Prediction of outcome of treatment of acute severe ulcerative colitis using principal component analysis and artificial intelligence.

BACKGROUND AND AIM: About 15% patients with acute severe ulcerative colitis (UC) fail to respond to medical treatment and may require colectomy. An early prediction of response may help the treating team and the patients and their family to prepare for alternative treatment options.

METHODS: Data of 263 patients (mean age 37.0 ± 14.0-years, 176, 77% male) with acute severe UC admitted during a 12-year period were used to study predictors of response using univariate analysis, multivariate linear principal component analysis (PCA), and nonlinear artificial neural network (ANN).

RESULTS: Of 263 patients, 231 (87.8%) responded to the initial medical treatment that included oral prednisolone ( n = 14, 5.3%), intravenous (IV) hydrocortisone ( n = 238, 90.5%), IV cyclosporine ( n = 9, 3.4%), and inflixmab ( n = 2, 0.7%), and 28 (10.6%) did not respond and the remaining 4 (1.5%) died, all of whom did were also nonresponders. Nonresponding patients had to stay longer in the hospital and died more often. On univariate analysis, the presence of complications, the need for use of cyclosporin, lower Hb, platelets, albumin, serum potassium, and higher C-reactive protein were predictors of nonresponse. Hb and albumin were strong predictive factors on both PCA and ANN. Though the nonlinear modeling using ANN had a good predictive accuracy for the response, its accuracy for predicting nonresponse was lower.

CONCLUSION: It is possible to predict the response to medical treatment in patients with UC using linear and nonlinear modeling technique. Serum albumin and Hb are strong predictive factors.