Comparative Effectiveness and Safety of Direct Oral Anticoagulants versus Warfarin in Patients with Atrial Fibrillation and Stage III Chronic Kidney Disease
Bibliographic record
Abstract
Aim: The effectiveness and safety of direct oral anticoagulants (DOACs) in atrial fibrillation (AF) patients with stage III chronic kidney disease (CKD) are still subject to debate. We therefore assessed and compared the effectiveness and safety of DOACs vs. warfarin in this population. Methods: A cohort of patients with an inpatient or outpatient code for AF and stage III CKD who were newly prescribed an oral anticoagulant (OAC) was created using administrative databases from the Quebec province of Canada between 2013 and 2017. The primary effectiveness outcome was a composite of ischemic stroke, systemic embolism, and death, whereas the primary safety outcome was a composite of major bleeding within a year of DOAC vs. warfarin initiation. Treatment groups were compared in an on-treatment analysis using inverse probability of treatment weighting and Cox proportional hazards. Results. A total of 8,899 included patients filled a new OAC claim: 3,335 for warfarin and 5,564 DOACs. Compared with warfarin, rivaroxaban 15 mg and 20 mg presented a similar effectiveness and safety composite risk. Apixaban 5.0 mg was associated with a lower effectiveness composite risk (Hazard ratio [HR] 0.76; 95% confidence interval [CI] 0.65–0.88) and a similar safety risk (HR 0.94; 95% CI 0.66–1.35), whereas apixaban 2.5 mg was associated with a similar effectiveness composite (HR 1.00; 95% CI 0.79–1.26) and a lower safety risk (HR 0.65; 95% CI 0.43–0.99). Conclusion: In comparison with warfarin, rivaroxaban and apixaban appear to be effective and safe in AF patients with stage III 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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".