Cost–utility analysis of apixaban compared with usual care for primary thromboprophylaxis in ambulatory patients with cancer
Bibliographic record
Abstract
BACKGROUND: Apixaban (2.5 mg) taken twice daily has been shown to substantially reduce the risk of venous thromboembolism (VTE) compared with placebo for the primary thromboprophylaxis of ambulatory patients with cancer who are starting chemotherapy and are at intermediate-to-high risk of VTE. We aimed to compare the health system costs and health benefits associated with primary thromboprophylaxis using apixaban with those associated with the current standard of care (where no primary thromboprophylaxis is given), from the perspective of Canada's publicly funded health care system in this subpopulation of patients with cancer over a lifetime horizon. METHODS: We performed a cost-utility analysis to estimate the incremental cost per quality-adjusted life-year (QALY) gained with primary thromboprophylaxis using apixaban. We obtained baseline event rates and the efficacy of apixaban from the Apixaban for the Prevention of Venous Thromboembolism in High-Risk Ambulatory Cancer Patients (AVERT) trial on apixaban prophylaxis. We estimated relative risk for bleeding, risk of complications associated with VTE treatment, mortality rates, costs and utilities from other published sources. RESULTS: Over a lifetime horizon, apixaban resulted in lower costs to the health system (Can$7902.98 v. Can$14 875.82) and an improvement in QALYs (9.089 v. 9.006). The key driver of cost-effectiveness results was the relative risk of VTE as a result of apixaban. Results from the probabilistic analysis showed that at a willingness to pay of Can$50 000 per QALY, the strategy with the highest probability of being most cost-effective was apixaban, with a probability of 99.87%. INTERPRETATION: We found that apixaban is a cost-saving option for the primary thromboprophylaxis of ambulatory patients with cancer who are starting chemotherapy and are at intermediate-to-high risk of VTE.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".