Risk prediction models for stress urinary incontinence after pelvic organ prolapse (POP) surgery: a systematic review and meta-analysis
Abstract Objective To systematically evaluate existing developed and validated predictive models for stress urinary incontinence after pelvic floor reconstruction. Methods Relevant literature in PubMed, Embase, Web of Science, Cochrane Library, OVID, China National Knowledge Infrastructure(CNKI), Wa...
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| Language: | English |
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BMC
2025-02-01
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| Series: | BMC Women's Health |
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| Online Access: | https://doi.org/10.1186/s12905-025-03584-8 |
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| author | Bi Jun Yu Hao Chong He Li Wang Han Mei Shao Ying Min Liu Xiao Ying Yan Jian Liu |
| author_facet | Bi Jun Yu Hao Chong He Li Wang Han Mei Shao Ying Min Liu Xiao Ying Yan Jian Liu |
| author_sort | Bi Jun Yu |
| collection | DOAJ |
| description | Abstract Objective To systematically evaluate existing developed and validated predictive models for stress urinary incontinence after pelvic floor reconstruction. Methods Relevant literature in PubMed, Embase, Web of Science, Cochrane Library, OVID, China National Knowledge Infrastructure(CNKI), Wan Fang Database, VIP database and Chinese Biomedical Literature Service System (SinoMed) were search from inception to 1 March 2024. Literature screening and data extraction were performed independently by two researchers. The chosen study’s statistics included study design, data sources, outcome definitions, sample size, predictors, model development, and performance. The Predictive Modelling Risk of Bias Assessment Tool (PROBAST) checklist was used to assess risk of bias and applicability. Results A total of 7 studies containing 9 predictive models were included. All studies had a high risk of bias, primarily due to retrospective design, small sample sizes, single-center trials, lack of blinding, and missing data reporting. The meta-analysis revealed moderate heterogeneity (I² = 68.8%). The pooled AUC value of the validated models was 0.72 (95% CI: 0.65, 0.79), indicating moderate predictive ability. Conclusion The prediction models evaluated demonstrated moderate discrimination, but significant bias and methodological flaws. The meta-analysis revealed moderate heterogeneity (I² = 68.8%) among the included studies, reflecting differences in study populations, predictors, and methods, which limits the generalizability of the findings. Despite these challenges, these models highlight the potential to identify high-risk patients for targeted interventions to improve surgical outcomes and reduce postoperative complications. The findings suggest that by integrating these models into clinical decision-making, clinicians can better tailor surgical plans and preoperative counseling, thereby improving patient satisfaction and reducing the incidence of postoperative stress urinary incontinence. Future research should follow TRIPOD and PROBAST principles, focus on addressing sources of heterogeneity, improve model development through robust designs, large sample sizes, comprehensive predictors, and novel modelling approaches, and validate tools that can be effectively integrated into clinical decision-making to manage stress urinary incontinence after pelvic floor reconstruction. |
| format | Article |
| id | doaj-art-48488a1236f84342a84b020733cc0c69 |
| institution | Kabale University |
| issn | 1472-6874 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Women's Health |
| spelling | doaj-art-48488a1236f84342a84b020733cc0c692025-02-09T12:52:47ZengBMCBMC Women's Health1472-68742025-02-0125111310.1186/s12905-025-03584-8Risk prediction models for stress urinary incontinence after pelvic organ prolapse (POP) surgery: a systematic review and meta-analysisBi Jun Yu0Hao Chong He1Li Wang2Han Mei Shao3Ying Min Liu4Xiao Ying Yan5Jian Liu6Guangdong Pharmaceutical UniversityGuangdong Jiangmen Chinese Medicine CollegePeople’s HospitalJiangmen Central hospitalJiangmen Central hospitalGuangdong Pharmaceutical UniversityJiangmen Central hospitalAbstract Objective To systematically evaluate existing developed and validated predictive models for stress urinary incontinence after pelvic floor reconstruction. Methods Relevant literature in PubMed, Embase, Web of Science, Cochrane Library, OVID, China National Knowledge Infrastructure(CNKI), Wan Fang Database, VIP database and Chinese Biomedical Literature Service System (SinoMed) were search from inception to 1 March 2024. Literature screening and data extraction were performed independently by two researchers. The chosen study’s statistics included study design, data sources, outcome definitions, sample size, predictors, model development, and performance. The Predictive Modelling Risk of Bias Assessment Tool (PROBAST) checklist was used to assess risk of bias and applicability. Results A total of 7 studies containing 9 predictive models were included. All studies had a high risk of bias, primarily due to retrospective design, small sample sizes, single-center trials, lack of blinding, and missing data reporting. The meta-analysis revealed moderate heterogeneity (I² = 68.8%). The pooled AUC value of the validated models was 0.72 (95% CI: 0.65, 0.79), indicating moderate predictive ability. Conclusion The prediction models evaluated demonstrated moderate discrimination, but significant bias and methodological flaws. The meta-analysis revealed moderate heterogeneity (I² = 68.8%) among the included studies, reflecting differences in study populations, predictors, and methods, which limits the generalizability of the findings. Despite these challenges, these models highlight the potential to identify high-risk patients for targeted interventions to improve surgical outcomes and reduce postoperative complications. The findings suggest that by integrating these models into clinical decision-making, clinicians can better tailor surgical plans and preoperative counseling, thereby improving patient satisfaction and reducing the incidence of postoperative stress urinary incontinence. Future research should follow TRIPOD and PROBAST principles, focus on addressing sources of heterogeneity, improve model development through robust designs, large sample sizes, comprehensive predictors, and novel modelling approaches, and validate tools that can be effectively integrated into clinical decision-making to manage stress urinary incontinence after pelvic floor reconstruction.https://doi.org/10.1186/s12905-025-03584-8Clinical predictorsPelvic floor reconstructionPrediction modelStress urinary incontinenceSystematical analysis |
| spellingShingle | Bi Jun Yu Hao Chong He Li Wang Han Mei Shao Ying Min Liu Xiao Ying Yan Jian Liu Risk prediction models for stress urinary incontinence after pelvic organ prolapse (POP) surgery: a systematic review and meta-analysis BMC Women's Health Clinical predictors Pelvic floor reconstruction Prediction model Stress urinary incontinence Systematical analysis |
| title | Risk prediction models for stress urinary incontinence after pelvic organ prolapse (POP) surgery: a systematic review and meta-analysis |
| title_full | Risk prediction models for stress urinary incontinence after pelvic organ prolapse (POP) surgery: a systematic review and meta-analysis |
| title_fullStr | Risk prediction models for stress urinary incontinence after pelvic organ prolapse (POP) surgery: a systematic review and meta-analysis |
| title_full_unstemmed | Risk prediction models for stress urinary incontinence after pelvic organ prolapse (POP) surgery: a systematic review and meta-analysis |
| title_short | Risk prediction models for stress urinary incontinence after pelvic organ prolapse (POP) surgery: a systematic review and meta-analysis |
| title_sort | risk prediction models for stress urinary incontinence after pelvic organ prolapse pop surgery a systematic review and meta analysis |
| topic | Clinical predictors Pelvic floor reconstruction Prediction model Stress urinary incontinence Systematical analysis |
| url | https://doi.org/10.1186/s12905-025-03584-8 |
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