Steinstrasse is an important complication following extracorporeal shock wave lithotripsy; however, predictive models for risk factors in pediatric populations remain limited. This study aimed to develop and externally validate an explainable machine learning model for predicting steinstrasse formation after ESWL in children. This two-center retrospective study included 807 pediatric patients (< 18 years) who underwent ESWL for renal stones (2015–2025). The internal cohort comprised 685 patients (Adıyaman), while the external validation cohort consisted of 122 patients (Adana). Twenty-six preoperative variables were evaluated. Optimal predictors were identified through consensus-based feature selection using eight algorithms. Thirteen machine learning algorithms were compared using nested cross-validation. Model interpretability was assessed through SHAP, LIME, and partial dependence analyses. The overall steinstrasse incidence was 10.5% (85/807). Nine predictors were selected: S.T.O.N.E. Score, Triple-D Score, age, stone perimeter, stone depth, Hounsfield unit, stone volume, distal ureteral diameter, and skin-to-stone distance. XGBoost demonstrated optimal external validation performance (AUC = 0.949, sensitivity = 90%, specificity = 99.1%, MCC = 0.891) and exhibited excellent generalizability (ΔAUC = 0.001). Explainable artificial intelligence analyses identified age (mean|SHAP|=1.965) and distal ureteral diameter (mean|SHAP|=1.575) as dominant predictors with significant interaction (SHAP = 0.458). Decision curve analysis confirmed clinical utility across a threshold range of 2%–98%. This externally validated model accurately predicts steinstrasse formation using routine preoperative parameters. Integration of patient age, ureteral anatomy, and stone characteristics may guide decisions between ESWL, prophylactic stenting, or alternative endoscopic approaches in pediatric patients.
Çoban et al. (2026) studied this question.