Predicting risk of pelvic floor disorders 12 and 20 years after delivery.

Abstract

Little progress has been made in the prevention of pelvic floor disorders, despite their significant health and economic impact. The identification of women who are at risk remains a key element in targeting prevention and planning health resource allocation strategies. Although events around the time of childbirth are recognized clinically as important predictors, it is difficult to counsel women and to intervene around the time of childbirth because of an inability to convey a patient's risk accurately in the presence of multiple risk factors and the long time lapse, which is often decades, between obstetric events and the onset of pelvic floor disorders later in life. Prediction models and scoring systems have been used in other areas of medicine to identify patients who are at risk for chronic diseases. Models have been developed for use before delivery that predict short-term risk of pelvic floor disorders after childbirth, but no models that predict long-term risk exist.The purpose of this study was to use variables that are known before and during childbirth to develop and validate prognostic models that will estimate the risks of these disorders 12 and 20 years after delivery.Obstetric variables were collected from 2 cohorts: (1) women who gave birth in the United Kingdom and New Zealand (n=3763) and (2) women from the Swedish Medical Birth Register (n=4991). Pelvic floor disorders were self-reported 12 years after childbirth in the United Kingdom/New Zealand cohort and 20 years after childbirth in the Swedish Register. The cohorts were split so that data during the first half of the cohort's time period were used to fit prediction models, and validation was performed from the second half (temporal validation). Because there is currently no consensus on how to best define pelvic floor disorders from a patient's perspective, we chose to fit the data for each model using multiple outcome definitions for prolapse, urinary incontinence, fecal incontinence, ≥1 pelvic floor disorder, and ≥2 pelvic floor disorders. Model accuracy was measured in the following manner: (1) by ranking an individual's risk among all subjects in the cohort (discrimination) with the use of a concordance index and (2) by observing whether the predicted probability was too high or low (calibration) at a range of predicted probabilities with the use of visual plots.Models were able to discriminate between women who experienced bothersome symptoms or received treatment at 12 and 20 years, respectively, for pelvic organ prolapse (concordance indices, 0.570, 0.627), urinary incontinence (concordance indices, 0.653, 0.689), fecal incontinence (concordance indices, 0.618, 0.676), ≥1 pelvic floor disorders (concordance indices, 0.639, 0.675), and ≥2 pelvic floor disorders (concordance indices, 0.635, 0.619). Route of delivery and family history of each pelvic floor disorder were strong predictors in most models. Urinary incontinence before and during the index pregnancy was a strong predictor for the development of all pelvic floor disorders in most models 12 years after delivery. The 12- and 20-year bothersome symptoms or treatment for prolapse models were accurate when predictions were provided for risk from 0% to approximately 15%. The 12- and 20-year primiparous model began to over predict when risk rates reached 20%. When we predicted bothersome symptoms or treatment for urinary incontinence, the 12-year models were accurate when predictions ranged from approximately 5-60%; the 20-year primiparous models were accurate from 5% and 80%. For bothersome symptoms or treatment for fecal incontinence, the 12- and 20-year models were accurate from 1-15% risk and began to over predict at rates at >15% and 20%, respectively.Models may provide an opportunity before birth to identify women who are at low risk of the development of pelvic floor disorders and may provide institute prevention strategies such as pelvic floor muscle training, weight control, or elective cesarean section for women who are at higher risk. Models are provided at http://riskcalc.org/UR_CHOICE/.

Department

Description

Provenance

Citation

Published Version (Please cite this version)

10.1016/j.ajog.2017.10.014

Publication Info

Jelovsek, J Eric, Kevin Chagin, Maria Gyhagen, Suzanne Hagen, Don Wilson, Michael W Kattan, Andrew Elders, Matthew D Barber, et al. (2018). Predicting risk of pelvic floor disorders 12 and 20 years after delivery. American journal of obstetrics and gynecology, 218(2). pp. 222.e1–222.e19. 10.1016/j.ajog.2017.10.014 Retrieved from https://hdl.handle.net/10161/19760.

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.

Scholars@Duke

Jelovsek

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 clinical prediction models using traditional biostatistics and machine learning approaches. These “individualized” patient-centered prediction tools aim to improve decision-making regarding the prevention of lower urinary tract symptoms (LUTS) and other pelvic floor disorders after childbirth (PMID:29056536), de novo stress urinary incontinence and other patient-perceived outcomes after pelvic organ prolapse surgery, risk of transfusion during gynecologic surgery, and urinary outcomes after mid-urethral sling surgery (PMID: 26942362). He also has significant expertise in leading trans-disciplinary teams through NIH-funded multi-center research networks and international settings. As alternate-PI for the Cleveland Clinic site in the NICHD Pelvic Floor Disorders Network, he was principal investigator on the CAPABLe trial (PMID: 31320277), one of the largest multi-center trials for fecal incontinence studying anal exercises with biofeedback and loperamide for the treatment of fecal incontinence. He was the principal investigator of the E-OPTIMAL study (PMID: 29677302), describing the long-term follow up sacrospinous ligament fixation compared to uterosacral ligament suspension for apical vaginal prolapse. He was also primary author on research establishing the minimum important clinical difference for commonly used measures of fecal incontinence. Currently, he serves as co-PI in the NIDDK Symptoms of Lower Urinary Tract Dysfunction Research Network (LURN) (U01DK097780-05) where he has been involved in studies in the development of Symptoms of Lower Urinary Tract Dysfunction Research Network Symptom Index-29 (LURN SI-29) and LURN SI-10 questionnaires for men and women with LUTS. He is also the site-PI for the PREMIER trial (1R01HD105892): Patient-Centered Outcomes of Sacrocolpopexy versus Uterosacral Ligament Suspension for the Treatment of Uterovaginal Prolapse.

Barber

Matthew Don Barber

Professor of Obstetrics and Gynecology

Unless otherwise indicated, scholarly articles published by Duke faculty members are made available here with a CC-BY-NC (Creative Commons Attribution Non-Commercial) license, as enabled by the Duke Open Access Policy. If you wish to use the materials in ways not already permitted under CC-BY-NC, please consult the copyright owner. Other materials are made available here through the author’s grant of a non-exclusive license to make their work openly accessible.