A prospective, observational, US-based study (PROVe) used three questionnaires (Pruritus-VAS, Skindex-29, MF/SS-CTCL QoL) to assess quality of life in patients diagnosed with mycosis fungoides cutaneous T-cell lymphoma (MF-CTCL); however, none of these studies was provided with a preference-based algorithm yielding health state utility values (HSUVs).
This study aimed to assess the feasibility of deriving HSUVs from published mapping algorithms by comparing mapped utilities with the HSUVs reported in the MF-CTCL literature.
We searched PubMed, the School of Health and Related Research Health Utility Database (ScHARRHUD), and the Health Economics Research Centre (HERC) database of mapping studies (version 7.0) to identify any studies mapping Pruritus-VAS, Skindex-29, or MF/SS-CTCL QoL to a preference-based instrument (ideally, EQ-5D), and any studies assessing HSUVs in MF-CTCL. Two algorithms from a recent study that mapped Pruritus-VAS onto EQ-5D-3L were applied to the PROVe patient-level data. We performed multiple imputation to handle missing VAS data, calculated average mapped utilities in the whole sample, and compared them with relevant factors using the t-test and one-way analysis of variance (ANOVA).
Overall, 298 patients provided 1441 Pruritus-VAS scores over a 2-year follow-up (1-21 visits per patient). The average mapped HSUVs ranged between 0.950 and 0.999 depending on the algorithm applied and imputation of missing data. In subgroup analysis, significant differences (p < 0.05) were observed according to age, race, and cancer stage. A few previous studies that collected HSUVs from MF-CTCL patients reported mean values of between 0.82 and 0.87 using time trade-off, 0.63 and 0.83 using EQ-5D, and 0.51 and 0.69 using the HUI3.
The HSUVs derived by applying published mapping algorithms to PROVe Pruritus-VAS data appeared largely overestimated if compared with the existing literature. More research is required to understand the applicability of existing mapping algorithms and to develop new mapping algorithms in MF-CTCL.

© 2022. The Author(s).