Healthcare Resource Utilization and Costs of Rivaroxaban Versus Warfarin Among Non-valvular Atrial Fibrillation (NVAF) Patients with Diabetes in a US Population
Bibliographic record
Abstract
INTRODUCTION: The healthcare resource utilization (HRU) and costs of oral anticoagulant-naïve patients with non-valvular atrial fibrillation (NVAF) and diabetes initiated on rivaroxaban or warfarin in the United States (US) has not been previously evaluated. METHODS: Data Mart Database (1 January, 2012 to 30 September, 2021) to evaluate the HRU and costs of adult patients with NVAF and diabetes newly initiated on rivaroxaban or warfarin (on or after January 2013). Inverse probability of treatment weighting (IPTW) was used to adjust for confounding between cohorts. HRU and costs (USD 2021) were assessed per patient-year (PPY) post-treatment initiation. Weighted cohorts were compared using rate ratios (RR) from Poisson regression models, odds ratios (OR) from logistic regression models, and cost differences; 95% confidence intervals (CI) and p values were generated using non-parametric bootstrap procedures. RESULTS: After IPTW, 17,881 and 19,274 patients initiated on rivaroxaban and warfarin were included, respectively (mean age: 73 years; 40% female). During 12 months of follow-up, the rivaroxaban cohort had lower all-cause HRU PPY across all components, including lower rates of inpatient stays (RR: 0.84, 95% CI 0.81, 0.88), outpatient visits (RR: 0.67, 95% CI 0.66, 0.68), and 30 day hospital readmission (OR: 0.75, 95% CI 0.66, 0.83; all p < 0.001) compared to the warfarin cohort. Moreover, rivaroxaban was associated with medical cost savings PPY (mean cost difference: - $9306, 95% CI - $11,769, - $6607), which compensated for higher pharmacy costs relative to warfarin (mean cost difference: $5518, 95% CI $5193, $5839), resulting in significantly lower all-cause total healthcare costs for rivaroxaban versus warfarin (mean cost difference: - $3788, 95% CI - $6258, - $1035; all p < 0.001). CONCLUSION: Among NVAF patients with diabetes in a real-world US setting, rivaroxaban was associated with lower healthcare costs compared to warfarin.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".