Economic Evaluation of Azacitidine in Elderly Patients with Acute Myeloid Leukemia with High Blast Counts
Bibliographic record
Abstract
BACKGROUND: Azacitidine is an hypomethylating agent widely adopted for the treatment of acute myeloid leukaemia (AML) in patients who are ineligible for curative-intent chemotherapy. Patients with low bone marrow blast counts (< 30%) experience improved survival with azacitidine, but the benefits are significantly lower in patients with > 30% blasts in the bone marrow. As such, there is uncertainty around the economic benefit of azacitidine in patients with higher blast counts. OBJECTIVE: We present a cost-utility analysis of azacitidine in patients with AML with > 30% blasts to determine the economic value of azacitidine in this patient population from the perspective of a third-party payer. METHODS: A Markov model was developed with a time horizon of 25 months divided into 22 cycles of 35 days each. The cost utility of azacitidine was compared with that of conventional care regimens (which include best supportive care, low-dose cytarabine and induction chemotherapy). A Canadian public healthcare system perspective was selected. RESULTS: In the base case, the incremental cost per quality-adjusted life-year gained (incremental cost-effectiveness ratio [ICER]) for azacitidine compared with conventional care regimens was $Can160,438, year 2018 values. The estimated ICER was insensitive to a longer time horizon but sensitive to the cost of azacitidine and to assumptions relating to survival in both treatment regimens, although the ICER always remained greater than Can$80,000 in all scenarios. CONCLUSION: Azacitidine is unlikely to be cost effective given that the estimated ICER exceeds the willingness to pay commonly used in the Canadian healthcare system.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".