What question did this study set out to answer?

The aim is to use machine learning to identify and characterize sub-phenotypes of acute ulcerative colitis for better risk stratification.

P0339Unmasking the faces of Severe Colitis: A data-driven approach to defining Ulcerative Colitis phenotypes and predicting clinical outcomes

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The aim is to use machine learning to identify and characterize sub-phenotypes of acute ulcerative colitis for better risk stratification.
Conducted a retrospective cohort analysis on 753 acute ulcerative colitis admissions.
Utilized 13 baseline variables for analysis, including demographics and inflammatory markers.
Performed unsupervised hierarchical clustering to identify clinical sub-phenotypes.
Identified six discrete acute ulcerative colitis phenotypes with varying disease severity and prognosis.
One-year colectomy rates ranged from 19% to 58% across different phenotypes.
Revealed a 'CRP paradox' where treatment history is a better predictor of colectomy risk than acute phase reactants.

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Retrospective cohort analysis identifies distinct ulcerative colitis phenotypes, suggesting tailored treatment strategies.

Etchegaray et al. (2026) studied this question.

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