ArticleInternational urogynecology journal2022
Online prediction tool for female pelvic floor dysfunction: development and validation.
Article in International urogynecology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed, 3 citations in OpenAlex.
- Development and Validation of Risk Assessment Model for Pelvic Organ Prolapse Based on A Retrospective Study with Machine Learning Algorithms.International urogynecology journal · 2025Article
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6 authors at 4 institutions in 1 country.
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Abstract
introduction and hypothesisThe aim was to develop and validate (internally and externally) a prediction model for the presence and diagnosis of pelvic floor dysfunction (PFD) in women, including pelvic organ prolapse, stress urinary incontinence and/or overactive bladder via a patient-completed online tool.
methodsUsing a retrospective cohort of women aged >18 years, from multiple tertiary gynaecology units within Queensland, Australia (2014-2018), the prediction model was developed via penalized logistic regression with internal and external validation utilizing multiple clinical predictors (42 questions from the Australian Pelvic Floor Questionnaire and demographics: age, body mass index, parity and mode of delivery). The main outcome measures were the accuracy of the model in predicting a diagnosis of pelvic floor dysfunction and its specific conditions of prolapse and incontinence.
resultsA total of 3,501 women were utilized for model development and internal validation and 449 for external validation. On internal validation the model correctly identified those with PFD with 97% sensitivity, 74% specificity and a concordance index (C-index) of 0.96. Predictions of pelvic organ prolapse were also accurate, with 86% sensitivity, 83% specificity, C-index 0.83, as was stress urinary incontinence, 84% sensitivity, 87% specificity, C-index 0.87, and overactive bladder, 76% sensitivity, 77% specificity, C-index 0.77. External validation confirmed the model's accuracy with a similar C-index in all parameters.
conclusionsThis model provides an accurate online tool to differentiate between those with and without PFD and diagnoses of common pelvic floor disorders. It serves as a valuable self-assessment for women and primary care providers.
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