Developing a predictive model for early urinary incontinence after laparoscopic radical prostatectomy: a...

To create a predictive model for early urinary incontinence following laparoscopic radical prostatectomy (LRP).

This study examined clinical data from 408 patients who received LRP at Xiangyang Central Hospital during the period December 2020 to December 2025. Patients were divided into groups based on the presence or absence of urinary incontinence. Univariate and multivariate logistic regression analyses were conducted to identify independent predictors of urinary incontinence, which were then used to construct a nomogram. The predictive model's discrimination, calibration, and clinical utility were assessed via receiver operating characteristic (ROC), calibration, and decision curves.

The analysis included 408 patients. Among the participants, 168 (41.2%) were categorized as having urinary incontinence, while 240 (58.8%) were not. Univariate logistic regression analysis identified prostate volume, membranous urethral length (MUL), intravesical prostatic protrusion length (IPPL), clinical staging and neurovascular bundle (NVB) preservation as predictors of urinary incontinence. Multivariate logistic regression analysis further confirmed that prostate volume, MUL, IPPL and NVB preservation were independent predictors. Subsequently, a clinical prediction model was developed and visualized as a nomogram, with its discriminative performance evaluated using a ROC curve. The area under the curve (AUC) was 0.910 (95%CI 0.862-0.957), demonstrating good discriminatory power. Good consistency was observed from the calibration curve, and a significant clinical net benefit was shown by the decision curve.

The prostate volume, MUL, IPPL and NVB preservation have a good predictive value for early urinary incontinence after LRP, and the resulting nomogram demonstrates good predictive performance.