Predictors of long-term outcomes in patients with acute severe colitis: A northern Indian cohort study

Background and AimKnowledge 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. MethodsHospitalized patients satisfying Truelove and Witts' criteria under follow-up at a single center from January 2003 to December 2013 were included. Patients avoiding colectomy at index admission were categorized as complete (3 non bloody stool per day) or incomplete responders, 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. ResultsOf 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 incomplete responder was 3.6 (1.7-7.5, P=0.001) compared with complete responder. Modeling based on four variables, response at day 7 of hospitalization, steroid use during the first year of diagnosis, longer disease duration before ASC, and number of extra-intestinal manifestations, was able to predict colectomy with an accuracy of 77%. ConclusionsDisease behavior of ASC in India is similar to the West, with a third undergoing colectomy at 10years. Clinical features, especially response at day 7 hospitalization for index ASC, can predict both colectomy and steroid dependence with reasonable accuracy.