Effectiveness, safety, and healthcare costs associated with rivaroxaban versus warfarin among venous thromboembolism patients with obesity: a real-world study in the United States
Bibliographic record
Abstract
Prior observational studies suggest rivaroxaban is safe and effective among patients with morbid obesity who suffered a venous thromboembolism (VTE) event, but existing data are more limited in the broader population of VTE patients with obesity. This study assessed VTE recurrence, major bleeding, healthcare resource utilization, and healthcare costs among VTE patients with obesity who received rivaroxaban versus warfarin. VTE patients with obesity who initiated rivaroxaban or warfarin after a first VTE (index date) were identified from the IQVIA PharMetrics® Plus database (01/02/2011-09/30/2019). The follow-up period spanned from the index date until health plan disenrollment, end of data availability, cancer diagnosis/treatment, end of the 12 month post-index period, or (for the analysis of major bleeding) anticoagulant discontinuation or switch. Patient characteristics were balanced using inverse probability of treatment weighting. The weighted rivaroxaban (N = 8666) and warfarin cohorts (N = 5946) were well balanced (mean age = 51 years, females = 52%). Over a 9.6 months mean observation period, rivaroxaban users had a significantly lower risk of VTE recurrence [7.0% vs. 8.2%, HR(95% CI) = 0.85(0.75;0.97)] and a similar risk of major bleeding [4.1% vs. 3.6%, HR(95% CI) = 1.11(0.89;1.37)] relative to warfarin users at 12 months. Relative to warfarin users, rivaroxaban users had significantly fewer all-cause outpatient visits [RR(95% CI) = 0.71(0.70;0.74)]. The higher pharmacy costs incurred by rivaroxaban recipients (cost difference = $1252) were offset by lower medical costs (cost difference = - $2515, all p < 0.05) compared with warfarin recipients. Our findings suggest that rivaroxaban is safe and effective versus warfarin, and associated with lower medical costs among VTE patients with obesity.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".