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

Area of Science:

• Obstetrics and Gynecology
• Urogynecology
• Public Health

Background:

• Urinary incontinence is a prevalent and debilitating condition affecting many women.
• Despite its preventability, progress in reducing incontinence rates has been limited.
• Effective risk identification is essential for targeted prevention strategies.

Purpose of the Study:

• To develop and validate a predictive model (nomogram) for postpartum urinary incontinence.
• To identify key risk factors for postpartum urinary incontinence.
• To provide a tool for early risk assessment during pregnancy and the postpartum period.

Main Methods:

• A prospective cohort of women was enrolled in a tertiary maternity hospital.
• Logistic regression was employed for predictor selection in a nomogram.
• Model performance was assessed using Area Under the ROC Curve (AUC), Hosmer-Lemeshow test, and calibration curves.
• Internal and temporal validation were performed using bootstrapping and data splitting.

Main Results:

• Seven predictors were identified: birth mode, pre-pregnancy and pregnancy incontinence, residence, feeding pattern, parity, and age at first birth.
• The nomogram achieved good discrimination with an AUC of 0.757 in the derivation cohort and 0.759 in the validation cohort.
• The model demonstrated good calibration and reliability in both cohorts.

Conclusions:

• The developed nomogram is a practical and dependable tool for assessing postpartum incontinence risk.
• This tool can be utilized during pregnancy and the postnatal period for personalized risk estimation.
• Implementing this nomogram can facilitate timely interventions and improve prevention of postpartum urinary incontinence.