Effectiveness and safety of rivaroxaban vs. apixaban in patients with atrial fibrillation and peripheral artery disease
Bibliographic record
Abstract
AIMS: To assess whether rivaroxaban is associated with a decreased risk of major adverse limb events (MALE), stroke, systemic embolism (SE), and major bleeding (MB) among patients with non-valvular atrial fibrillation (NVAF) and peripheral artery disease (PAD), compared with apixaban. METHODS AND RESULTS: We conducted a population-based cohort study using the UK Clinical Practice Research Datalink. Patients aged ≥45 years with incident NVAF and PAD who initiated rivaroxaban or apixaban between 2013 and 2021 were included. Primary effectiveness outcomes were MALE, and a composite of ischaemic stroke, transient ischaemic attack (TIA), or SE. The primary safety outcome was MB. The risk of major cardiovascular events (MACE) was assessed as a secondary outcome. Confounding was addressed using propensity score fine stratification and weighting. Weighted Cox proportional hazards models estimated hazard ratios (HRs) with 95% confidence intervals (CIs). The cohort included 6170 new users of rivaroxaban and 9990 new users of apixaban (44% female; mean [SD] age 78.5 [9.2] years). Incidence rates were similar for MALE (6.7 vs. 5.6/1000 person-years; adjusted HR (aHR): 1.20; 95% CI 0.87-1.65), stroke/TIA/SE (24.5 vs. 21.3/1000 person-years; aHR: 1.15; 95% CI 0.97-1.36), and MACE (40.1 vs. 35.9 per 1000 person-years; aHR 1.10: 95% CI 0.94-1.28). Major bleeding rates were higher with rivaroxaban (46.1 vs. 29.8/1000 person-years; aHR: 1.55; 95% CI 1.36-1.77). CONCLUSION: In patients with NVAF and PAD, rivaroxaban was associated with a similar risk of MALE and stroke/TIA/SE, but a higher risk of MB compared with apixaban. These findings support apixaban as a potentially safer anticoagulant in this high-risk 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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".