Is rivaroxaban associated with lower inpatient costs compared to warfarin among patients with non-valvular atrial fibrillation?
Bibliographic record
Abstract
BACKGROUND: Warfarin has been the mainstay treatment used by patients with a moderate-to-high risk of stroke due to non-valvular atrial fibrillation (NVAF). Unlike rivaroxaban, laboratory monitoring to allow the attainment of the prothrombin time international normalized ratio goal is required with warfarin, thereby potentially increasing a patient's hospitalization costs. OBJECTIVE: To compare hospitalization costs between hospitalized NVAF patients using rivaroxaban versus warfarin in a real-world setting. METHODS: A retrospective claims analysis was conducted using the Premier Perspective Comparative Hospital Database from November 2010 to September 2012. The study included adult patients hospitalized for NVAF after November 2011. Patients using rivaroxaban during hospitalization were matched with up to four warfarin users by propensity score analyses. Hospitalization costs were compared between the matched cohorts using generalized estimating equations. A sub-analysis was performed for patients who were first administered their treatment on day three or later of their hospital stay. Sensitivity analyses were conducted on matched cohorts with a primary diagnosis of AF. RESULTS: The matched cohorts' (2809 rivaroxaban and 11,085 warfarin users) characteristics were well balanced. The mean age of cohorts was 71 years and 49% of patients were female. The average hospitalization cost of rivaroxaban users was $11,993 compared to $13,255 for warfarin users. The cost difference was significantly lower by $1284 (P < 0.001). Patients who were administered rivaroxaban treatment on day three or after incurred significantly lower hospitalization costs (cost difference: $4350; P < 0.001) compared to warfarin users. Rivaroxaban users with a primary diagnosis of AF also had significantly lower costs compared to warfarin users. LIMITATIONS: These included possible inaccuracies or omissions in diagnoses, completeness of baseline characteristics, and a study population that included patients newly initiated on and patients who continued anticoagulant therapy. CONCLUSION: Hospitalization costs for rivaroxaban were significantly lower than those for warfarin in NVAF patients treated with rivaroxaban.
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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 | 0.008 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".