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Prediction models for postpartum stress urinary incontinence: A systematic review
Liyun Wang1, Minghui Zhang1, Kaihui Sha1
1School of Nursing, Binzhou Medical University, Shandong, China.
Heliyon
|October 9, 2024
Summary
Existing models for predicting postpartum stress urinary incontinence have a high risk of bias and methodological flaws. Further development is needed to create reliable and clinically applicable predictive tools for this common condition.
Area of Science:
- Obstetrics and Gynecology
- Women's Health Research
- Clinical Prediction Modeling
Background:
- Postpartum stress urinary incontinence (SUI) significantly affects women's quality of life and health.
- Reliable predictive models for postpartum SUI are crucial for prevention.
- Current models' quality and clinical applicability are largely unknown.
Purpose of the Study:
- To systematically review and evaluate existing models for predicting postpartum SUI risk.
- Assess the quality and applicability of current predictive models.
Main Methods:
- Systematic search of multiple databases (PubMed, Embase, Web of Science, etc.) up to October 2023.
- Utilized CHARMS checklist for data extraction and PROBAST for bias and applicability assessment.
- Included eight papers with ten postpartum SUI prediction models.
Main Results:
- Common predictors identified: pregnancy UI, delivery mode, maternal age, parity, and pre-pregnancy UI.
- Nine models showed discrimination (AUC/C-index) between 0.680-0.850.
- All studies exhibited high risk of bias due to methodological issues.
Conclusions:
- Postpartum SUI prediction models are in early development stages.
- Existing models suffer from high bias and poor methodological quality, limiting clinical use.
- Future models require adherence to development norms for improved performance and applicability.
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