SynthesisBJOG : an international journal of obstetrics and gynaecology2025
Risk Prediction Models for Stress Urinary Incontinence in Pregnant and Postpartum Women: A Systematic Review and Critical Appraisal.
Synthesis in BJOG : an international journal of obstetrics and gynaecology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning-based prediction model for postpartum stress urinary incontinence risk: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Mode-of-Delivery-Specific Nomograms for Predicting Stress Urinary Incontinence One Year After Childbirth.International urogynecology journal · 2026Article
- Risk Factors and Nomogram for Postpartum Myofascial Pelvic Pain: A Prospective Cohort Study.Journal of pain research · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMany studies have developed or validated prediction models to estimate the risk of stress urinary incontinence (SUI) in pregnant and postpartum women, but the quality of the model development and model applicability remains unknown.
objectivesTo systematically review and critically evaluate currently available prediction models for SUI in pregnant and postpartum women. SEARCH STRATEGY: Cochrane Library, EBSCO, PubMed, Web of Science, EMBASE, Chinese CNKI, Wanfang and VIP databases were searched from inception until February 2024. SELECTION CRITERIA: Prospective cohort or retrospective studies were considered eligible if they developed or validated prediction models for SUI in pregnant or postpartum women. DATA COLLECTION AND ANALYSIS: Two reviewers independently screened the literature, extracted data and evaluated the quality of the included studies using PROBAST. MAIN
resultsA total of 15 models were included. Eleven models were internally validated, including cross-validation and bootstrap and four models were externally validated. The most commonly used predictors were age, body mass index (BMI) and mode of delivery. The area under the curve or C-statistics reported by the modelling and validation groups ranged from 0.602 to 0.888. Only one study had a low risk of bias and 14 studies had a high risk of bias.
conclusionsFourteen models for predicting SUI in pregnant and postpartum women had a high risk of bias according to the PROBAST. Future research should focus on improving the methodological quality of the existing prediction models and developing new models.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.