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Multidimensional Early Prediction Score for Drug-Resistant Epilepsyopen access

Authors
Kang, K.W.Cho, Y.W.Lee, S.K.Jung, K.-Y.Kim, J.H.Kim, D.W.Lee, S.-A.Hong, S.B.Na, I.-S.Lee, S.-H.Baek, W.-K.Choi, S.-Y.Kim, M.-K.
Issue Date
2022
Publisher
Korean Neurological Association
Keywords
drug resistant epilepsy; genetic predictor; genome-wide association study; ‌epilepsy
Citation
Journal of Clinical Neurology (Korea), v.18, no.5, pp.553 - 561
Indexed
SCIE
SCOPUS
KCI
Journal Title
Journal of Clinical Neurology (Korea)
Volume
18
Number
5
Start Page
553
End Page
561
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/146021
DOI
10.3988/jcn.2022.18.5.553
ISSN
1738-6586
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. © 2022 Korean Neurological Association.
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