Analysis of influencing factors and construction of risk prediction model for urinary reten-tion in elderly patients after endoscopic submucosal dissection

Objective To investigate the influencing factors for urinary retention in elderly patients after endoscopic submucosal dissection (ESD), and to construct and verify a risk prediction model.

Methods The retrospective cohort study was conducted. The clinical data of 1 825 elderly patients who underwent ESD at The First Affiliated Hospital with Nanjing Medical University from October 2022 to September 2025 were collected. There were 1 207 males and 618 females, aged 68(63,72) years. All 1 825 patients were divided into a training set of 1 278 cases and a validation set of 547 cases based on the random number table method at a ratio of 7∶3. The training set was used to construct a risk prediction model, and the validation set was used to verify the model′s perfor-mance. Observation indicators: (1) analysis of influencing factors for urinary retention in elderly patients after ESD; (2) construction and efficacy evaluation of a predictive model for urinary retention in elderly patients after ESD. Comparison of measurement data with skewed distribution between groups was conducted using the Mann‑Whitney U test. Comparison of count data between groups was conducted using the chi‑square test or Fisher exact probability. Comparison of ordinal data between groups was conducted using the Wilcoxon rank‑sum test. Univariate analysis was performed using statistical methods appropriate to the data type. Multivariate analysis was conducted using the Logistic regression model. A nomogram prediction model was constructed based on regression coefficients, and efficacy evaluation of the model was conducted using the area under the curve (AUC) of receiver operating characteristic curve, the calibration curve and decision curve.

Results (1) Analysis of influencing factors for urinary retention in elderly patients after ESD: results of multivariate analysis in 1 278 elderly patients of the training set showed that male, history of stroke, history of dysuria, prolonged operation time, and increased postoperative pain score were independent risk factors for urinary retention after ESD (odds ratio=1.759, 2.773, 12.619, 1.016, 1.300, 95% confidence interval as 1.072-3.271, 1.213-5.806, 4.197-38.668, 1.006-1.027, 1.067-1.573, P<0.05). (2) Construction and efficacy evaluation of a predictive model for urinary retention in elderly patients after ESD: based on the results of multivariate analysis, a nomogram prediction model for urinary retention in elderly patients after ESD was constructed. The AUC of the nomogram prediction model in the training set was 0.741 (95% confidence interval as 0.679-0.804), and the AUC in the validation set was 0.705 (95% confidence interval as 0.611-0.799). The calibration curve analysis showed that the predictive probability of the nomogram prediction model in both the training set and the validation set had high consistency with the observed probability. The decision curve analysis showed that the nomogram prediction model had good clinical net benefit in both the training set and the validation set.

Conclusions Male, history of stroke, history of dysuria, prolonged operation time, and increased postoperative pain score are independent risk factors for urinary retention in elderly patients after ESD. The nomogram model constructed based on these factors exhibits good predictive performance and high clinical application value.