Machine Learning for Predicting Postoperative Complications After Hypospadias Surgery: A 10-Year Single-Center Retrospective Cohort Study

作者：Li Li, Haosen Shen, Ying Qiu, Baoling Bai, Kexin Zhang, Shuangshuang Yang, Chen Shen, Jiaxin Cheng, Qin Zhang, Xianghui Xie · 发表于：Children · 年份：2026 · DOI：10.3390/children13070962 · 研究领域：Urological Disorders and Treatments、Hernia repair and management、Pediatric Urology and Nephrology Studies

Objectives: Hypospadias is one of the most common congenital malformations of the male genitourinary system, and postoperative complications remain a major concern affecting surgical outcomes and patients‘ quality of life. Whether machine learning models can effectively predict complication risk using routinely available clinical variables remains unclear. Methods: A retrospective analysis was performed on 671 hypospadias patients who underwent urethroplasty at the Department of Urology, Capital Children’s Medical Center, between December 2015 and September 2024. The final dataset included 671 patients (training set: 536; validation set: 135). The median follow-up duration was 48 months (range: 19 to 72 months). Least absolute shrinkage and selection operator (LASSO) regression with nested cross-validation within the training set was used for feature selection, followed by the development of five machine learning models (Random Forest, XGBoost, LightGBM, Logistic Regression, and Support Vector Machine). Model performance was evaluated using AUC, calibration curves, Brier score, and decision curve analysis. Feature importance was assessed using SHapley Additive exPlanations (SHAP). Results: LASSO retained four features for model development: hypospadias type, surgical technique, surgeon experience, and patient age. The overall complication rate was 22.9% (154/671). Among the models evaluated, the Support Vector Machine (SVM) showed the most balanced performance in the validati...