Cost‐utility analysis of emicizumab for the treatment of severe hemophilia A patients in Canada
Bibliographic record
Abstract
INTRODUCTION: EHL FVIII products and emicizumab provide clinicians with other prophylactic options for treating hemophilia A, however, it is unclear if emicizumab is a cost-saving option. The objective of this study is to estimate the health and economic effects of using prophylactic EHL FVIII, SHL FVIII, and emicizumab in severe haemophilia A patients. MATERIALS AND METHODS: A state-transition Markov model evaluated the cost-effectiveness of prophylactic SHL FVIII, EHL FVIII, and emicizumab in a cohort of 2-year-old male patients over a lifetime horizon in the form of a cost-utility analysis using a Canadian provincial ministry of health payer perspective. The transition probabilities, costs, and utilities were obtained from literature and the Canadian Bleeding Disorders Registry. Probabilistic sensitivity and scenario analyses were performed to test the robustness of the model. RESULTS: The base-case analysis, over a lifetime horizon, resulted in a total cost and utilities per person for SHL FVIII, EHL FVIII, and emicizumab of $27.2 million (M), $36.7 M, and $26.2 M, respectively, and 31.30, 31.16, and 31.61 quality-adjusted life years, respectively. Emicizumab treatment resulted in 29 and 16 less bleeds in a lifetime compared to SHL FVIII and EHL FVIII, respectively. Probabilistic sensitivity analysis showed that emicizumab was cost-saving 100% of the time compared to SHL FVIII and EHL FVIII. CONCLUSION: The cost-utility analysis showed that emicizumab is more effective and may be less costly than FVIII for Canadian haemophilia A patients, conditional on drug cost assumptions. Our model indicates that emicizumab may be a potentially favourable treatment option for minimising healthcare costs and providing higher effectiveness.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".