A cost-analysis model for anticoagulant treatment in the hospital setting
Bibliographic record
Abstract
BACKGROUND: Rivaroxaban is the first oral factor Xa inhibitor approved in the US to reduce the risk of stroke and blood clots among people with non-valvular atrial fibrillation, treat deep vein thrombosis (DVT), treat pulmonary embolism (PE), reduce the risk of recurrence of DVT and PE, and prevent DVT and PE after knee or hip replacement surgery. The objective of this study was to evaluate the costs from a hospital perspective of treating patients with rivaroxaban vs other anticoagulant agents across these five populations. METHODS: An economic model was developed using treatment regimens from the ROCKET-AF, EINSTEIN-DVT and PE, and RECORD1-3 randomized clinical trials. The distribution of hospital admissions used in the model across the different populations was derived from the 2010 Healthcare Cost and Utilization Project database. The model compared total costs of anticoagulant treatment, monitoring, inpatient stay, and administration for patients receiving rivaroxaban vs other anticoagulant agents. The length of inpatient stay (LOS) was determined from the literature. RESULTS: Across all populations, rivaroxaban was associated with an overall mean cost savings of $1520 per patient. The largest cost savings associated with rivaroxaban was observed in patients with DVT or PE ($6205 and $2742 per patient, respectively). The main driver of the cost savings resulted from the reduction in LOS associated with rivaroxaban, contributing to ∼90% of the total savings. Furthermore, the overall mean anticoagulant treatment cost was lower for rivaroxaban vs the reference groups. LIMITATIONS: The distribution of patients across indications used in the model may not be generalizable to all hospitals, where practice patterns may vary, and average LOS cost may not reflect the actual reimbursements that hospitals received. CONCLUSION: From a hospital perspective, the use of rivaroxaban may be associated with cost savings when compared to other anticoagulant treatments due to lower drug cost and shorter LOS associated 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".