Constipation Predominant Irritable Bowel Syndrome and Functional Constipation Are Not Discrete Disorders: A Machine Learning Approach

Am J Gastroenterol. 2020 Aug 31. doi: 10.14309/ajg.0000000000000816. Online ahead of print.

James K Ruffle 1 2 3, Linda Tinkler 4, Christopher Emmett 4, Alexander C Ford 5, Parashkev Nachev 3, Qasim Aziz 1, Adam D Farmer 1 6, Yan Yiannakou 4


Author information

  • 1Centre for Neuroscience, Surgery and Trauma, Blizard Institute, Wingate Institute of Neurogastroenterology, Barts and the London School of Medicine & Dentistry, Queen Mary University of London, London, United Kingdom.
  • 2Department of Radiology, University College London Hospital NHS Foundation Trust, London, United Kingdom.
  • 3Institute of Neurology, UCL, London, United Kingdom.
  • 4Durham Bowel Dysfunction Service, University Hospital North Durham, County Durham and Darlington NHS Trust, Durham, United Kingdom.
  • 5Leeds Institute of Medical Research at St. James's, University of Leeds, Leeds, United Kingdom.
  • 6Department of Gastroenterology, University Hospitals Midlands NHS Trust, Stoke on Trent, Staffordshire, ST4 6QG, United Kingdom.


Introduction: Chronic constipation is classified into 2 main syndromes, irritable bowel syndrome with constipation (IBS-C) and functional constipation (FC), on the assumption that they differ along multiple clinical characteristics and are plausibly of distinct pathophysiology. Our aim was to test this assumption by applying machine learning to a large prospective cohort of comprehensively phenotyped patients with constipation.

Methods: Demographics, validated symptom and quality of life questionnaires, clinical examination findings, stool transit, and diagnosis were collected in 768 patients with chronic constipation from a tertiary center. We used machine learning to compare the accuracy of diagnostic models for IBS-C and FC based on single differentiating features such as abdominal pain (a "unisymptomatic" model) vs multiple features encompassing a range of symptoms, examination findings and investigations (a "syndromic" model) to assess the grounds for the syndromic segregation of IBS-C and FC in a statistically formalized way.

Results: Unisymptomatic models of abdominal pain distinguished between IBS-C and FC cohorts near perfectly (area under the curve 0.97). Syndromic models did not significantly increase diagnostic accuracy (P > 0.15). Furthermore, syndromic models from which abdominal pain was omitted performed at chance-level (area under the curve 0.56). Statistical clustering of clinical characteristics showed no structure relatable to diagnosis, but a syndromic segregation of 18 features differentiating patients by impact of constipation on daily life.

Discussion: IBS-C and FC differ only about the presence of abdominal pain, arguably a self-fulfilling difference given that abdominal pain inherently distinguishes the 2 in current diagnostic criteria. This suggests that they are not distinct syndromes but a single syndrome varying along one clinical dimension. An alternative syndromic segregation is identified, which needs evaluation in community-based cohorts. These results have implications for patient recruitment into clinical trials, future disease classifications, and management guidelines.

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