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

Abstract

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 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. 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 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%. Disease 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.