J Clin Neurol.  2022 Sep;18(5):553-561. 10.3988/jcn.2022.18.5.553.

Multidimensional Early Prediction Score for Drug-Resistant Epilepsy

Affiliations
  • 1Department of Neurology, Chonnam National University Hospital, Chonnam National University Medical School, Gwangju, Korea
  • 2Department of Neurology, Dongsan Medical Center, Keimyung University School of Medicine, Daegu, Korea
  • 3Department of Neurology, Comprehensive Epilepsy Center, Laboratory for Neurotherapeutics, Biomedical Research Institute, Seoul National University Hospital, Seoul, Korea Program in Neuroscience, Seoul National University College of Medicine, Seoul, Korea
  • 4Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Korea
  • 5Department of Neurology, Konkuk University School of Medicine, Seoul, Korea
  • 6Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
  • 7Department of Neurology, Samsung Medical Center, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University School of Medicine, Samsung Biomedical Research Institute (SBRI), Seoul, Korea
  • 8National Epilepsy Care Center, Seoul, Korea
  • 9National Program of Excellence in Software Centre, Chosun University, Gwangju, Korea
  • 10Department of Biomedical Science, Chonnam National University Medical School, Hwasun, Korea
  • 11Department of Microbiology, Keimyung University School of Medicine, Daegu, Korea

Abstract

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-DREgen), clinical predictors (EPS-DREcln), and two types of predictor mix (EPS-DREmix) in both the initial test set and the validation set.
Results
The multidimensional EPS-DREmix of the predictor mix group provided a better match to the outcome data than did the unidimensional EPS-DREgen or EPS-DREcln. Unlike previous studies, the EPS-DREmix 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-DREmix 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-DREmix model.

Keyword

epilepsy; drug resistant epilepsy; genome-wide association study; genetic predictor
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