Histopathological features in colonic biopsies at diagnosis predict long-term disease course in patients with Crohn's disease

Background and aims Crohn's disease (CD) is characterized by a heterogeneous disease course. Patient stratification at diagnosis using clinical, serological or genetic markers does not predict disease course sufficiently to facilitate clinical decision making. The current study aimed to investigate the additive predictive value of histopathological features to discriminate between a long-term mild and severe disease course. Methods Diagnostic biopsies from treatment-naive CD patients with mild or severe disease courses in the first 10 years after diagnosis were reviewed by two gastrointestinal pathologists after developing a standardized form comprising 15 histopathological features. Multivariable logistic regression models were built to identify predictive features and compute receiver operating characteristics (ROC) curves. Models were internally validated using bootstrapping to obtain optimism-corrected performance estimates. Results In total, 817 biopsies from 137 patients (64 mild, 73 severe) were included. Using clinical baseline characteristics, disease course could only be moderately predicted (AUROC: 0.738 (optimism 0.018), 95%CI 0.65-0.83, sensitivity 83.6%, specificity 53.1%). When adding histopathological features, in colonic biopsies a combination of (1) basal plasmacytosis, (2) severe lymphocyte infiltration in lamina propria, (3) Paneth cell metaplasia and (4) absence of ulcers were identified and resulted in significantly better prediction of a severe course (AUROC: 0.883 (optimism 0.033), 95%CI 0.82-0.94, sensitivity 80.4%, specificity 84.2%). Conclusions In this first study investigating the additive predictive value of histopathological features in biopsies at CD diagnosis, we found that certain features of chronic inflammation in colonic biopsies contributed to prediction of a severe disease course, thereby presenting a novel approach to improve stratification and facilitate clinical decision making.