LAUSR

BackgroundThere is no research on mapping algorithms between EQ-5D and COPD assessment test (CAT) in Korea. The purpose of this study was to develop mapping algorithms that predict EQ-5D-3 L utility from the CAT in patients with COPD.MethodsSurvey data of 300 COPD patients were collected from three tertiary teaching hospitals in Korea. To predict EQ-5D-3 L utility from the CAT, various models were assessed. Models were developed using randomly split training samples. Subsequently, the models were validated based on root mean square error (RMSE) and mean absolute error (MAE) in validation samples. The models were also validated using the bootstrap method, which involves iterative splitting, training, and validating of the sample data at least 10,000 times. Average RMSEs and MAEs were used as criteria for model selection.ResultsThe recommended mapping algorithms were based on ordinary least squares (OLS) regression models, which revealed five CAT items (chest tightness, breathlessness, activity, leaving home, and energy) as statistically significant on the EQ-5D-3 L. The mapping models estimated the overall mean of EQ-5D-3 L utilities effectively, but EQ-5D-3 L utilities for severe (low utility) patients (< 0.6) were overestimated as the observed EQ-5D-3 L utilities were often distributed over 0.6.ConclusionMapping algorithms can be used to predict EQ-5D-3 L utilities from the CAT. However, mapping algorithms should be used cautiously when applied to groups with greater disease severity.