Machine Learning Models for Predicting Postoperative Complications and Hospitalization After Percutaneous Nephrolithotomy

PCNL treatment is often associated with complications of hemorrhagic or infectious origin, which can result in prolonged hospitalization. This study aims to develop predictive models using machine learning (ML) techniques to anticipate these outcomes. Multiple ML algorithms—including Logistic Regression, Decision Tree, Random Forest, and Extreme Gradient Boosting—were evaluated on separate validation and test datasets. The Random Forest model achieved the highest predictive performance for hospitalization need (AUC 0.726/0.736) and infectious complications (AUC 0.799/0.735). Threshold adjustment was applied to increase sensitivity, reducing false negatives. The interpretability of the models was ensured through SHAP analysis, identifying clinically meaningful variables. Risk factors for both hospitalization and infectious complications models included nephrostomy drainage, a neutrophils percentage higher than 80, Guy’s score of grade 4, leukocytes level higher than 15 or lower than 4.5, and balloon dilation, while protective features included tubeless intervention, easy localization of a stone, negative culture, and microorganism results. However, no model achieved acceptable performance for predicting hemorrhagic complications, likely due to limited data. These results suggest that AI-based models can contribute to risk stratification after PCNL. Further experiments with larger, multi-center datasets are needed to confirm these findings and improve the generalizability of the models.