ArticleEpilepsia2026
A translational multimodal machine-learning prototype predicting valproate response in epilepsy treatment.
Article in Epilepsia, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Low diagnostic yield of presurgical genetic testing in adult patients with epilepsy.Epilepsia · 2026Article
- Glymphatic dysfunction as a predictor of response to lacosamide add-on therapy in drug-resistant focal epilepsy.Frontiers in neurologyArticle
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16 authors.
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Abstract
objectiveEpilepsy affects ~1% of the global population and often requires lifelong antiseizure medication (ASM) therapy. Valproic acid (VPA) is a commonly prescribed first-line ASM, yet only approximately half of patients achieve sustained seizure freedom. Treatment selection remains largely empirical. We aimed to develop and independently validate a multimodal predictive model to estimate response to VPA and support more individualized treatment strategies.
methodsThis cross-sectional treatment response modeling study used data from a subset of the international Epi25 cohort (Belgium, Finland, Germany). Individuals with epilepsy were included if they had received VPA monotherapy and had available genetic or clinical data. Discovery data (1965-2021, 58% female) were split into a training set (n = 196) and test set (n = 133). Independent validation was performed in a Canadian cohort (2021-2022, n = 156, 40% female). The primary outcome was binary VPA response. Responders achieved ≥12 months of seizure freedom attributed to VPA; nonresponders had >50% seizure recurrence or discontinued VPA due to inefficacy, adverse effects, or unclear reasons. The predictive algorithm integrated features derived from common and rare variants in VPA pharmacokinetic and pharmacodynamic genes, in vitro neuronal VPA response measures, and clinical features. Model performance was assessed using accuracy, predictive values (negative predictive value [NPV]/positive predictive value [PPV]), and area under the curve (AUC).
resultsIn the independent validation cohort, the multimodal classifier achieved a balanced accuracy of 63% (95% confidence interval [CI] = 52%-73%), NPV of 70% (95% CI = 51%-85%), PPV of 60% (95% CI = 46%-72%), and AUC of .73 (95% CI = .63-.83). Models restricted to single or dual data modalities showed consistently lower predictive performance. SIGNIFICANCE: This proof-of-concept study demonstrates that integrating genetic, cellular, and clinical data enables prediction of VPA treatment response with clinically meaningful accuracy. Although not yet ready for clinical application, this approach supports the feasibility of biomarker-informed ASM selection and may ultimately reduce time to effective seizure control.
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