A nomogram for predicting bladder dysfunction in patients with type 2 diabetes mellitus: a retrospective study - Yingjie Hu; Fengming Hao; Ying Wang; Ling Chen; Lihua Wen; Jue Li; Wei Ren; Wenzhi Cai (PeerJ)
Background Diabetic bladder dysfunction (DBD) is a common urinary complication in diabetic patients, significantly affecting their overall well-being and quality of life, and placing a considerable burden on healthcare resources. Early prevention is crucial; however, the absence of a simple and effective tool to predict DBD onset remains a significant challenge. This study aims to identify risk factors for DBD in patients with type 2 diabetes mellitus (T2DM) and to develop a predictive nomogram for clinical application. Methods This retrospective study included patients with T2DM treated at two hospitals. Data from patients treated at one hospital between January 2020 and August 2023 were used to create the training set, while data from patients treated at another hospital between March 2022 and October 2023 were used to create the validation set. Patients were classified into two groups based on the presence or absence of DBD: the DBD group and the non-DBD group. Significant factors identified via bivariate analysis (P < 0.05) were incorporated into multivariate logistic regression to construct a predictive model, and a corresponding nomogram was developed. The model’s performance was assessed using receiver operating characteristic (ROC) curves, calibration plots, decision curve analysis (DCA), and clinical impact plots (CIC), with validation performed through 1,000 bootstrap resamplings. Results A total of 1,010 participants were included in this study, with a DBD incidence rate of 38.81% (392/1,010). Multivariate logistic regression analysis identified HbA1c, PCP-2h, DPN, TCO2, PAB, T-Bil, I-Bil, IgE, URBC, UI and UR as independent risk factors for DBD. A nomogram was constructed based on these factors. Both internal and external validations demonstrated the good predictive performance of the nomogram. The area under the curve (AUC) for the training and validation datasets was 0.897 and 0.862, respectively. The calibration curve showed a high degree of consistency. Results from DCA and CIC indicated that the prediction model had high clinical utility. Conclusions A predictive model and nomogram for DBD in T2DM patients were developed, demonstrating strong accuracy and clinical utility, aiding in early DBD risk assessment and intervention.