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Risk factor identification for delayed excretion in pediatric high-dose methotrexate therapy: a machine learning
Chengyu Zhou1,2, Yali Qian1, Yao Xue3
1Pediatric Intensive Care Unit, Children's Hospital of Nanjing Medical University, Nanjing, China.
Frontiers in Pharmacology
|October 3, 2025
Summary
Delayed methotrexate excretion in pediatric patients receiving high-dose methotrexate (HDMTX) therapy is common. An XGBoost model using clinical data accurately predicts delayed excretion, identifying serum creatinine as a key risk factor.
Area of Science:
- Pediatric Oncology
- Pharmacokinetics
- Machine Learning in Medicine
Background:
- High-dose methotrexate (HDMTX) therapy is crucial for treating pediatric cancers.
- Delayed methotrexate (MTX) excretion can lead to severe toxicity in pediatric patients.
- Identifying risk factors and predicting delayed MTX excretion is essential for optimizing treatment outcomes.
Purpose of the Study:
- To identify risk factors for delayed MTX excretion in pediatric patients undergoing HDMTX therapy.
- To develop and validate a predictive model for delayed MTX excretion using real-world data.
- To enhance clinical decision-making and patient safety during HDMTX treatment.
Main Methods:
- Retrospective analysis of 1,485 pediatric HDMTX chemotherapy cycles.
- Variable selection using LASSO regression, Random Forest, and SVM-RFE.
- Development and internal validation of predictive models (LR, NB, SVM, XGBoost) using bootstrap.
- Model performance evaluation using accuracy, F1 score, AUROC, and Brier score.
- Clinical utility assessed by Decision Curve Analysis (DCA) and SHAP values for interpretability.
Main Results:
- Delayed MTX excretion occurred in 26.1% of pediatric HDMTX cycles.
- Key predictors identified: serum creatinine (Scr), total drug dose (Dose), alkaline phosphatase (ALP), creatine kinase (CK), blood urea nitrogen (Urea), gamma-glutamyl transferase (GGT), hemoglobin (HB), and height.
- The XGBoost model demonstrated superior performance: accuracy 0.780, F1 score 0.669, AUROC 0.842.
- SHAP analysis highlighted Scr as the most significant predictor of delayed MTX excretion.
Conclusions:
- The developed XGBoost model accurately predicts delayed MTX excretion in pediatric patients receiving HDMTX.
- The model exhibits good clinical utility and interpretability, with serum creatinine being a primary risk factor.
- This predictive tool can aid clinicians in managing HDMTX therapy and mitigating toxicity risks.
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