Risk factor identification for delayed excretion in pediatric high-dose methotrexate therapy: a machine learning analysis of real-world data.

ABSTRACT:

Purpose

This study was to identify risk factors associated with delayed methotrexate (MTX) excretion in pediatric patients receiving high-dose MTX (HDMTX) therapy based on real-world data, and to develop and evaluate a predictive model.

Methods

Clinical data were retrospectively collected from 1,485 pediatric HDMTX chemotherapy cycles at the Children's Hospital affiliated with Nanjing Medical University between 2021 and 2023. Key predictive variables were identified by Least Absolute Shrinkage and Selection Operator (LASSO) regression, Random Forest (RF), and Support Vector Machine Recursive Feature Elimination (SVM-RFE), and then incorporated into predictive models for MTX delayed excretion using Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), and

SUBMITTER: Zhou C 

PROVIDER: S-EPMC12483887 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature