OBJECTIVE: 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.
MATERIALS AND METHODS: 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.
RESULTS: 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).
CONCLUSION: 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.
Written by:
Jelovsek JE, Chagin K, Brubaker L, Rogers RG, Richter HE, Arya L, Barber MD, Shepherd JP, Nolen TL, Norton P, Sung V, Menefee S, Siddiqui N, Meikle SF, Kattan MW. Are you the author?
Obstetrics, Gynecology & Women's Health Institute and Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio; Department of Obstetrics & Gynecology and Urology, Stritch School of Medicine, Loyola University Chicago, Chicago, Illinois; Departments of Obstetrics and Gynecology and Surgery, University of New Mexico Health Sciences Center, Albuquerque, New Mexico; Departments of Obstetrics and Gynecology, University of Alabama at Birmingham, Birmingham, Alabama; University of Pennsylvania, Philadelphia, Pennsylvania; University of Utah, Salt Lake City, Utah; Women and Infants Hospital, Providence, Rhode Island, Kaiser Permanente, San Diego, California; and Duke University Medical Center, Durham, North Carolina; Department of Obstetrics, Gynecology and Reproductive Sciences, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania; RTI International, Research Triangle Park, North Carolina; Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland.
Reference: Obstet Gynecol. 2014 Feb;123(2 Pt 1):279-87.
doi: 10.1097/AOG.0000000000000094
PubMed Abstract
PMID: 24402598
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