A model for predicting the risk of de novo stress urinary incontinence in women undergoing pelvic organ prolapse surgery.
Abstract
To construct and validate a prediction model for estimating the risk of de novo stress
urinary incontinence (SUI) after vaginal pelvic organ prolapse (POP) surgery and compare
it with predictions using preoperative urinary stress testing and expert surgeons'
predictions.Using the data set (n=457) from the Outcomes Following Vaginal Prolapse
Repair and Midurethral Sling trial, a model using 12 clinical preoperative predictors
of de novo SUI was constructed. De novo SUI was determined by Pelvic Floor Distress
Inventory responses through 12 months postoperatively. After fitting the multivariable
logistic regression model using the best predictors, the model was internally validated
with 1,000 bootstrap samples to obtain bias-corrected accuracy using a concordance
index. The model's predictions were also externally validated by comparing findings
against actual outcomes using Colpopexy and Urinary Reduction Efforts trial patients
(n=316). The final model's performance was compared with experts using a test data
set of 32 randomly chosen Outcomes Following Vaginal Prolapse Repair and Midurethral
Sling trial patients through comparison of the model's area under the curve against:
1) 22 experts' predictions; and 2) preoperative prolapse reduction stress testing.A
model containing seven predictors discriminated between de novo SUI status (concordance
index 0.73, 95% confidence interval [CI] 0.65-0.80) in Outcomes Following Vaginal
Prolapse Repair and Midurethral Sling participants and outperformed expert clinicians
(area under the curve 0.72 compared with 0.62, P<.001) and preoperative urinary stress
testing (area under the curve 0.72 compared with 0.54, P<.001). The concordance index
for Colpopexy and Urinary Reduction Efforts trial participants was 0.62 (95% CI 0.56-0.69).This
individualized prediction model for de novo SUI after vaginal POP surgery is valid
and outperforms preoperative stress testing, prediction by experts, and preoperative
reduction cough stress testing. An online calculator is provided for clinical use.III.
Type
Journal articleSubject
Pelvic Floor Disorders NetworkHumans
Urinary Incontinence, Stress
Postoperative Complications
Treatment Outcome
Preoperative Care
Logistic Models
Risk Factors
Aged
Middle Aged
Female
Suburethral Slings
Pelvic Organ Prolapse
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https://hdl.handle.net/10161/19761Published Version (Please cite this version)
10.1097/AOG.0000000000000094Publication Info
Jelovsek, J Eric; Chagin, Kevin; Brubaker, Linda; Rogers, Rebecca G; Richter, Holly
E; Arya, Lily; ... Pelvic Floor Disorders Network (2014). A model for predicting the risk of de novo stress urinary incontinence in women undergoing
pelvic organ prolapse surgery. Obstetrics and gynecology, 123(2 Pt 1). pp. 279-287. 10.1097/AOG.0000000000000094. Retrieved from https://hdl.handle.net/10161/19761.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Matthew Don Barber
Edwin Crowell Hamblen Distinguished Professor of Reproductive Biology and Family Planning
John E Jelovsek
Professor of Obstetrics and Gynecology
Dr. Jelovsek is the F. Bayard Carter Distinguished Professor of OBGYN at Duke University
and serves as Director of Data Science for Women’s Health. He is Board Certified in
OBGYN by the American Board of OBGYN and in Female Pelvic Medicine & Reconstructive
Surgery by the American Board of OBGYN and American Board of Urology. He has an active
surgical practice in urogynecology based out of Duke Raleigh. He has expertise as
a clinician-scientist in developing and evaluating clini
Nazema Yusuf Siddiqui
Associate Professor of Obstetrics and Gynecology
Research on pelvic floor disorders. Specific interests include: 1) studying the urinary
microbiome in aging, recurrent urinary tract infections, and overactive bladder; 2)
pathophysiology of overactive bladder with particular emphasis on translational biology.
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