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Updated: Jun 6, 2026

Chinese Herbal Retention Enema for the Treatment of Ulcerative Colitis
Published on: May 16, 2025
Distinct predictive factors for acute severe ulcerative colitis in East Asian and Western populations
Eun Soo Kim1, Dong Hyun Kim2, Seong-Jung Kim3
1Division of Gastroenterology, Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, 41944, Korea.
Background:
This study aimed to develop predictive models for acute severe ulcerative colitis (ASUC) using data from East Asian (EA) patients and to validate their performance in an Australia/New Zealand (ANZ) cohort.
Methods:
A retrospective international study was conducted across 23 referral hospitals in EA and ANZ, enrolling consecutive ASUC patients between January 2015 and December 2022. Logistic regression analyses were used to construct predictive models for 1-year colectomy and non-response to corticosteroid therapy (NRS).
Results:
Overall, 826 patients with ASUC were included (411 EA and 415 ANZ). Among EA patients, independent predictors of 1-year colectomy included female sex, prior exposure to tumor necrosis factor inhibitors, and admission albumin ≤3 g/dL. Independent predictors of NRS in the EA cohort were age at diagnosis ≤37 years, baseline steroid use, admission albumin ≤2.5 g/dL, and the presence of extraintestinal manifestations. The EA-derived scoring systems demonstrated strong predictive performance within the EA cohort (1-year colectomy: P < .0001; NRS: P = .001) but showed limited utility in the ANZ cohort (P = .106 and P = .012). In contrast, European-developed models-including the French 1-year colectomy score and the ADMIT-ASC index for NRS-accurately predicted outcomes in the ANZ cohort (P = .007 and P < .0001) but had reduced predictive capacity in the EA cohort (P = .106 and P = .026). These findings were consistent in both cohorts following propensity score matching.
Conclusions:
Predictive factors for ASUC differ substantially between EA and ANZ patients, highlighting the need for population-specific predictive tools.
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