Abstract
OBJECTIVES: A systematic literature review (SLR) was conducted to identify utilities in acute myeloid leukaemia (AML).
METHODS: SLR methods conformed to National Institute for Health and Care Excellence technology appraisal requirements. Eligible studies were economic evaluations (cost-effectiveness, cost-utility and cost-benefit analyses), utility studies, SLRs and health technology assessments. Nine bibliographic databases and 4 conferences were searched (November 2018) and retrieved 2,278 unique records. Two reviewers independently selected records and one extracted data.
RESULTS: Twenty-six studies (28 documents) met inclusion criteria. Data were derived using: EQ-5D (14 studies, including 2 using EQ-5D-5L), TTO (4), VAS (4), DCE (2), HUI2 (1), QLQ-PBM (1), EORTC-8D (1), standard gamble (1), as well as proxy data (1) and data mapped to utility (1, from QLQ-C30). Studies were conducted in Canada, Japan, the Netherlands, UK, USA, and one study was across Europe. Mean utility data were identified for these health states: induction chemotherapy (-0.15 to 0.706), consolidation chemotherapy (-0.11 to 0.71), maintenance (0.81 to 0.95 [median]), complete remission (0.62 to 0.99) [in transplant: 0.61 to 0.71; prior to transplant: 0.826], no relapse (0.83 to 0.90 [median]), temporary remission (0.66), partial remission (0.6574 to 0.7160), relapse (0.1 to 0.79), refractory (-0.1 to 0.568) and transplant (-0.21 to 0.94) [short-term: 0.4; recovery: 0.75 to 0.826; graft versus host disease (GVHD): 0.37 to 0.691; without GVHD: 0.79 to 0.864; long term: 0.94 to 1 [median]]. Disutilities were reported for chemotherapy (0.42) and transplant (0.57).
CONCLUSIONS: The SLR reported wide variations in utility values across AML health states, with most studies referring to first line treatment with induction/consolidation rather than relapse/refractory. Variations could be due to underlying disease activity within health states, differences utility elicitation methods, and health state definitions. Choice of utility values for decision analytic models should consider these differences to improve validity.