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

backgroundIrritable 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. PATIENTS AND

methodsThis 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.

resultsCompared 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

conclusionsPatients 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.