To develop risk predictive models of postpartum stress urinary incontinence (SUI) for both primiparous and multiparous women.
From July 2016 to July 2017, 815 singleton pregnant women without incontinence before pregnancy who were 18 years or older and admitted to 2 hospitals in Shenzhen, China, were enrolled. Pregnancy-related data were collected at enrollment. Delivery information was obtained from electronic medical records. Telephone follow-up was conducted to investigate SUI at 6 weeks postpartum. Multivariable logistic regression analyses using stepwise selection were used to establish predictive models for postpartum SUI for all women, and separately for primiparous and multiparous. Internal validation of the models was performed with discrimination and calibration using a bootstrapping (1,000 resampling) method.
The analysis included 727 participants. The prevalence of postpartum SUI was 15.96% (116/727), 12.5% (49/393) for primiparous women and 20.1% (67/334) for multiparous women, with a significant difference between them (p = 0.008). For primiparous women, the predictive postpartum SUI model included age, abortion/miscarriage history, SUI during pregnancy, and mode of delivery. For multiparous women, pre-pregnancy BMI, abortion/miscarriage history, SUI during pregnancy, and mode of delivery were included in the model. There was satisfactory calibration between the models' predicted probability of postpartum SUI and the observed probability for both primiparous and multiparous women (Hosmer-Lemeshow test, p = 0.390 for primiparous and 0.364 for multiparous women). The optimism-corrected C-statistic of the models by bootstrapping stepwise was 0.763 (95% confidence interval [CI]: 0.693-0.833) for primiparous women and 0.783 (95% CI: 0.726-0.841) for multiparous women.
We developed predictive models of postpartum SUI for both primiparous and multiparous women. This approach may provide a useful tool for high-risk prediction of postpartum SUI before and after delivery.
Urologia internationalis. 2020 Aug 05 [Epub ahead of print]
Ling Chen, Dan Luo, Xiaomin Chen, Mei Jin, Xiajuan Yu, Wenzhi Cai
Nursing Department, Shenzhen Hospital, Southern Medical University, Shenzhen, China., Department of Neonatology, Shenzhen Maternal & Child Healthcare Hospital, Shenzhen, China., Nursing Department, Shenzhen Hospital, Southern Medical University, Shenzhen, China, .