Mapping CushingQOL scores to EQ-5D utility values using data from the European Registry on Cushing's syndrome (ERCUSYN)

Qual Life Res. 2013 Dec;22(10):2941-50. doi: 10.1007/s11136-013-0396-7. Epub 2013 Mar 29.


Purpose: To construct a model to predict preference-adjusted EuroQol 5D (EQ-5D) health utilities for CS using the disease-specific health-related quality of life measure (CushingQOL).

Methods: Data were obtained from the European Registry on CS (ERCUSYN). ERCUSYN is a web-based, multicenter, observational study that enrolled 508 CS patients from 36 centers in 23 European countries. Patients included in the study completed both the EQ-5D and the disease-specific CushingQOL questionnaire. Socio-demographic and clinical data were also collected. The UK tariff values were used to calculate EQ-5D utility scores. Various predictive models were tested, and the final model was selected based on four criteria: explanatory power (adjusted R-squared), consistency of estimated coefficients (sign and parameter estimation), normality of prediction errors (mean error, mean absolute error, root mean squared error), and parsimony.

Results: For the mapping analysis, data were available from a total of 129 patients. Mean (SD) age was 43.1 (13) years, and the sample was predominantly female (84.5 %). Patients had a mean (SD) CushingQOL score of 39.7 (17.1) and a mean (SD) 'tariff' value on the EQ-5D of 0.55 (0.3). The model which best met the criteria for selection included the intercept and 3 CushingQOL's questions and had an R(2) of 0.506 and a root mean square error of 0.216.

Conclusions: It was possible to find a mapping function which successfully predicted the EQ-5D UK utilities from disease-specific CushingQOL scores. The function may be useful in calculating EQ-5D scores when EQ-5D data have not been gathered directly in a study.

MeSH terms

  • Adult
  • Aged
  • Cushing Syndrome / psychology
  • Cushing Syndrome / therapy*
  • Europe
  • Female
  • Health Services / statistics & numerical data
  • Health Status*
  • Humans
  • Male
  • Middle Aged
  • Models, Theoretical
  • Patient Preference*
  • Psychometrics
  • Quality of Life*
  • Quality-Adjusted Life Years*
  • Registries
  • Regression Analysis
  • Surveys and Questionnaires*