Author:
Jian Chang,Chen Siqi,Wang Zhuangcheng,Zhou Yang,Zhang Yang,Li Ziyu,Jian Jie,Wang Tingting,Xiang Tianyu,Wang Xiao,Jia Yuntao,Wang Huilai,Gong Jun
Abstract
Abstract
Background
High-dose methotrexate (HD-MTX) is a potent chemotherapeutic agent used to treat pediatric acute lymphoblastic leukemia (ALL). HD-MTX is known for cause delayed elimination and drug-related adverse events. Therefore, close monitoring of delayed MTX elimination in ALL patients is essential.
Objective
This study aimed to identify the risk factors associated with delayed MTX elimination and to develop a predictive tool for its occurrence.
Methods
Patients who received MTX chemotherapy during hospitalization were selected for inclusion in our study. Univariate and least absolute shrinkage and selection operator (LASSO) methods were used to screen for relevant features. Then four machine learning (ML) algorithms were used to construct prediction model in different sampling method. Furthermore, the performance of the model was evaluated using several indicators. Finally, the optimal model was deployed on a web page to create a visual prediction tool.
Results
The study included 329 patients with delayed MTX elimination and 1400 patients without delayed MTX elimination who met the inclusion criteria. Univariate and LASSO regression analysis identified eleven predictors, including age, weight, creatinine, uric acid, total bilirubin, albumin, white blood cell count, hemoglobin, prothrombin time, immunological classification, and co-medication with omeprazole. The XGBoost algorithm with SMOTE exhibited AUROC of 0.897, AUPR of 0.729, sensitivity of 0.808, specificity of 0.847, outperforming the other models. And had AUROC of 0.788 in external validation.
Conclusion
The XGBoost algorithm provides superior performance in predicting the delayed elimination of MTX. We have created a prediction tool to assist medical professionals in predicting MTX metabolic delay.
Funder
Intelligent medicine project of Chongqing Medical University
Science and Technology Research Project of Chongqing
Key project of Chongqing Science and Health Joint Medical Scientific Research Project
Future Medicine Youth Innovation Team project of Chongqing Medical University
Publisher
Springer Science and Business Media LLC
Subject
Health Informatics,Health Policy,Computer Science Applications
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