Development and validation of a depression risk prediction nomogram for US women with urinary incontinence, based on NHANES 2007–2018

Objective 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. Methods 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. Results 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. Conclusions 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.