{
  "abstract": "Objective\n\nTo 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\n\nA 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\n\nSeven 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\n\nIn 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.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Pharmacy, The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, People's Republic of China.",
    "Department of Pharmacy, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, People's Republic of China.",
    "Department of Pathology, The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, People's Republic of China.",
    "Department of Pathology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, People's Republic of China.",
    "Department of Pediatrics, The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, People's Republic of China.",
    "School of Medicine, Zhangzhou College of Science and Technology, Zhangzhou, Fujian, People's Republic of China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Xin Rao"
    },
    {
      "affs": [
        2,
        3
      ],
      "name": "Ping Wu"
    },
    {
      "affs": [
        4
      ],
      "name": "Xi Lin"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Jie-Min Zhang"
    },
    {
      "affs": [
        5
      ],
      "name": "Xiao-Jun Lin"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Zhou-Jie Liu"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Rong-Fang Lin"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Yue-Wen Chen",
      "orcid": "0009-0000-9531-4608"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/crxq4y4k04hkk.bib",
  "cited_by": 0,
  "doi": "10.2147/dddt.s648788",
  "fulltext_url": "https://europepmc.org/articles/PMC13724030",
  "id": "crxq4y4k04hkk",
  "issns": [
    "1177-8881"
  ],
  "landing_url": "https://europepmc.org/article/MED/42968942",
  "language": "eng",
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Original Research"
    ],
    [
      "openalex",
      "2738|Epilepsy research and treatment"
    ]
  ],
  "openalex": "W7224116728",
  "pdf_url": "https://europepmc.org/articles/PMC13724030?pdf=render",
  "pmcid": "PMC13724030",
  "pmid": "42968942",
  "published": "2026-10-28",
  "publisher": "Dove Press",
  "ris": "https://research.draconitic.com/paper/crxq4y4k04hkk.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T15:20:53Z",
      "native_id": "MED:42968942",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42968942"
    },
    {
      "first_seen": "2026-11-04T12:22:33Z",
      "native_id": "PMC13724030",
      "source": "pmc",
      "url": "https://doi.org/10.2147/dddt.s648788"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2738|Epilepsy research and treatment",
      "subfield": "psychiatry"
    }
  ],
  "title": "Explainable Machine Learning for Predicting Early Response to Perampanel in Pediatric Epilepsy: A Real-World Study.",
  "title_text": "Explainable Machine Learning for Predicting Early Response to Perampanel in Pediatric Epilepsy: A Real-World Study.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-05T12:20:44Z",
  "url": "https://research.draconitic.com/paper/crxq4y4k04hkk",
  "venue": "Drug design, development and therapy",
  "versions": []
}