Comparative Effectiveness and Safety of Rivaroxaban and Warfarin Among Nonvalvular Atrial Fibrillation (NVAF) Patients with Obesity and Polypharmacy in the United States (US)
Bibliographic record
Abstract
INTRODUCTION: Current evidence indicates that rivaroxaban may be a safe and effective alternative to warfarin among patients with nonvalvular atrial fibrillation (NVAF) and obesity. However, evidence regarding the impact of polypharmacy is limited in this population. The present study evaluated the effectiveness and safety of rivaroxaban versus warfarin among NVAF patients with obesity and polypharmacy in the US. METHODS: ) and polypharmacy (≥ 5 medications) initiated on rivaroxaban or warfarin. Inverse probability of treatment weighting (IPTW) was used to adjust for imbalances between groups. Study outcomes were evaluated up to 36 months post-treatment initiation and included the composite of stroke or systemic embolism (stroke/SE) and major bleeding. Subgroup analyses were conducted stratified by polypharmacy category (5-9 or ≥ 10 medications). Outcomes were assessed using Cox proportional hazards regression models with hazard ratios (HR) and 95% confidence intervals (CIs). RESULTS: A total of 7000 and 3920 NVAF patients with obesity and polypharmacy were initiated on rivaroxaban and warfarin, respectively. At 36 months of follow-up, rivaroxaban was associated with a 29% lower risk of stroke/SE relative to warfarin (HR 0.71, 95% CI 0.57, 0.90). Major bleeding risk was not significantly different among rivaroxaban- compared to warfarin-treated patients (HR 0.85, 95% CI 0.70, 1.03). Subgroup analyses yielded results that were largely consistent with the overall polypharmacy analysis. CONCLUSIONS: These results suggest that rivaroxaban is an effective and safe treatment option among NVAF patients with obesity and polypharmacy in a commercially-insured US 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 | 0.003 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".