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

*Corresponding author for this work

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Abstract

Background
Little progress has been made in preventing pelvic floor disorders despite their significant health and economic impact. Identifying women at risk remains a key element in targeting prevention and planning health resource allocation strategies. Although events around the time of childbirth are clinically recognized as important predictors, it is difficult to counsel women and intervene around the time of childbirth due to an inability to accurately convey a patient’s risk in the presence of multiple risk factors and the long time lapse, 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 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 predicting long-term risk exist.

Objective
To use variables known before and during childbirth to develop and validate prognostic models estimating risks of these disorders 12 and 20 years after delivery.

Study Design
Obstetric variables were collected from two 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 UK/NZ 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). As 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 or more pelvic floor disorder and 2 or more pelvic floor disorders. Model accuracy was measured: 1) by ranking an individual’s risk among all subjects in the cohort (discrimination) using a concordance index and 2) by observing whether the predicted probability was too high or low (calibration) at a range of predicted probabilities using visual plots.

Results
Models were able to discriminate between women who developed 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). The discriminatory ability of all models is shown in Table 2. 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 developing all pelvic floor disorders in most models 12 years after delivery. The 12 and 20-year bothersome or treatment for prolapse models were accurate when providing predictions for risk from 0% to approximately 15%. The 12-year and 20-year primiparous model began to over-predict when risk rates reached 20%. When predicting bothersome symptoms or treatment for urinary incontinence, the 12-year models were accurate when predictions ranged from approximately 5% to 60% and 20-year primiparous models were accurate between 5% and 80%. For bothersome symptoms or treatment for fecal incontinence, the 12 and 20-year models were accurate between 1% and 15% risk and began to over-predict at rates above 15% and 20%, respectively.

Conclusion
Models may provide an opportunity before birth to identify women at low risk of developing pelvic floor disorders and institute prevention strategies such as pelvic floor muscle training, weight control or elective cesarean section for women at higher risk. Models are provided at: http://riskcalc.org/UR_CHOICE/
Original languageEnglish
Pages (from-to)222.e1- 222.e19
Number of pages19
JournalAmerican Journal of Obstetrics and Gynecology
Volume218
Issue number2
Early online date19 Oct 2017
DOIs
Publication statusPublished - 1 Feb 2018

Keywords

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

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    Jelovsek, J. E., Chagin, K., Gyhagen, M., Hagen, S., Wilson, D., Kattan, M. W., Elders, A., Barber, M. D., Areskoug, B., MacArthur, C., & Milsom, I. (2018). Predicting risk of pelvic floor disorders 12 and 20 years after delivery. American Journal of Obstetrics and Gynecology, 218(2), 222.e1- 222.e19. https://doi.org/10.1016/j.ajog.2017.10.014