Predicting oxcarbazepine-induced hyponatremia in adult epilepsy patients: A multicenter machine learning analysis using real-world CDM data

Gucheol Jung, JaeHyeok Lee, Sung-Min Gho, YoungMi Han, ByungKwan Choi, Jae Wook Cho, Jiyoung Kim, Gha-hyun Lee,
Predicting oxcarbazepine-induced hyponatremia in adult epilepsy patients: A multicenter machine learning analysis using real-world CDM data,
Seizure: European Journal of Epilepsy,
Volume 133,
2025,
Pages 167-174,
ISSN 1059-1311,
https://doi.org/10.1016/j.seizure.2025.10.004.
(https://www.sciencedirect.com/science/article/pii/S1059131125002729)
Abstract: Purpose
Oxcarbazepine (OXC) is a widely used antiseizure medication (ASM) associated with hyponatremia. This study aimed to assess the prevalence and risk factors for OXC-induced severe hyponatremia using machine learning (ML) models applied to multicenter real-world data standardized within the Observational Medical Outcomes Partnership–Common Data Model (OMOP–CDM).
Methods
We conducted a retrospective cohort study using OMOP-CDM data from two tertiary hospitals in South Korea. Adult epilepsy patients prescribed OXC were included, and severe hyponatremia was defined as a serum sodium concentration ≤128 mmol/L. Two prediction experiments were conducted: (1) single-institution training and external validation of an XGBoost model; and (2) multicenter training and evaluation of five machine learning algorithms, including XGBoost, random forest, support vector machine, logistic regression, and naïve Bayes. SHAP (SHapley Additive exPlanations) values were used for model interpretation.
Results
Among 2253 patients, the prevalence of severe hyponatremia was 8.4%. In Experiment 1, XGBoost showed strong internal performance (AUROC 0.82) but decreased external performance (AUROC 0.72). In Experiment 2, XGBoost trained on multicenter data achieved the highest AUROC (0.83) and F1-score (0.41), outperforming other models. SHAP analysis identified key predictors including valproate use, diuretics, high OXC dosage, age, and stroke history. Additional medications such as beta-blockers, calcium channel blockers, hypnotics, and other ASMs (e.g., levetiracetam, pregabalin, lacosamide) also contributed to risk.
Conclusion
XGBoost demonstrated robust predictive performance for OXC-induced severe hyponatremia using multicenter CDM data. SHAP-based interpretation revealed clinically relevant risk factors, supporting the implementation of personalized monitoring strategies in epilepsy care.
Keywords: Oxcarbazepine; Hyponatremia; Machine learning; SHAP; Epilepsy; Common data model; Polypharmacy