Venetoclax in Combination with Azacitidine for the Treatment of Newly Diagnosed Acute Myeloid Leukemia: A Canadian Cost–Utility Analysis
Bibliographic record
Abstract
Treatment for acute myeloid leukemia (AML) typically involves intensive chemotherapy (IC); however, there is an unmet need for approximately 50% of AML patients who are deemed unfit or ineligible for IC. The purpose of this study was to evaluate, from a Canadian perspective, the economic impact of venetoclax in combination with azacitidine (Ven+Aza) for the treatment of patients with newly diagnosed AML who are 75 years or older or who have comorbidities that preclude using IC. A lifetime partitioned survival model was developed to assess the cost-effectiveness of Ven+Aza compared with Aza. Health states included event-free survival, progressive/relapsed disease, and death. Efficacy parameters were based on the VIALE-A trial. Analyses were conducted from Ministry of Health (MoH) and societal perspectives. Over a lifetime horizon, Ven+Aza was associated with a gain of 1.65 quality-adjusted life years (QALYs) compared with Aza. From an MoH perspective, Ven+Aza and Aza were associated with total costs of $204,305 and $82,333, respectively, resulting in an incremental cost-utility ratio of $73,841/QALY. Results were similar from a societal perspective. This economic evaluation demonstrates that, in comparison with Aza, Ven+Aza is a cost-effective strategy for the treatment of patients with newly diagnosed AML who are deemed unfit for IC.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| 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".