Constipation Predominant Irritable Bowel Syndrome and Functional Constipation Are Not Discrete Disorders: A Machine Learning Approach. (31st January 2021)
- Record Type:
- Journal Article
- Title:
- Constipation Predominant Irritable Bowel Syndrome and Functional Constipation Are Not Discrete Disorders: A Machine Learning Approach. (31st January 2021)
- Main Title:
- Constipation Predominant Irritable Bowel Syndrome and Functional Constipation Are Not Discrete Disorders: A Machine Learning Approach
- Authors:
- Ruffle, James K.
Tinkler, Linda
Emmett, Christopher
Ford, Alexander C.
Nachev, Parashkev
Aziz, Qasim
Farmer, Adam D.
Yiannakou, Yan - Abstract:
- Abstract : 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 aAbstract : 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. … (more)
- Is Part Of:
- American journal of gastroenterology. Volume 116:Number 1(2021)
- Journal:
- American journal of gastroenterology
- Issue:
- Volume 116:Number 1(2021)
- Issue Display:
- Volume 116, Issue 1 (2021)
- Year:
- 2021
- Volume:
- 116
- Issue:
- 1
- Issue Sort Value:
- 2021-0116-0001-0000
- Page Start:
- 142
- Page End:
- 151
- Publication Date:
- 2021-01-31
- Subjects:
- Stomach -- Diseases -- Periodicals
Intestines -- Diseases -- Periodicals
Gastroenterology -- Periodicals
Gastrointestinal Diseases -- Periodicals
Electronic journals
Periodicals
616.33 - Journal URLs:
- http://www.mdconsult.com/public/search?search_type=journal&j_sort=pub_date&j_date_range=1995-current&j_issn=0002-9270 ↗
http://www.amjgastro.com/ ↗
http://www.nature.com/ajg/archive/index.html ↗
http://www.sciencedirect.com/science/journal/00029270 ↗
http://www.nature.com/ ↗
http://www3.interscience.wiley.com/journal/117955841/home ↗
http://firstsearch.oclc.org ↗
http://firstsearch.oclc.org/journal=0002-9270;screen=info;ECOIP ↗ - DOI:
- 10.14309/ajg.0000000000000816 ↗
- Languages:
- English
- ISSNs:
- 0002-9270
- Deposit Type:
- Legaldeposit
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