Abstract
Objectives:
To identify risk factors for ureteral stricture after endoscopic lithotripsy for impacted ureteral stones and to develop and externally validate a machine-learning-based prognostic model.
Methods:
This multicenter study enrolled patients with impacted ureteral stones in China. During model construction, controls were matched 1:1 with stricture cases based on gender, age, year of surgical procedure, and surgeon to minimize the influence of variability in surgical equipment and techniques. The primary outcome was postoperative ureteral stricture. The model development cohort consisted of patients who underwent endoscopic laser lithotripsy at the Second Hospital of Tianjin Medical University between 2012 and 2021. External validation was performed using independent datasets from the Tongji Hospital of Tongji Medical College, the Huazhong University of Science and Technology, and the Tianjin Medical University General Hospital between 2022 and 2023.
Results:
Among the six machine learning models, the least absolute shrinkage and selection operator regression model demonstrated the best overall performance, achieving an area under the receiver operating characteristic curve (AUC) of 0.814 in the internal cohort and AUCs of 0.794 and 0.782 in the two external validation cohorts, respectively. The final model incorporated seven readily available clinical features: residual stone, ureteral wall thickness, stone burden, stone culture, surgical method, history of ureter-related procedure, and intraoperative ureteral injury grade. To enhance clinical usability, the model was further translated into a user-friendly predictive tool.
Conclusions:
The developed model accurately predicts the risk of ureteral stricture using perioperative clinical variables. This tool may assist clinicians in surgical planning and postoperative management for patients with impacted ureteral stones.