Health-care Cost Impact of Continued Anticoagulation With Rivaroxaban vs Aspirin for Prevention of Recurrent Symptomatic VTE in the EINSTEIN-CHOICE Trial Population
Bibliographic record
Abstract
BACKGROUND: Using data from the Reduced-Dose Rivaroxaban in the Long-Term Prevention of Recurrent Symptomatic Venous Thromboembolism (EINSTEIN-CHOICE) trial, this study assessed cost impact of continued anticoagulation therapy with rivaroxaban vs aspirin. METHODS: Total health-care costs (2016 USD) associated with rivaroxaban and aspirin were calculated as the sum of clinical event costs and drug costs from a US managed care perspective. Clinical event costs were calculated by multiplying event rate by cost of care. One-year Kaplan-Meier clinical event rates for recurrent pulmonary embolism, recurrent DVT, all-cause mortality, and bleeding were obtained from EINSTEIN-CHOICE. Cost of care was determined by literature review. Drug costs were the product of drug price (wholesale acquisition cost) and treatment duration. A one-way sensitivity analysis was conducted. RESULTS: Rivaroxaban users had lower per patient per month (PPPM) clinical event costs compared with aspirin users ($123, $243, and $381 for rivaroxaban 10 mg, rivaroxaban 20 mg, and aspirin, respectively). However, vs aspirin, PPPM total health-care costs were $24 higher for patients treated with rivaroxaban 10 mg ($143 higher for rivaroxaban 20 mg) due to higher cost of rivaroxaban. With a 15% discount for rivaroxaban 10 mg, the lower cost of clinical events for the rivaroxaban-treated patients more than fully offset the higher drug costs, and yielded a $19 lower total health-care cost. CONCLUSIONS: Continued therapy with rivaroxaban 10 and 20 mg vs aspirin was associated with lower clinical event costs but higher total health-care costs; with a 15% drug discount rivaroxaban 10 mg had lower total health-care costs than aspirin.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".