Development and Validation of a Risk Prediction Model for Female Stress Urinary Incontinence in Rural Fujian, China

Abstract

Purpose: With China's rapidly aging population and the rising proportion of obese people, an increase in the number of women suffering from urinary incontinence (UI) is to be expected. In order to identify high-risk groups before leakage occurs, we aimed to develop and validate a model to predict the risk of stress UI (SUI) in rural women.

Patients and methods: This study included women aged 20-70 years in rural Fujian who participated in an epidemiologic survey of female UI conducted between June and October 2022. Subsequently the data was randomly divided into training and validation sets in a ratio of 7:3. Univariate and multivariate logistic regression analyses were used to identify independent risk factors as well as to further construct a nomogram for risk prediction. Finally, concordance index (C-index), calibration curve and decision curve analysis were applied to evaluate the performance of the predictive models.

Results: A total of 5290 rural females were enrolled, of whom 771 (14.6%) had SUI. Age, body mass index (BMI), postmenopausal status, number of vaginal deliveries, vaginal delivery of large infant, constipation and family history of pelvic organ prolapse (POP) and SUI were included in the nomogram. C-index of this prediction model for the training and validation sets was 0.835 (95% confidence interval [CI] = 0.818-0.851) and 0.829 (95% CI = 0.796-0.858), respectively, and the calibration curves and decision analysis curves for both the training and validation sets showed that the model was well-calibrated and had a positive net benefit.

Conclusion: This model accurately estimated the SUI risk of rural women in Fujian, which may serve as an effective primary screening tool for the early identification of SUI risk and provide a basis for further implementation of individualized early intervention. Moreover, the model is concise and intuitive, which makes it more operational for rural women with scarce medical resources.

Keywords: nomogram; predict model; risk factors; rural women; stress urinary incontinence.

Figure 1

Flowchart for respondent selection and model development and validation.

Figure 2

A nomogram for predicting stress urinary incontinence (SUI) in rural women. A score is assigned to the value of each variable on the variable axis. Each individual score was summed to obtain a total score to determine the corresponding risk of SUI prevalence.

Figure 3

The receiver operating characteristic curve of the predictive model in the training set (A) and the validation set (B). (ROC) curve, equal to concordance index (C-index value).

Figure 4

The calibration curve of the predictive model in the training set (A) and the validation set (B). The Hosmer-Lemeshow test showed no significant deviation between the observed and predicted values (P-values of 0.669 and 0.245 for the training and validation sets, respectively).

Figure 5

The decision curve analysis (DCA) of the predictive model in the training set (A) and the validation set (B). The threshold probabilities of positive net benefits associated with the use of the nomogram to detect SUI in the training and validation cohorts were 0.03 to 0.95 and 0.03 to 0.90, respectively.