Risk Factors for Drug-Resistant Epilepsy in Children

Overview

This prospective cohort study evaluates clinical, electroencephalographic predictors of drug-resistant epilepsy in children. Predictive statistical and machine-learning models will be developed to facilitate early identification of children at high risk for drug-resistant epilepsy.

Epilepsy is one of the most common neurological disorders in childhood. Approximately one-third of pediatric patients eventually develop drug-resistant epilepsy despite appropriate antiseizure medication treatment. Early identification of children at high risk of drug resistance may facilitate timely referral for advanced therapies including epilepsy surgery, ketogenic diet, or neuromodulation. This prospective cohort study aims to identify independent predictors of drug-resistant epilepsy in children treated at Children's Hospital 1, Ho Chi Minh City, Vietnam. Clinical characteristics, electroencephalography findings, neuroimaging, genetic testing, metabolic investigations, treatment response, and developmental outcomes will be prospectively collected. Cox proportional hazards regression will be used to identify independent predictors of drug resistance. Machine learning models including logistic regression and random forest will subsequently be developed and internally validated to predict drug-resistant epilepsy.

Conditions

Interventions

Eligibility

Locations (1)

Outcomes