Development of a prediction model based on decision tree for acute kidney injury in critically ill children and its predictive value
10.3760/cma.j.issn.1673-4912.2025.02.010
- VernacularTitle:基于决策树的重症儿童急性肾损伤预测模型的建立及其预测价值分析
- Author:
Huiwen LI
1
;
Jiao CHEN
;
Junlong HU
;
Jing XU
;
Zhenjiang BAI
;
Xiaozhong LI
;
Yanhong LI
Author Information
1. 苏州大学附属儿童医院肾脏免疫科 215000
- Publication Type:Journal Article
- Keywords:
Critically ill children;
Acute kidney injury;
Machine learning;
Prediction model;
Decision tree
- From:
Chinese Pediatric Emergency Medicine
2025;32(2):128-134
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To establish and validate a prediction model based on least absolute shrinkage and selection operator（LASSO）regression and classification and regression tree（CART）algorithm for acute kidney injury（AKI）in PICU.Methods:The prospective derivation cohort consisted of 350 critically ill children admitted to the PICU of Children′s Hospital of Soochow University from September 2020 to January 2021.The external data set consisting of 866 critically ill children admitted to the PICU of Children′s Hospital of Soochow University from February 2021 to February 2022 was employed for the external validation.Clinical data was obtained from the electronic medical record system，including demographic characteristics，laboratory data and the pediatric risk of mortality Ⅲ（PRISM Ⅲ）score.The variables associated with AKI were identified using LASSO regression.Subsequently，a decision tree prediction model was built using the CART algorithm.The predictive value of decision tree prediction model was evaluated using the receiver operating characteristic（ROC）curve，calibration curve，and decision curve analysis.Results:Among the 350 children in the derivation cohort，107（30.6%）developed AKI during the PICU stay；and of 866 children in the external validation cohort，165（19.1%）developed AKI during the PICU stay.The LASSO regression screened 16 candidate variables for further analysis，and the decision tree model ultimately identified 4 variables more closely associated with AKI，including fold change in serum creatinine from baseline，urine volume，PRISM Ⅲ，and C-reactive protein.The decision tree model exhibited high accuracy with AUC of 0.92，0.88，and 0.86 in the training，internal validation，and external validation cohorts，respectively.The model demonstrated good calibration and clinical applicability based on the calibration curve and decision curve analysis.Conclusion:The decision tree model based on the 4 identified clinical indicators，including fold change in serum creatinine from baseline，urine volume，PRISM Ⅲ，and C-reactive protein，is effective for the early prediction of AKI.
