Literature DB >> 32467289

Pelvic floor disorders in women who consult primary care clinics: development and validation of case definitions using primary care electronic medical records.

Sue Ross1, Hilary Fast2, Stephanie Garies2, Deb Slade2, Dave Jackson2, Meghan Doraty2, Rebecca Miyagishima2, Boglarka Soos2, Matt Taylor2, Tyler Williamson2, Neil Drummond2.   

Abstract

BACKGROUND: To date, there has been no validated method to identify cases of pelvic floor disorders in primary care electronic medical record (EMR) data. We aimed to develop and validate symptom-based case definitions for urinary incontinence, fecal incontinence and pelvic organ prolapse in women, for use in primary care epidemiologic or clinical research.
METHODS: Our retrospective study used EMR data from the Southern Alberta Primary Care Research Network (SAPCReN) and the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) in southern Alberta. Trained researchers remotely reviewed a random sample of EMR charts of women aged 18 years or older from 6 rural and urban clinics to validate case definitions for urinary incontinence, fecal incontinence and pelvic organ prolapse. We calculated sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV), and estimated SAPCReN prevalence as appropriate.
RESULTS: Charts of 900 women were included. Sensitivity was 81.9% (95% confidence interval [CI] 75.1-87.2) for urinary incontinence, 61.2% (95% CI 46.2-74.5) for fecal incontinence, and 51.8% (95% CI 40.6-62.8) for pelvic organ prolapse. Corresponding specificity values were 71.9% (95% CI 68.4-75.1), 99.2% (95% CI 98.2-99.6) and 98.8% (95% CI 97.7-99.4), PPVs 40.6% (95% CI 35.4-46.0), 81.1% (95% CI 64.3-91.4) and 81.1% (95% CI 67.6-90.1), and NPVs 94.4% (95% CI 92.1-96.1), 97.8% (95% CI 96.5-98.6) and 95.3% (95% CI 93.6-96.6). The SAPCReN-observed prevalence for urinary incontinence was 29.7% (95% CI 29.3-30.0), but the adjusted prevalence was 2.97%.
INTERPRETATION: The case definition for urinary incontinence met our standard for validity (sensitivity and specificity > 70%), and the case definitions for fecal incontinence and pelvic organ prolapse had PPVs greater than 80%. The urinary incontinence definition may be used in epidemiologic research, and those for fecal incontinence and pelvic organ prolapse may be used in quality-improvement studies or creation of disease registries. Our symptom-based case definitions could also be adapted for research in other EMR settings. Copyright 2020, Joule Inc. or its licensors.

Entities:  

Year:  2020        PMID: 32467289      PMCID: PMC7269601          DOI: 10.9778/cmajo.20190145

Source DB:  PubMed          Journal:  CMAJ Open        ISSN: 2291-0026


  25 in total

1.  Data Resource Profile: National electronic medical record data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN).

Authors:  Stephanie Garies; Richard Birtwhistle; Neil Drummond; John Queenan; Tyler Williamson
Journal:  Int J Epidemiol       Date:  2017-08-01       Impact factor: 7.196

2.  Misclassification errors in prevalence estimation: Bayesian handling with care.

Authors:  Niko Speybroeck; Brecht Devleesschauwer; Lawrence Joseph; Dirk Berkvens
Journal:  Int J Public Health       Date:  2012-12-20       Impact factor: 3.380

3.  Age effects on pelvic floor symptoms in a cohort of nulliparous patients.

Authors:  Lieschen H Quiroz; Dena E White; Dianna Juarez; Seyed Abbas Shobeiri
Journal:  Female Pelvic Med Reconstr Surg       Date:  2012 Nov-Dec       Impact factor: 2.091

4.  Representativeness of patients and providers in the Canadian Primary Care Sentinel Surveillance Network: a cross-sectional study.

Authors:  John A Queenan; Tyler Williamson; Shahriar Khan; Neil Drummond; Stephanie Garies; Rachael Morkem; Richard Birtwhistle
Journal:  CMAJ Open       Date:  2016-01-25

5.  Ambulatory care related to female pelvic floor disorders in the United States, 1995-2006.

Authors:  Vivian W Sung; Christina A Raker; Deborah L Myers; Melissa A Clark
Journal:  Am J Obstet Gynecol       Date:  2009-08-15       Impact factor: 8.661

6.  Methods to Describe Referral Patterns in a Canadian Primary Care Electronic Medical Record Database: Modelling Multilevel Count Data.

Authors:  Bridget L Ryan; Joshua Shadd; Heather Maddocks; Moira Stewart; Amardeep Thind; Amanda L Terry
Journal:  J Innov Health Inform       Date:  2017-11-17

7.  Team-based comanagement of diabetes in rural primary care.

Authors:  R Ryan Reyes; Gavin Parker; Stephanie Garies; Cheryl Dolan; Susan Gerber; Beverly Burton; Tracy Burton; Jeff Brockmann; Rebecca Miyagishima; Neil Drummond
Journal:  Can Fam Physician       Date:  2018-08       Impact factor: 3.275

8.  Diabetes and the occurrence of infection in primary care: a matched cohort study.

Authors:  Waseem Abu-Ashour; Laurie K Twells; James E Valcour; John-Michael Gamble
Journal:  BMC Infect Dis       Date:  2018-02-05       Impact factor: 3.090

9.  The Impact of Antidepressant Therapy on Glycemic Control in Canadian Primary Care Patients With Diabetes Mellitus.

Authors:  Justin Gagnon; Marie-Thérèse Lussier; Brenda MacGibbon; Stella S Daskalopoulou; Gillian Bartlett
Journal:  Front Nutr       Date:  2018-06-12

Review 10.  Factors influencing the development of primary care data collection projects from electronic health records: a systematic review of the literature.

Authors:  Marie-Line Gentil; Marc Cuggia; Laure Fiquet; Camille Hagenbourger; Thomas Le Berre; Agnès Banâtre; Eric Renault; Guillaume Bouzille; Anthony Chapron
Journal:  BMC Med Inform Decis Mak       Date:  2017-09-25       Impact factor: 2.796

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