Work

Explainable Machine Learning for Predicting Early Response to Perampanel in Pediatric Epilepsy: A Real-World Study

Year: 2026

Type: article

Abstract: Objective: To develop and validate an early prediction model for perampanel (PER) response in children with epilepsy (CWE) using clinical covariates and ensemble learning, to support individualized treatment decisions. Methods: A retrospective cohort of 325 CWE (< 15 years) treated with PER from January 2021 to December 2025 was enrolled. Treatment response at 2 months was defined as the study outcome (responder vs non-responder). Key covariates were screened using Light Gradient Boosting Machine (LGBM) feature importance ranking combined with forward feature selection. Five core algorithms were selected from 11 candidate algorithms, and a soft voting ensemble model was constructed, interpreted using SHapley Additive exPlanations (SHAP), and subjected to age-stratified analysis. Results: Seven covariates were ultimately included. The ensemble model achieved a test set AUC of 0.861 (95% CI: 0.727-0.963), accuracy of 0.892 (95% CI: 0.815-0.954), and specificity of 0.940 (95% CI: 0.860-1.000); the mean 5-fold cross-validation AUC was 0.915 ± 0.064; external validation yielded an AUC of 0.798 (95% CI: 0.668-0.900). Decision curve analysis (DCA) demonstrated improved net benefit within threshold probabilities of 0.07 to 0.75. Calibration curves showed good calibration (brier score = 0.12). SHAP analysis identified the number of antiseizure medications (ASMs, 43.7%), PER maintenance dose (17.0%), and CYP3A4 enzyme inducers (CYP3A4-I, 13.1%) as the primary predictors, all positively associated with treatment non-response. Exploratory age-stratified SHAP analysis (≤72 months vs >72 months) suggested that the associations of polytherapy, higher PER maintenance dose, and shorter disease duration with predicted treatment non-response may be more pronounced in younger children. Conclusion: In this pediatric cohort, the ensemble model demonstrated good discrimination and calibration for predicting early perampanel response at 2 months. The number of ASMs, PER maintenance dose, and CYP3A4-I were the leading predictors of treatment non-response.

Source: Drug Design Development and Therapy

DOI: 10.2147/dddt.s648788