A nomogram for predicting bladder dysfunction in patients with type 2 diabetes mellitus: a retrospective study.

Area of Science:

• Urology
• Endocrinology
• Medical Informatics

Background:

• Diabetic bladder dysfunction (DBD) is a common complication in type 2 diabetes mellitus (T2DM) patients.
• It significantly impacts quality of life and increases healthcare costs.
• Early prediction of DBD is challenging due to the lack of effective tools.

Purpose of the Study:

• To identify independent risk factors for DBD in T2DM patients.
• To develop and validate a predictive nomogram for clinical application.

Main Methods:

• Retrospective study of 1,010 T2DM patients.
• Bivariate analysis and multivariate logistic regression to identify risk factors.
• Nomogram construction and validation using ROC curves, calibration plots, DCA, and CIC.

Main Results:

• Identified HbA1c, PCP-2h, DPN, TCO2, PAB, T-Bil, I-Bil, IgE, URBC, UI, and UR as independent risk factors for DBD.
• Developed a nomogram with strong predictive performance (AUC 0.897 training, 0.862 validation).
• Demonstrated high clinical utility and consistency through validation analyses.

Conclusions:

• A validated predictive nomogram for DBD in T2DM patients has been developed.
• The nomogram shows high accuracy and clinical utility for early risk assessment.
• This tool can aid in timely intervention strategies for DBD.