ArticleAsian journal of urology2026
Transecting or non-transecting: Developing and validating a prediction model for short-segment bulbar urethral strictures in males.
Article in Asian journal of urology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: Excision and primary anastomosis is a standard approach for managing short-segment bulbar urethral strictures. However, the choice between transecting and non-transecting anastomoses is typically determined during surgery based on intraoperative findings. This study aimed to develop a predictive model for patients with short-segment bulbar urethral strictures, forecasting the likelihood of undergoing a non-transecting procedure through preoperative assessments. Methods: Retrospective data collection was conducted for patients diagnosed with short-segment bulbar urethral strictures at our institution from June 1, 2016, to May 31, 2022. The patients were categorized into the transecting and non-transecting groups for the training set. Factors displaying significant differences, as identified through univariate analysis between groups, were integrated into logistic regression for a clinical-radiographic nomogram. External validation utilized prospective cohort data from June 1, 2022, to November 30, 2022. Results: The training set comprised 81 patients (44 transecting and 37 non-transecting), and the validation set had 33 (22 transecting and 11 non-transecting). The final model incorporated two factors: the stricture's etiology and ventral scar thickness measured by ultrasound. The model demonstrated an area under the curve of 0.91 (95% CI: 0.85-0.98) for the training dataset and 0.90 (95% CI: 0.79-1.00) for the validation dataset. The validation metrics demonstrated the model's good discrimination and calibration. Conclusion: This model enables preoperative prediction of the anastomosis strategy for patients with short-segment bulbar urethral strictures, considering etiology (traumatic or non-traumatic) and ultrasonographic measurements (ventral scar thickness). Having successfully undergone internal and external validation, the model demonstrates robust discrimination and calibration, offering crucial support for healthcare professionals in preoperative decision-making.
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