Mapping AcroQoL scores to EQ-5D to obtain utility values for patients with acromegaly.

AIMS: To estimate a preference-based single index for the disease-specific instrument (AcroQoL) by mapping it onto the EQ-5D to assist in future economic evaluations.

MATERIALS AND METHODS: A sample of 245 acromegaly patients with AcroQoL and EQ-5D scores was obtained from three previously published European studies. The sample was split into two: one sub-sample to construct the model (algorithm construction sample, n = 184), and the other one to confirm it (validation sample, n = 61). Various multiple regression models including two-part model, tobit model, and generalized additive models were tested and/or evaluated for predictive ability, consistency of estimated coefficients, normality of prediction errors, and simplicity.

RESULTS: Across these studies, mean age was 50-60 years and the proportion of males was 36-59%. At overall level the percentage of patients with controlled disease was 37.4%. Mean (SD) scores for AcroQoL Global Score and EQ-5D utility were 62.3 (18.5) and 0.71 (0.28), respectively. The best model for predicting EQ-5D was a generalized regression model that included the Physical Dimension summary score and categories from questions 9 and 14 as independent variables (Adj. R2  = 0.56, with mean absolute error of 0.0128 in the confirmatory sample). Observed and predicted utilities were strongly correlated (Spearman r = 0.73, p < .001) and paired t-Student test revealed non-significant differences between means (p > .05). Estimated utility scores showed a minimum error of ≤10% in 45% of patients; however, error increased in patients with an observed utility score under 0.2. The model's predictive ability was confirmed in the validation cohort.

LIMITATIONS AND CONCLUSIONS: A mapping algorithm was developed for mapping of AcroQoL to EQ-5D, using patient level data from three previously published studies, and including validation in the confirmatory sub-sample. Mean (SD) utilities index in this study population was estimated as 0.71 (0.28). Additional research may be needed to test this mapping algorithm in other acromegaly populations.