ArticleWorld journal of urology2026
Development and validation of a nomogram for predicting ureteral stricture after ureteroscopic lithotripsy.
Article in World journal of urology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Machine learning-based prediction of Clavien-Dindo ≥ II complications after ureteroscopy.International urology and nephrology · 2026Article
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Abstract
objectiveThe aim of this study is to develop and validate a nomogram model for predicting the risk of ureteral stricture in patients undergoing ureteroscopic lithotripsy for ureteral stones.
methodsThis retrospective study involved the analysis of 251 patients who underwent ureteroscopic lithotripsy at a single center. Patients were classified into two groups: those who developed strictures (n = 24) and those who did not (n = 227), based on postoperative follow-up data. Clinical and preoperative computed tomography parameters were collected for analysis. Univariate and multivariate binary logistic regression analyses were conducted to identify independent risk factors for ureteral stricture. A nomogram prediction model was subsequently constructed using these factors and underwent internal validation through bootstrap resampling. The model's performance was evaluated using receiver operating characteristic curves, calibration plots, and decision curve analysis.
resultsMultivariate analysis identified the maximum transverse diameter of the stone (adjusted odds ratio = 1.83, 95% confidence interval: 1.31-2.57), the maximum ureteral diameter proximal to the stone, and the thickness of the ureteral wall at the stone site as independent risk factors for postoperative ureteral stricture. The nomogram model incorporating these variables demonstrated excellent predictive performance, as evidenced by an area under the receiver operating characteristic curve of 0.951 (95% CI: 0.911-0.992).
conclusionThis model shows promise as a risk-stratification tool that could aid clinicians in identifying high-risk patients for enhanced counseling and consideration of early preventive strategies, following successful external validation.
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