DOP55 Histopathological features at diagnosis to predict long-term disease course of Crohn’s Disease
Rezazadeh Ardabili, A.(1,2);Goudkade, D.(3);Wintjens, D.S.J.(1,2);Romberg-Camps, M.J.L.(4);Winkens, B.(5);Pierik, M.J.(1,2);Grabsch, H.I.(6,7);Jonkers, D.M.A.E.(1,2)
(1)Department of Internal Medicine- Division of Gastroenterology and Hepatology, Maastricht University Medical Centre+, Maastricht, The Netherlands;(2)School for Nutrition and Translational Research in Metabolism NUTRIM, Maastricht University Medical Centre+, Maastricht, The Netherlands;(3)Department of Pathology, Zuyderland Medical Centre, Geleen, The Netherlands;(4)Department of Gastroenterology- Geriatrics- Internal and Intensive Care Medicine, Zuyderland Medical Centre, Sittard-Geleen, The Netherlands;(5)Department of Methodology and Statistics- Care and Public Health Research Institute CAPHRI, Maastricht University, Maastricht, The Netherlands;(6)Department of Pathology- GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands;(7)Division of Pathology & Data Analytics- Lees Institute of Medical Research at St. James's, Univeristy of Leeds, Leeds, United Kingdom
Background
Crohn’s disease (CD) is characterized by a heterogeneous disease course and treatment response. There is a clinical need to identify CD patients at diagnosis who are at risk for developing a severe disease course. Patient stratification using state-of-the-art 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 at diagnosis to discriminate between patients with a long-term mild and severe disease course.
Methods
Diagnostic biopsies from treatment-naïve CD patients with mild or severe disease courses in the first 10 years after diagnosis (i.e. based on the number of quarterly flares) were reviewed by two senior gastrointestinal pathologists after developing a standardized form comprising 15 histopathological features related to acute and chronic inflammation. Multivariable logistic regression models were built to identify predictive features and compute receiver operating characteristics (ROC) curves. Model 1 included clinically relevant baseline characteristics (Montreal classification, smoking status and gender). Next, histopathological were added by applying two different model-building strategies (forward selection and purposeful selection algorithm)(Model 2). Prediction models were internally validated using bootstrapping to obtain optimism-corrected performance estimates.
Results
In total, 817 biopsies from 137 CD patients (64 mild disease course, 73 severe disease course) were included. Based on clinical baseline characteristics alone, disease course could only be moderately predicted (Model 1 Area under ROC (AUROC): 0.738 (optimism 0.018), 95%CI 0.65-0.83, sensitivity 83.6%, specificity 53.1%). When adding histopathological features, in colonic, but not ileal, biopsies a combination of (1) basal plasmacytosis, (2) severe lymphocyte and plasma cell infiltration in the lamina propria, (3) Paneth cell metaplasia and (4) absence of ulcers were identified and resulted in significantly better prediction of a severe disease course (Model 2 AUROC: 0.883 (optimism 0.033), 95%CI 0.82-0.94, sensitivity 80.4%, specificity 84.2%, model 2 vs. model 1 AUROC p = 0.001)[Figure 1].
Conclusion
In this first study investigating the additive predictive value of multiple 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.