Development and validation of a depression risk prediction nomogram for US women with urinary incontinence, based on...

To develop and internally validate a nomogram for predicting the likelihood of depression among adult women with urinary incontinence (UI) using data from a nationally representative survey.

This study included 6308 women with UI aged 20 years or older from the National Health and Nutrition Examination Survey (NHANES) 2007-2018. The women were selected at random: 75% were the training set and the remaining 25% comprised the testing set. Least absolute shrinkage and selection operator (LASSO) binomial and logistic regression models were used to select the optimal predictive variables. The depression probability was calculated using a predictor-based nomogram. Receiver operating characteristics area under the curve (ROC-AUC), calibration curve, and decision curve analysis (DCA) were used to evaluate the nomogram's performance.

The nomogram included 11 predictors: age, education, ratio of family income to poverty, smoking, stroke, sleep time, trouble sleeping, leakage bother level, daily activities affected, number of nighttime urinations, and moderate-vigorous recreational activity. A nomogram model for depression risk was established based on these predictors. The AUC of the nomogram was 0.811 (95% confidence interval [CI] 0.793-0.829) in the training set and 0.810 (95% CI 0.780-0.839) in the testing set. The nomogram was well calibrated according to the calibration curve. The DCA demonstrated that the nomogram was clinically useful.

This study established a nomogram that is helpful for screening indivudals with UI at high risk of depression and assisting gynecologists in identifying signs of depression in patients and providing treatment.