Development and internal validation of a machine learning-based disease burden index for irritable bowel syndrome: a multicentre cross-sectional study.

Authors

Yanbin Wei, Heyang Zhang, Nan Wang, Haifeng Jin, Zitan Feng, Jiapeng Guo, Jiafei Peng, Jia Feng, Jia Zhi, Shutian Zhang, Shengtao Zhu, Xin Yao

Published in

Annals of medicine. Volume 58. Issue 1. Pages 2721087. Epub Aug 20, 2026.

Abstract

Irritable bowel syndrome (IBS) is one of the most common disorders of gut-brain interaction in gastroenterology clinics worldwide, becoming a significant public health burden. This study aims to provide an exploratory approach for comprehensive assessment of IBS burden and may serve as a basis for future external validation and further evaluation of its potential clinical application.
This multicentre cross-sectional study included IBS patients diagnosed according to the Rome III and/or Rome IV criteria. Rome criteria-based comparisons and latent class analysis (LCA) were performed to characterize clinical heterogeneity, followed by the development of the IBS burden index (IBS-BI) using machine learning.
Compared to the Rome IV-ineligible group, the Rome IV-eligible group exhibited significantly more severe IBS symptoms, somatization symptoms, anxiety symptoms, depression symptoms, and poorer health-related quality of life (all p <0.05). Linear regression analysis showed that the severity of these symptoms was inversely related to health-related quality of life (all p <0.05). LCA identified four heterogeneous IBS burden phenotypes: high-symptom burden, low-symptom burden, somatization-dominant, and psychological comorbid phenotypes. Using machine learning, we developed the IBS-BI and an interactive clinical calculator. The validation was limited to an internal training-validation split, and no external validation was performed.
Patients with IBS who fulfilled the Rome IV criteria showed a higher disease burden, which may be related to the more stringent diagnostic requirements of these criteria. The IBS-BI may have potential value for evaluating disease burden and patient stratification. Its clinical utility and broader applicability require further validation in independent external cohorts and prospective studies.

PMID:
42622375
Bibliographic data and abstract were imported from PubMed on 20 Aug 2026.

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