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Knowledge of long-term outcomes following an index episode of acute severe colitis (ASC) can help informed decision-making at a time of acute exacerbation especially when colectomy is an option. We aimed to identify long-term outcomes and their predictors after a first episode of ASC in a large North Indian cohort.Hospitalized patients satisfying Truelove and Witts' criteria under follow-up at a single centre from January 2003-December 2013 were included. Patients avoiding colectomy at index admission were categorized as complete (≤3 non bloody stool per day) or incomplete responders (CR, IR), based upon response to corticosteroids at day 7. Random Forest based machine learning models were constructed to predict the long term risk of colectomy or steroid dependence following an index episode of ASC.Of 1731 patients with ulcerative colitis, 179(10%) had an index episode of ASC. Nineteen(11%) patients underwent colectomy at index admission and 42(26%) over a median follow-up of 56(1-159) months. Hazard ratio for colectomy for IR was 3.6(1.7-7.5, p=0.001) compared to CR. Modeling based on four variables: response at day 7 of hospitalization, steroid use during first year of diagnosis, longer disease duration prior to ASC and number of extra-intestinal manifestations, was able to predict colectomy with an accuracy of 77%.Disease behavior of ASC in India is similar to the West, with a third undergoing colectomy at 10 years. Clinical features, especially response at day 7 hospitalization for index ASC, can predict both colectomy and steroid dependence with reasonable accuracy.

Original publication

DOI

10.1111/jgh.13921

Type

Journal article

Journal

Journal of gastroenterology and hepatology

Publication Date

12/08/2017

Addresses

Department of Gastroenterology, All India Institute of Medical Sciences, New Delhi, India.