) with 95% confidence interval (95% CIs) using a random-effects model. MEDLINE, Embase, and the Cochrane Library were searched to identify articles published up to March 2013. We selected published randomized controlled trials of NOACs compared with VKAs of at least 4 weeks' duration that enrolled patients with CKD (defined as creatinine clearance of 30-50 ml/min) and reported data on comparative efficacy and bleeding events. Eight randomized controlled trials were eligible. There was no significant difference in the primary efficacy outcomes of stroke and systemic thromboembolism (four trials, 9693 participants; RR, 0.64 [95% CI, 0.39 to 1.04]) and recurrent thromboembolism or thromboembolism-related death (four trials, 891 participants; RR, 0.97 [95% CI, 0.43 to 2.15]) with NOACs versus VKAs. The risk of major bleeding or the combined endpoint of major bleeding or clinically relevant nonmajor bleeding (primary safety outcome) (eight trials, 10,616 participants; RR 0.89 [95% CI, 0.68 to 1.16]) was similar between the groups. The use of NOACs in select patients with CKD demonstrates efficacy and safety similar to those with VKAs. Proactive postmarketing surveillance and further studies are pivotal to further define the rational use of these agents.
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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 | 0.019 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.021 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".