A web-based machine learning tool predicts delayed methotrexate (MTX) elimination in pediatric patients with acute lymphoblastic leukemia (ALL), according to research published in BMC Medical Informatics and Decision Making.

“Children are more prone to delayed MTX elimination, which could affect their prognosis or lead to other adverse outcomes. Consequently, it is crucial to find ways to reduce the delayed elimination of MTX and the incidence of side effects,” the study authors noted in their report.

The researchers used retrospective data from 7 medical institutions to identify risk factors associated with delayed MTX elimination in pediatric patients with ALL and to develop a predictive tool for delayed MTX elimination.

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Patients ≤18 years of age who received MTX chemotherapy during hospitalization were included in the study if their medical record included ALL risk classification, morphotyping, and immunological classification and measurements of MTX blood concentration during hospitalization that started no later than 7 days after MTX administration.

Variables evaluated in the study included relevant demographic characteristics, clinical features, combination medications, and laboratory test data. The researchers then used machine learning algorithms to construct and identify the best-performing prediction model using several indicators.

A total of 1729 patients were included in the study. Of those, there were 329 patients with delayed MTX elimination and 1400 patients without delayed MTX elimination.

The analysis identified 11 predictors of delayed MTX elimination, including age, weight, creatinine, uric acid, total bilirubin, albumin, white blood cell count, hemoglobin, prothrombin time, immunological classification, and co-medication with omeprazole.

The best performing model exhibited an area under the receiver operating characteristic curve (AUROC) of 0.897, area under the precision recall curve of 0.729, sensitivity of 0.808, and specificity of 0.847. When tested with an external validation dataset, the model demonstrated an AUROC of 0.788.

“Our predictive model provides a reliable means for monitoring the metabolic delay of MTX, even in the absence of MTX plasma concentration monitoring. By utilizing this tool, medical professionals can take timely targeted measures to prevent the occurrence of MTX-related adverse drug events,” the study authors concluded in their report.

Limitations of the study included the retrospective design and potential lack of generalizability due to differences in ALL incidence, treatment, and patient characteristics across different regions.


Jian C, Chen S, Wang Z, et al. Predicting delayed methotrexate elimination in pediatric acute lymphoblastic leukemia patients: an innovative web-based machine learning tool developed through a multicenter, retrospective analysis. BMC Med Inform Decis Mak. 2023;23(1):148. doi:10.1186/s12911-023-02248-7