Development and validation of a predictive model for urinary incontinence postpartum: a prospective longitudinal...

Urinary incontinence is a common and burdensome problem amongst women. Although it can be prevented, little progress has been made. Identifying individuals at risk remains the key to the prevention of urinary incontinence.

Eligible women were enrolled in the obstetric wards of a tertiary maternity hospital in 2020. Urinary incontinence was measured using a well-accepted questionnaire. Logistic regression with a backward stepwise process was used for predictor selection. The discrimination and calibration of the nomogram were measured using the area under the ROC curve (AUC), Hosmer-Lemeshow test and calibration curve respectively. Bootstrapping procedure with 1000 resamples was used for internal validity. A temporal split (7:3) was made and data collected from the later period were used for temporal validation.

Seven predictors including birth mode, urinary incontinence before and during pregnancy, place of residence, feeding pattern, parity, and age at first birth remained in the final model. The AUC was 0.757 (95% CI: 0.72-0.80) and 0.759 (95% CI: 0.70-0.82) in the derivation and validation cohorts respectively. No significant differences were detected in the Hosmer-Lemeshow test and calibration curve in both cohorts.

The nomogram proved to be a convenient and reliable tool in the clinical setting for risk assessment of postpartum incontinence, which can be applied during pregnancy and the postnatal period for individual risk estimates of postpartum incontinence.