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Decision tree for early introduction of rescue therapy in active ulcerative colitis treated with steroids
Míriam Mañosa1, Eduard Cabré, Esther Garcia-Planella
1Hospital Universitari Germans Trias i Pujol, Badalona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Spain. mmanosa.germanstrias@gencat.cat
Background:
Corticosteroids are the treatment of choice for moderate-to-severe active ulcerative colitis (UC) but up to 30%-40% of patients fail to respond. It has been reported that early clinical-biological parameters may identify those patients at high risk of colectomy. The aim was to identify predictors of rapid response to systemic steroids in moderate-to-severe attacks of UC.
Methods:
Consecutive patients treated with prednisone 1 mg/kg/day for moderate-to-severe attacks of UC were prospectively included. Clinical and biological parameters at 3 and 7 days after starting steroids were recorded. Response was defined as mild or inactive UC activity at day 7 (as assessed by the Montreal Classification of severity) together with no need for rescue therapies (cyclosporin, infliximab, or colectomy). A logistic regression analysis was performed to identify those independent predictors of response. In addition, a decision-tree analysis was also performed.
Results:
Sixty-eight percent of patients (64 out of 94) responded to steroids. In the univariate analysis the number of bowel movements, rectal bleeding, platelet count, and C-reactive protein (CRP) levels at day 3 were associated with response at day 7, but only rectal bleeding was found to be an independent predictor in the logistic regression analysis. Conversely, the classification and regression tree (CART) model included these four variables. The decision-tree model showed a higher sensitivity in predicting a rapid response to steroids than the logistic regression one.
Conclusions:
Rapid response to steroids in active UC attacks can be predicted after 3 days of treatment by simple clinical and biological parameters. A decision-tree model for early introduction of rescue therapies is provided.
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