Predictive Nomogram for Suboptimal Valproate Concentration in Pediatric Epilepsy

121 pediatric epilepsy patients aged 2-18 years were analyzed in a retrospective study to develop a predictive nomogram. The model, created by Tian Hu and last updated in April 2026, identifies four predictors for suboptimal valproate levels: daily dose, acute liver injury, acute kidney injury, and concurrent meropenem use.

Use Cases

• Predicting suboptimal valproate concentrations based on patient dose and clinical factors.
• Building risk stratification models for pediatric epilepsy treatment using clinical and pharmacological variables.
• Validating predictive algorithms for narrow therapeutic range drugs via internal bootstrap resampling.
• Supporting clinical decision-making for targeted therapeutic drug monitoring in pediatric neurology.

Strengths

• Model demonstrated excellent discrimination with an AUC of 0.911.
• Data includes 121 patient records with steady-state trough concentrations.
• Predictors were selected using the Boruta algorithm for feature importance.
• Model performance was internally validated using bootstrap resampling.

Limitations

• Row count for the underlying dataset is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Data is from a single-center retrospective study, which may limit generalizability.

Provenance

Source

Tianxin Hu

Collection Method

Single-center retrospective cohort study of pediatric patients with epilepsy receiving valproate.

Time Range

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Freshness

Last updated 2026-04-24 07:02:14; freshness should be verified.

Geography

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