PP261 Development Of A Mapping Algorithm To Predict SF-6D Values In People With Drug-Resistant Focal Onset Seizures

• Published in: International journal of technology assessment in health care Vol. 37; no. S1; p. 31
• Main Authors: Flint, India, Medjedovic, Jasmina, O'Flaherty, Ewa Drogon, Alvarez-Baron, Elena, Thangavelu, Karthinathan, Meunier, Aurelie, Longworth, Louise, Savic, Natasa
• Format: Journal Article
• Language: English
• Published: New York, USA Cambridge University Press 01.12.2021
• Subjects: Algorithms; Clinical trials; Convulsions & seizures; Cost analysis; Drug resistance; Epilepsy; Mapping; Norms; Poster Presentations; Quality of life; Questionnaires; Regression analysis; Regression models; Root-mean-square errors; Seizures
• ISSN: 0266-4623; 1471-6348; 1471-6348
• DOI: 10.1017/S0266462321001434

IntroductionFocal-onset-seizures (FOS) are commonly experienced by individuals with epilepsy and have a significant impact on quality of life (QoL). This study aimed to develop a mapping algorithm to predict the 6 dimension short form questionnaire (SF-6D) values in adults with FOS for use in economic evaluations of a new treatment, cenobamate.MethodsAn online survey, including questions on sociodemographic, disease history, the short form (SF) 36, and an epilepsy-specific measure (quality of life in epilepsy problems questionnaire, QOLIE-31-P) was administered to individuals with drug-resistant FOS in the top 5 EU countries (UK, Spain, Germany, Italy and France). A range of regression models were fitted to SF-6D scores including direct and response mapping approaches.ResultsThe analysis included 361 people. In the previous 28 days, the mean number of FOS experienced was three, (range: 0–43) and longest seizure-free period was 14 days (range: 1–28). 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 (RMSE) and mean absolute error (MAE) values of 0.0977 and 0.0742, respectively. Explanatory variables which best predicted SF-6D included seizure frequency, seizure severity, seizure freedom, and age.ConclusionsPeople with drug-resistant FOS have poor QoL. The mapping algorithm enables the prediction of SF-6D values from clinical outcomes in individuals with drug-resistant FOS. It can be applied to outcome data from clinical trials to facilitate cost-utility analysis.