A nomogram for predicting the response to exclusive enteral nutrition in adult patients with isolated colonic Crohn's disease

Therap Adv Gastroenterol. 2019 Oct 11:12:1756284819881301. doi: 10.1177/1756284819881301. eCollection 2019.

Abstract

Background: Isolated colonic Crohn's disease (cCD) responds less well to induction therapy with exclusive enteral nutrition (EEN) compared with ileal or ileocolonic disease in adult patients; therefore, we aimed to identify the factors that influence the response to EEN and develop a predictive nomogram model to optimize the use of EEN in cCD patients.

Materials and methods: Eighty-five cCD patients treated with EEN as first-line therapy at our center between 1 June 2012 and 30 June 2018 were retrospectively analyzed as the primary cohort. The primary endpoint was clinical remission after EEN therapy. Potential predictive factors for the efficacy of EEN were assessed by univariate and multivariate analyses, and a nomogram to predict the response to EEN therapy in cCD patients was designed. Another 19 cCD patients were retrospectively included in the validation cohort to verify the accuracy of the nomogram model.

Results: The clinical remission rates for the primary cohort and validation cohort were 52.9% and 47.4%, respectively. Pancolitis was the greatest contributor to the risk of failure to respond to EEN [odds ratio (OR) = 4.896; 95% confidence interval (CI) = 1.223-19.607; p = 0.025], lean body mass index (LBMI), colonic lesion features, simple endoscopic scores for Crohn's disease, C-reactive protein before treatment and ∆prealbumin were also related to the efficacy of EEN in cCD. The nomogram model showed robust discrimination, with an area under the receiving operating characteristic curve of 0.906.

Conclusion: Several predictive factors for response to EEN therapy in cCD adult patients were identified, and a promising nomogram that can predict the effect of EEN in cCD was developed.

Keywords: exclusive enteral nutrition; isolated colonic Crohn’s disease; nomogram model.