Systematic review of predictive models for stress urinary incontinence in pregnant and postpartum women
10.3760/cma.j.cn115682-20241021-05728
- VernacularTitle:孕产妇压力性尿失禁预测模型的系统评价
- Author:
Xiaoying LIANG
1
;
Jialu ZHANG
;
Tianyi WANG
;
Caile ZHANG
;
Jie CHEN
;
Guorong FAN
;
Dongying ZHANG
;
Meng ZHANG
;
Yilin LI
;
Haixin BO
Author Information
1. 中国医学科学院北京协和医学院护理学院，北京 100144
- Publication Type:Journal Article
- Keywords:
Maternity care;
Stress urinary incontinence;
Predictive model;
Systematic review
- From:
Chinese Journal of Modern Nursing
2025;31(12):1619-1627
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To systematically evaluate predictive models for stress urinary incontinence (SUI) in pregnant and postpartum women, providing a reference for model development, application, and promotion.Methods:A comprehensive literature search was conducted in PubMed, Embase, Web of Science, Cochrane Library, China National Knowledge Infrastructure, Wanfang Database, and China Biology Medicine disc for studies on SUI predictive models in pregnant and postpartum women. The search period was from database inception to September 30, 2024. Two researchers independently screened the literature and extracted data according to inclusion and exclusion criteria. The risk of bias in the predictive models was assessed using the prediction model risk of bias assessment tool.Results:A total of 23 studies were included, covering 31 predictive models for SUI, with a combined sample size of 14 473 women. Among them, six models focused on predicting SUI in pregnant women, while 25 models were developed for postpartum SUI. The predictive factors identified in these models were categorized into nine groups, including: general information for pregnant and postpartum women, delivery data, neonatal data, past history, abortion history, lifestyle data, pelvic floor muscle screening results, 2D and 3D ultrasound data, and serological indicators. Among these, age, mode of delivery, parity, body mass index, history of SUI, and neonatal weight were widely recognized as key predictive factors. External validation was performed in five studies. Five studies showed good applicability and low bias risk, except for one study that had limitations in both bias risk and applicability, and the remaining studies exhibited a high risk of bias but demonstrated good applicability.Conclusions:The methodological quality of SUI predictive models for pregnant and postpartum women needs further improvement. External validation remains insufficient. Future model development should be based on large-sample, prospective studies, incorporating appropriate predictive factors and stratifying SUI risk in different populations to enhance clinical applicability.
