In clinical practice, health outcomes relevant for economic evaluations, such as health-related quality of life (i.e., utilities), are not always available. However, algorithms can be developed to map available outcome measures to utilities. In the Netherlands, the Health of the Nations Outcomes Scores (HoNOS) is a physician-reported, disease-specific outcome measure frequently used for patients with severe mental illness for which no mapping algorithms for utilities currently exist. The aim of this study was to develop an algorithm to map responses on the HoNOS to EQ-5D utilities for use in economic evaluations.

A dataset was obtained from the Pharmacotherapy Monitoring and Outcome Survey cohort study containing data from patients with psychotic disorders. The dataset contains EQ-5D-3L domain and HoNOS scores, the age and sex of patients, and additional demographics. Correlation between the EQ-5D-3L and HoNOS was evaluated. To derive mapping functions, least absolute shrinkage and selection operator (LASSO) regression and random forest algorithms were applied with various predictor variables using a machine learning approach, whereby data were split into separate training and test sets. Cross-validation was then used to compare the performance of different models using R-squared and the root mean square error (RMSE).

A total of 2,111 patients were included in the study. Spearman’s correlation coefficients indicated a weak to moderate negative correlation of -0.31. Based on model performance metrics, LASSO models outperformed random forest models on the training set, where the model including all individual HoNOS items and the age and sex of patients showed the best overall performance with an RMSE of 0.237 and an R-squared of 0.218. When applied to the test set, this resulted in an R-squared of 0.233 and a mean absolute error of 0.177.

The HoNOS can be mapped onto EQ-5D-3L utilities with moderate predictive accuracy. The reported mapping algorithm may be sufficient to predict overall population utility scores for use in health economic evaluations but lacks accuracy for individual patient predictions.