Multidimensional Early Prediction Score for Drug-Resistant Epilepsy

BACKGROUND AND PURPOSE: Achieving favorable postoperative outcomes in patients with drug-resistant epilepsy (DRE) requires early referrals for preoperative examinations. The purpose of this study was to investigate the possibility of a user-friendly early DRE prediction model that is easy for nonexperts to utilize. METHODS: A two-step genotype analysis was performed, by applying 1) whole-exome sequencing (WES) to the initial test set (n=243) and 2) target sequencing to the validation set (n=311). Based on a multicenter case–control study design using the WES data set, 11 genetic and 2 clinical predictors were selected to develop the DRE risk prediction model. The early prediction scores for DRE (EPS-DRE) was calculated for each group of the selected genetic predictors (EPS-DRE(gen)), clinical predictors (EPS-DRE(cln)), and two types of predictor mix (EPS-DRE(mix)) in both the initial test set and the validation set. RESULTS: The multidimensional EPS-DRE(mix) of the predictor mix group provided a better match to the outcome data than did the unidimensional EPS-DRE(gen) or EPS-DRE(cln). Unlike previous studies, the EPS-DRE(mix) model was developed using only 11 genetic and 2 clinical predictors, but it exhibited good discrimination ability in distinguishing DRE from drug-responsive epilepsy. These results were verified using an unrelated validation set. CONCLUSIONS: Our results suggest that EPS-DRE(mix) has good performance in early DRE prediction and is a user-friendly tool that is easy to apply in real clinical trials, especially by nonexperts who do not have detailed knowledge or equipment for assessing DRE. Further studies are needed to improve the performance of the EPS-DRE(mix) model.