Direct Oral Anticoagulant Use in Chronic Kidney Disease and Dialysis Patients With Venous Thromboembolism: A Systematic Review of Thrombosis and Bleeding Outcomes
Bibliographic record
Abstract
OBJECTIVE: To evaluate how treatment with DOACs for VTE affects thrombosis and bleeding outcomes compared to warfarin in CKD and dialysis patients. DATA SOURCES: A literature search was conducted for studies evaluating VTE and bleeding outcomes with DOAC use in CKD and dialysis patients. Searches conducted through EMBASE, MEDLINE/PubMed, Scopus, and Cochrane Central Register of Controlled Trials, from inception to September 22, 2020. STUDY SELECTION AND DATA EXTRACTION: Randomized controlled trials, cohort studies, and case series with ≥10 patients included. DATA SYNTHESIS: From 7286 studies, nine studies met inclusion criteria. There was no significant difference between DOACs (dabigatran, rivaroxaban, apixaban) and warfarin for reducing recurrent VTE and bleeding events in moderate CKD patients. The risk of overall major bleeding increased when the degree of kidney impairment increased. There was no significant difference between apixaban and warfarin for VTE outcomes in dialysis patients. RELEVANCE TO PATIENT CARE AND CLINICAL PRACTICE: There continues to be a controversial debate whether it may be more beneficial to use DOACs versus warfarin in CKD/dialysis patients with venous thromboembolism (VTE). The risk vs benefit of using DOACs in the CKD/ESKD population should continue to be evaluated for each individual patient. CONCLUSION: Apixaban may be used cautiously as an alternative in acute VTE treatment in severe CKD patients. Insufficient evidence is available to suggest the use of dabigatran and rivaroxaban in this patient population. The benefit of using DOACs in this population for VTE treatment should be weighed against the potential bleeding risk in patients with CKD.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".