TL;DR
By integrating routinely available perioperative variables, the model enables individualized risk stratification and may support preoperative counseling and clinical decision-making.
Overall, these findings suggest that a machine learning–based approach may provide complementary individualized risk stratification for postoperative stone-free status after RIRS when used alongside established clinical judgment and existing scoring systems.
Stone location, particularly proximal ureteral location, showed a risk-increasing pattern in univariable analysis, SHAP interpretation, and meta-analysis, and these results may support individualized preoperative assessment before attempted placement of a planned 11/13F UAS.
SHAP analysis revealed that stone burden, preoperative serum creatinine, preoperative white blood cell count, stone complexity, and hydronephrosis were the most critical predictors of SFS.
A standardized framework for reporting complications following endoscopic enucleation of the prostate is proposed using a modified Clavien-Dindo (CD) classification, and predictors of postoperative complications following HoLEP are identified.
This model provides a reference for surgical risk stratification, which helps establish personalized postoperative monitoring strategies and demonstrates an interpretable machine learning model to effectively predict POF following fURL.
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