Role of endoscopy, cross-sectional imaging and biomarkers in Crohn's disease monitoring.

Crohn's disease is characterised by recurrent and/or chronic inflammation of the gastrointestinal tract leading to cumulative intestinal tissue damage. Treatment tailoring to try to prevent this tissue damage as well as achieve optimal benefit/risk ratio over the whole disease course is becoming an important aspect of Crohn's disease management. For decades, clinical symptoms have been the main trigger for diagnostic procedures and treatment strategy adaptations. However, the correlation between symptoms and intestinal lesions is only weak. Furthermore, preliminary evidence suggests that a state of remission beyond the simple control of clinical symptoms, and including mucosal healing, may be associated with better disease outcome. Therefore monitoring the disease through the use of endoscopy and cross-sectional imaging is proposed. However, the degree of mucosal or bowel wall healing that needs to be reached to improve disease outcome has not been appropriately studied. Furthermore, owing to their invasive nature and cost, endoscopy and cross-sectional imaging are not optimal tools for the patients or the payers. The use of biomarkers as surrogate markers of intestinal and systemic inflammation might help. Two biomarkers have been most broadly assessed in Crohn's disease: C-reactive protein and faecal calprotectin. These markers correlate significantly with endoscopic lesions, with the risk of relapse and with response to therapy. They could be used to help make decisions about diagnostic procedures and treatment. In particular, with the use of appropriate threshold values, they could determine the need for endoscopic or medical imaging procedures to confirm the disease activity state

What changes in inflammatory bowel disease management can be implemented today?

Innovative ideas are required to improve the management of inflammatory bowel disease and to share best practice that can be implemented into clinical practice today. The use of biomarkers such as calprotectin to monitor disease progression and treatment response could help to improve management of inflammatory bowel disease, but several strategies need to be implemented to make this a reality in clinical practice. The use of calprotectin as a biomarker and the manipulation of the thiopurine pathway to extend the use of current therapies are examples of how basic research can translate into patient benefit. Translational research into the use of microbiota and predictive factors for response and toxicity to drugs, may provide future clinical applications. Global improvement in care in inflammatory bowel disease could also be advanced by improving service provision. For example, the establishment of 'Centres of Excellence', a global interactive inflammatory disease map, and the alignment of processes and standards of care within treatment centres may help to achieve better outcomes for patients with inflammatory bowel disease. Realization of this goal, as well as a better understanding of the aetiology of the disease, may be furthered by collaborative efforts between organizations involved in inflammatory bowel disease as well as wider collaboration across countries and globally

A Novel 8-Predictors Signature to Predict Complicated Disease Course in Pediatric-onset Crohn’s Disease: A Population-based Study

International audienceBackground The identification of patients at high risk of a disabling disease course would be invaluable in guiding initial therapy in Crohn’s disease (CD). Our objective was to evaluate a combination of clinical, serological, and genetic factors to predict complicated disease course in pediatric-onset CD. Methods Data for pediatric-onset CD patients, diagnosed before 17 years of age between 1988 and 2004 and followed more than 5 years, were extracted from the population-based EPIMAD registry. The main outcome was defined by the occurrence of complicated behavior (stricturing or penetrating) and/or intestinal resection within the 5 years following diagnosis. Lasso logistic regression models were used to build a predictive model based on clinical data at diagnosis, serological data (ASCA, pANCA, anti-OmpC, anti-Cbir1, anti-Fla2, anti-Flax), and 369 candidate single nucleotide polymorphisms. Results In total, 156 children with an inflammatory (B1) disease at diagnosis were included. Among them, 35% (n = 54) progressed to a complicated behavior or an intestinal resection within the 5 years following diagnosis. The best predictive model (PREDICT-EPIMAD) included the location at diagnosis, pANCA, and 6 single nucleotide polymorphisms. This model showed good discrimination and good calibration, with an area under the curve of 0.80 after correction for optimism bias (sensitivity, 79%, specificity, 74%, positive predictive value, 61%, negative predictive value, 87%). Decision curve analysis confirmed the clinical utility of the model. Conclusions A combination of clinical, serotypic, and genotypic variables can predict disease progression in this population-based pediatric-onset CD cohort. Independent validation is needed before it can be used in clinical practice