Construction and validation of a nomogram model for prediction of overactive bladder in female nurses

This study aimed to construct a risk prediction model for female nurses with overactive bladder (OAB) and verify its predictive performance. A total of 738 female nurses from 8 hospitals in Gansu Province were enrolled between February and March 2023. Data were collected using a self-designed questionnaire. Binary logistic regression analysis was used to construct the model based on OAB incidence, followed by nomogram development. The model underwent internal and external validation. Five predictors were identified: urination behavior, urination symptoms, urine storage symptoms, urinary incontinence symptoms, and a history of urinary tract infection. The nomogram showed an area under curve of 0.789 (sensitivity: 0.824; specificity: 0.656) in internal validation and 0.846 (sensitivity: 0.707; specificity: 0.828) in external validation. The calibration curve confirmed the model's discriminative and calibrative capacity. The nomogram serves as an intuitive clinical tool for OAB risk stratification. Timely interventions targeting these identified factors could potentially decrease the incidence of OAB among female nursing staff.