External validation of a machine learning model for predicting liver injury in children with mycoplasma pneumoniae pneumonia
Abstract
Mycoplasma pneumoniae pneumonia (MPP) is associated with a high risk of liver injury, which adversely affects clinical outcomes and healthcare costs. Early identification of at-risk children remains challenging, and no validated predictive tool is currently available. This multicenter retrospective study included 1321 children with MPP from two centers in Nanjing, China. The development cohort was randomly split into training (67%) and test (33%) sets, with an independent external validation cohort (n = 640) from another hospital. Liver injury was defined as alanine aminotransferase (ALT) > 80 U/L. Feature selection was performed using the boruta algorithm, and ten machine learning algorithms were developed and evaluated. Model performance was assessed using area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity, specificity, calibration metrics, and decision curve analysis (DCA). The SHAP method was used for model interpretation. Among 1321 patients, 55 (4.2%) developed liver injury. The boruta algorithm identified six predictors: LDH, D-dimer, CK, age, IL-6, and CK-MB. The neural network (nnet) model demonstrated optimal performance, with AUC values of 0.914 in the training set, 0.829 in the test set, and 0.755 in the external validation set. The model showed acceptable calibration and modest clinical utility on decision curve analysis. SHAP analysis revealed LDH as the most important predictor. An interactive web-based application was developed to facilitate clinical implementation. We developed and validated a machine learning-based model using six readily available predictors that identifies children with MPP at risk for liver injury. The accompanying web-based tool may assist clinicians in early risk stratification and timely intervention.
Related
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BibTeX
@article{yu2026external,
title = {{External validation of a machine learning model for predicting liver injury in children with mycoplasma pneumoniae pneumonia}},
author = {Yang Yu and Yang Jing and Yinyan Tang and Feng Liu and Hongjuan Wei},
journal = {Scientific Reports},
year = {2026},
month = oct,
doi = {10.1038/s41598-026-67420-1},
url = {https://www.nature.com/articles/s41598-026-67420-1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - External validation of a machine learning model for predicting liver injury in children with mycoplasma pneumoniae pneumonia
AU - Yang Yu
AU - Yang Jing
AU - Yinyan Tang
AU - Feng Liu
AU - Hongjuan Wei
PY - 2026
DA - 2026/10/22
JO - Scientific Reports
DO - 10.1038/s41598-026-67420-1
UR - https://www.nature.com/articles/s41598-026-67420-1
AB - Mycoplasma pneumoniae pneumonia (MPP) is associated with a high risk of liver injury, which adversely affects clinical outcomes and healthcare costs. Early identification of at-risk children remains challenging, and no validated predictive tool is currently available. This multicenter retrospective study included 1321 children with MPP from two centers in Nanjing, China. The development cohort was randomly split into training (67%) and test (33%) sets, with an independent external validation cohort (n = 640) from another hospital. Liver injury was defined as alanine aminotransferase (ALT) > 80 U/L. Feature selection was performed using the boruta algorithm, and ten machine learning algorithms were developed and evaluated. Model performance was assessed using area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity, specificity, calibration metrics, and decision curve analysis (DCA). The SHAP method was used for model interpretation. Among 1321 patients, 55 (4.2%) developed liver injury. The boruta algorithm identified six predictors: LDH, D-dimer, CK, age, IL-6, and CK-MB. The neural network (nnet) model demonstrated optimal performance, with AUC values of 0.914 in the training set, 0.829 in the test set, and 0.755 in the external validation set. The model showed acceptable calibration and modest clinical utility on decision curve analysis. SHAP analysis revealed LDH as the most important predictor. An interactive web-based application was developed to facilitate clinical implementation. We developed and validated a machine learning-based model using six readily available predictors that identifies children with MPP at risk for liver injury. The accompanying web-based tool may assist clinicians in early risk stratification and timely intervention.
SN - 2045-2322
ER -