Abstract
Objective: Testing for mutations in genes of known prognostic importance in acute myeloid leukemia (AML) can inform treatment decisions once first complete remission is achieved. Our objective was to model the cost-effectiveness of a genomics-based diagnostic with appropriate consideration to the relevant decision problems and heterogeneous nature of AML.
Methods: A hybrid, decision-tree and Markov modeling approach was taken to conceptualize the history and chances involved in AML diagnosis and treatment. Outcomes from young adults (age ≥18 and <60 at diagnosis) with de novo, intermediate cytogenetic risk group AML in British Columbia, Canada, were used to inform transition probabilities in the model and outcomes. A separate, patient-level, cost dataset was built for each of the health states and cycles in the model. Our base-case scenario assessed the impact of testing for NCCN guideline specified mutations (FLT3-ITD and NPM1) versus no genomic testing. Deterministic analysis was applied to assess relevant parameter inputs such as the impact of testing for other emergent prognostic mutations in AML and the cost of the diagnostic test. Probabilistic analysis was applied to assess the combined parameter uncertainty of the model.
Results: Consolidation treatment decisions that follow successful first remission inductions (CR1) are critically important to health outcomes in AML. AML patients who undergo stem cell transplant in their first complete remission incur higher upfront costs than those who are treated with chemotherapy alone, yet survive significantly longer and have longer relapse-free survival. Cost savings are available from reduction of salvage transplants if high-risk patients are treated with a transplant in their first complete remission. The data shows a baseline chance that qualifying AML patients would receive a transplant that is equivalent to 34%, overall. Scenarios which project the impact of mutational testing predict that 15% more unrelated stem cell transplants could be expected at an increased cost of $4,822 (2013 CAD) and gain of 27 days of life per person, on average. Deterministic analysis identified the cost of a stem cell transplant to have a strong impact on cost-effectiveness, while the cost of the genomic test, and addition of other mutational tests were minor contributions to the simulated cost-effectiveness ratios. In probabilistic analysis, the resulting incremental cost-effectiveness ratios (averaging $43,634 per life-year gained) were found to be reasonably likely to be considered a cost-effective cancer intervention in Canada.
Conclusions: Modeling the impact of genomic tests in AML should sort cost and outcomes data according to treatment history and disease sub-classification. Mutational testing for young adult, de novo AML with intermediate risk characteristics is likely to be considered a cost-effective intervention to inform critical CR1 treatment decisions.
No relevant conflicts of interest to declare.
Author notes
Asterisk with author names denotes non-ASH members.
