Frontiers in medicine · 2026
Development and validation of an interpretable prediction model for high-grade vesicoureteral reflux in children with urinary tract infections.
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Ye Le-Zhen, Sun Jian-Xin, Chen Jing, Chen Jin-Liang, Bi Ye, Zhang Yan-Ru
Аннотация
OBJECTIVE: High-grade vesicoureteral reflux (VUR) is prone to causing adverse clinical outcomes such as kidney damage, recurrent urinary tract infections, and hypertension. Early identification of the risk of developing high-grade VUR in children with urinary tract infections is of great significance for reducing unnecessary voiding cystourethrography (VCUG), guiding clinical decisions, and optimizing antibiotic management strategies.
METHODS: A total of 434 children with urinary tract infections who completed VCUG examination were retrospectively enrolled in this study. They were randomly divided into a training set (n = 304) and a test set (n = 130) in a 7:3 ratio. LASSO regression was used for feature selection. Based on the selected features, six machine learning prediction models were constructed: Logistic Regression, Random Forest, XGBoost, Gradient Boosting Machine, Elastic Net, and Support Vector Machine. The performance of the models was comprehensively evaluated using the area under the curve (AUC), integrated calibration index (ICI), precision-recall curve (PR), and decision curve analysis (DCA). Feature importance was ranked and explained using SHAP values.
RESULTS: Among the 434 patients, 156 individuals (35.9%) were diagnosed with grade ≥ 3 VUR. Seven predictive features were identified: delayed treatment response, annual UTI episodes, hydronephrosis, abnormal renal signal on MRU, febrile UTI (fUTI)/UTI ratio, age at first UTI, and non-Escherichia coli UTI episodes. Discrimination performance was comparable across the six models (test AUC range: 0.810-0.832; all pairwise DeLong tests p > 0.05); XGBoost was selected as the final model based on its good calibration (ICI=0.074) and consistent clinical net benefit, achieving a test AUC of 0.832 (95% CI: 0.757-0.897). Decision curve analysis showed that the model provided net benefits across threshold probabilities ranging from 0.15 to 0.90.
CONCLUSIONS: We developed and validated a prediction model based on XGBoost, which can be used to assess the risk of high-grade vesicoureteral reflux in children with urinary tract infections. This model has good discrimination, acceptable calibration, and considerable clinical application potential. Additionally, based on this model, we developed and deployed a free online calculator at https://vur-prediction.yezhiqiu.cn, which can assist in real-time individualized risk assessment.
METHODS: A total of 434 children with urinary tract infections who completed VCUG examination were retrospectively enrolled in this study. They were randomly divided into a training set (n = 304) and a test set (n = 130) in a 7:3 ratio. LASSO regression was used for feature selection. Based on the selected features, six machine learning prediction models were constructed: Logistic Regression, Random Forest, XGBoost, Gradient Boosting Machine, Elastic Net, and Support Vector Machine. The performance of the models was comprehensively evaluated using the area under the curve (AUC), integrated calibration index (ICI), precision-recall curve (PR), and decision curve analysis (DCA). Feature importance was ranked and explained using SHAP values.
RESULTS: Among the 434 patients, 156 individuals (35.9%) were diagnosed with grade ≥ 3 VUR. Seven predictive features were identified: delayed treatment response, annual UTI episodes, hydronephrosis, abnormal renal signal on MRU, febrile UTI (fUTI)/UTI ratio, age at first UTI, and non-Escherichia coli UTI episodes. Discrimination performance was comparable across the six models (test AUC range: 0.810-0.832; all pairwise DeLong tests p > 0.05); XGBoost was selected as the final model based on its good calibration (ICI=0.074) and consistent clinical net benefit, achieving a test AUC of 0.832 (95% CI: 0.757-0.897). Decision curve analysis showed that the model provided net benefits across threshold probabilities ranging from 0.15 to 0.90.
CONCLUSIONS: We developed and validated a prediction model based on XGBoost, which can be used to assess the risk of high-grade vesicoureteral reflux in children with urinary tract infections. This model has good discrimination, acceptable calibration, and considerable clinical application potential. Additionally, based on this model, we developed and deployed a free online calculator at https://vur-prediction.yezhiqiu.cn, which can assist in real-time individualized risk assessment.