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Machine learning to predict high-dose methotrexate-related neutropenia and fever in children with B-cell acute
Min Zhan1, Ze-Bin Chen1, Chang-Cai Ding2
1Department of Pharmacy, Shenzhen Children's Hospital, Shenzhen, People's Republic of China.
Abstract:
Methotrexate (MTX), an antimetabolite for the treatment of leukemia, could cause neutropenia and subsequently fever, which might lead to treatment delay and affect prognosis. Here, we aimed to predict neutropenia and fever related to high-dose MTX using artificial intelligence. This study included 139 pediatric patients newly diagnosed with standard- or intermediate risk B-cell acute lymphoblastic leukemia. Fifty-seven SNPs of 16 genes were genotyped. Univariate and multivariate analysis were used to select SNPs and clinical covariates for model developing. Five machine learning algorithms combined with four resampling techniques were used to build optimal predictive model. The combination of random forest with adaptive synthetic appeared to be the best model for neutropenia (sensitivity = 0.935, specificity = 0.920, AUC = 0.927) and performed best for fever (sensitivity = 0.818, specificity = 0.924, AUC = 0.870). By machine learning, we have developed and validated comprehensive models to predict the risk of neutropenia and fever. Such models may be helpful for medical oncologists in quick decision-making.
Insights
Artificial intelligence models can predict neutropenia and fever in pediatric leukemia patients receiving high-dose methotrexate (MTX). These AI tools aid oncologists in making rapid treatment decisions, potentially improving patient outcomes.
Area of Science:
- Pharmacogenomics
- Computational Biology
- Pediatric Oncology
Background:
- High-dose methotrexate (MTX) is crucial for treating B-cell acute lymphoblastic leukemia (B-ALL).
- MTX can cause neutropenia and fever, leading to treatment delays and impacting prognosis.
- Predicting these adverse events is vital for optimizing pediatric leukemia treatment.
Purpose of the Study:
- To develop and validate artificial intelligence (AI) models for predicting neutropenia and fever in pediatric B-ALL patients undergoing high-dose MTX therapy.
- To identify genetic and clinical factors associated with MTX-induced toxicity.
Main Methods:
- Retrospective analysis of 139 pediatric B-ALL patients.
- Genotyping of 57 single nucleotide polymorphisms (SNPs) across 16 genes.
- Development of predictive models using five machine learning algorithms and four resampling techniques.
- Evaluation of model performance using sensitivity, specificity, and AUC.
Main Results:
- The random forest model combined with adaptive synthetic resampling demonstrated superior performance.
- The best model achieved high accuracy for predicting neutropenia (AUC = 0.927) and fever (AUC = 0.870).
- Validated AI models effectively predict the risk of neutropenia and fever.
Conclusions:
- AI-driven predictive models can accurately forecast MTX-related neutropenia and fever in pediatric B-ALL.
- These models offer valuable decision-support tools for medical oncologists.
- Early prediction of adverse events can facilitate timely interventions and improve treatment outcomes.
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