Mapping analysis to predict SF-6D utilities from health outcomes in people with focal epilepsy.

Abstract

Focal-onset seizures (FOS) are commonly experienced by people with epilepsy and have a significant impact on quality of life (QoL). This study aimed to develop a mapping algorithm to predict SF-6D values in adults with FOS for use in economic evaluations of a new treatment, cenobamate. An online survey, including questions on disease history, SF-36, and an epilepsy-specific measure (QOLIE-31-P) was administered to people with FOS in the UK, France, Italy, Germany, and Spain. A range of regression models were fitted to SF-6D scores including direct and response mapping approaches. 361 individuals were included in the analysis. In the previous 28days, the mean number of FOS experienced was 3, (range 0-43) and the mean longest period of consecutive days without experiencing a seizure was 14days (range 1-28days or more). Mean responses on all SF-36 dimensions were lower than general population norms. Mean SF-6D and QOLIE-31-P scores were 0.584 and 45.72, respectively. The best performing model was the ordinary least squares (OLS), with root mean squared error and mean absolute error values of 0.0977 and 0.0742, respectively. Explanatory variables which best predicted SF-6D included seizure frequency, severity, freedom, and age. People with uncontrolled FOS have poor QoL. The mapping algorithm enables the prediction of SF-6D values from clinical outcomes in people with FOS. It can be applied to outcome data from clinical trials to facilitate cost-utility analysis.