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

J. Eric Jelovsek, Kevin Chagin, Maria Gyhagen, Suzanne Hagen, Don Wilson, Michael W. Kattan, Andrew Elders, Matthew D. Barber, Björn Areskoug, Christine MacArthur, Ian Milsom

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Background: 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.
Objective: 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.
Study Design: 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.
Results: 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.
Conclusion: 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
Original languageEnglish
JournalAmerican journal of obstetrics and gynecology
Early online date19 Oct 2017
Publication statusE-pub ahead of print - 19 Oct 2017


  • fecal incontinence
  • machine learning
  • pelvic floor disorder
  • pelvic organ prolapse
  • prediction model
  • urinary incontinence


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