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

Area of Science:

• Urology
• Medical Informatics
• Surgical Outcomes

Background:

• Hypospadias is a common congenital malformation with significant postoperative complication rates.
• Predicting these complications is crucial for improving surgical outcomes and patient quality of life.
• Current methods for predicting complication risk are limited, highlighting the need for advanced analytical tools.

Purpose of the Study:

• To develop and validate machine learning models for predicting postoperative complications in hypospadias repair.
• To identify key clinical variables associated with complication risk.
• To assess the performance of various machine learning algorithms in this predictive task.

Main Methods:

• Retrospective analysis of 671 hypospadias repair cases.
• Feature selection using Least Absolute Shrinkage and Selection Operator (LASSO) regression.
• Development and evaluation of five machine learning models: Random Forest, XGBoost, LightGBM, Logistic Regression, and Support Vector Machine (SVM).
• Performance assessment using AUC, calibration curves, Brier score, and SHapley Additive exPlanations (SHAP).

Main Results:

• Four key predictors identified: hypospadias type, surgical technique, surgeon experience, and patient age.
• Overall complication rate was 22.9%.
• The SVM model achieved the best performance in the validation set (AUC: 0.810, Brier score: 0.157), with LightGBM showing comparable results.
• SHAP analysis indicated surgical technique as the most influential predictor.

Conclusions:

• An interpretable SVM-based model can effectively stratify risk for postoperative complications after hypospadias repair using routine clinical variables.
• SHAP analysis offers valuable visual insights into risk factors for clinicians.
• Further multicenter prospective studies are needed to validate these findings for clinical implementation.