OBJECTIVE: To evaluate the histomorphometric features of early colonic biopsies from patients with Crohn's disease (CD) and their relationship to clinical phenotypes. The clinical course of Crohn's disease is variable and relevant for treatment selection. Early aggressive treatment may change the course of disease but should be balanced by safety considerations. Currently, prediction of disease course is suboptimal. STUDY DESIGN: Colonic biopsies from CD colitis patients with different phenotypes were analyzed using histomorphometry. The quantitative results were used to predict postbiopsy clinical phenotypes and outcomes. Data analysis was performed using statistical and Neural Network models. RESULTS: Univariate analysis revealed differences between the phenotypes in the number of inflammatory cells (p = 0.003), lymphocytic aggregates (p = 0.005) and optical density of mature and young collagen (p = 0.008 and p = 0.01, respectively). Multivariate analysis allowed for differentiation between the clinical phenotypes and prediction of surgery, with good sensitivity and specificity. A neural network model predicted clinical phenotypes with an accuracy of 94%. CONCLUSIQN:.To our knowledge this is the first study that applied histomorphometry on early biopsies in order to predict the clinical phenotypes in Crohn's colitis. Measurements allowed differentiation and prediction of clinical phenotypes and outcomes such as surgery. This approach, in combination with other known predictors, may increase the ability to classify and predict the clinical course of CD colitis, thus improving patient management. Prospective validation using larger cohorts is needed.
OBJECTIVE: To evaluate the histomorphometric features of early colonic biopsies from patients with Crohn's disease (CD) and their relationship to clinical phenotypes. The clinical course of Crohn's disease is variable and relevant for treatment selection. Early aggressive treatment may change the course of disease but should be balanced by safety considerations. Currently, prediction of disease course is suboptimal. STUDY DESIGN:Colonic biopsies from CD colitispatients with different phenotypes were analyzed using histomorphometry. The quantitative results were used to predict postbiopsy clinical phenotypes and outcomes. Data analysis was performed using statistical and Neural Network models. RESULTS: Univariate analysis revealed differences between the phenotypes in the number of inflammatory cells (p = 0.003), lymphocytic aggregates (p = 0.005) and optical density of mature and young collagen (p = 0.008 and p = 0.01, respectively). Multivariate analysis allowed for differentiation between the clinical phenotypes and prediction of surgery, with good sensitivity and specificity. A neural network model predicted clinical phenotypes with an accuracy of 94%. CONCLUSIQN:.To our knowledge this is the first study that applied histomorphometry on early biopsies in order to predict the clinical phenotypes in Crohn's colitis. Measurements allowed differentiation and prediction of clinical phenotypes and outcomes such as surgery. This approach, in combination with other known predictors, may increase the ability to classify and predict the clinical course of CD colitis, thus improving patient management. Prospective validation using larger cohorts is needed.