Active clinical trials for "Crohn Disease"

Innovative programs exist that suggest that care for people with chronic conditions is optimized when patients and providers have the information they need at the point of care and over time, to engage in shared planning and execution of treatment goals and care plans. This project aims to build an Inflammatory Bowel Disease Learning Health System, a shared information environment, that highlights collaboration among patients, clinicians and care team members, and researchers; for effective use of data for guiding care, value, improvement, and research.

The aim of this study is to develop and assess the feasibility and effect of a web-based, personalized risk-estimation for Crohn's disease (PRE-CD) tool on behaviors and biomarkers associated with risk for Crohn's disease in unaffected first-degree relatives of patients with inflammatory bowel disease. We hypothesize that personalized risk disclosure via the PRE-CD educational tool is both feasible and successful in modifying behaviors associated with Crohn's disease risk and normalizing pre-clinical disease biomarkers when compared to standard Crohn's disease education. Broadly, completion of this project will also help elucidate the role of lifestyle and dietary factors in pre-clinical Crohn's disease development in high-risk individuals, and provide novel insight into potential strategies for disease prevention in this population.

Inflammatory bowel disease(IBD), including ulcerative colitis (UC) and Crohn's disease (CD), is characterized by chronic and recurrent nonspecific intestinal inflammation with high disability rate. During the past few decades, the prevalence of IBD is increasing, especially in developing countries, which brings great burden to patients themselves and medical insurance. Currently, biological medications such as TNFα inhibitors (infliximab, adalimumab, etc.), integrin receptor antagonist (vedolizumab) and interleukin 12/interleukin 23 inhibitor (ustekinumab) are commonly used in IBD treatment as well as traditional drugs such as glucocorticoid, immunosupressive agents and 5-Aminosalicylic Acid, and surgury. However, health-econimic analysis is lacking in Chinese IBD patients and more research is needed for making treatment choice. Meanwhile, the etiology, disease progression and prognosis prediction has not totally been clarified. The efficacy prediction model of vedolizumab and infliximab has been analyzed, whose prediction markers include level of albumin, smoking, surgery history, fistula, etc. However, no model has included predictors concerning disease pathway or pharmacological pathway in patients accepting different therapy. So a model to predict IBD progression and prognosis concerning pharmacological pathway is going to be explored.

This study learn how easily patients can use an educational tool that will be created for patients with melanoma and pre-existing autoimmune diseases who receive or will receive immune checkpoint inhibitor drugs. Patients will be asked their opinions about the design, accessibility, and content of the tool. Researchers will use the information collected to improve the educational materials that will help patients make future decisions about their treatment.

Inflammatory Bowel Disease (IBD) are chronic diseases of the gut comprising Crohn's Disease (CD) and Ulcerative Colitis (UC). The symptoms of IBD consist of diarrhea, abdominal discomfort, weight loss, fatigue and rectal bleeding. However, symptoms and treatment vary between patients. Early management of IBD can lead to better response rates and decrease the risk of irreversible bowel damage and future disease complications such as surgeries. Current clinical tools for diagnosis and or assessing progression of IBD are either invasive (colonoscopy), have low patient acceptance (fecal calprotectin) or low accuracy (C-reactive protein). The purpose of this study is to collect clinical data and samples (including blood, breath and stool) donated by patients with IBD and patients with no IBD (controls) to facilitate research that may result in the development of new non-invasive methods of diagnosing IBD and understand the progression of the disease over time in order to better manage IBD patients.

The purpose of this study is to evaluate the incidence, prevalence, clinical characteristics and prognosis of Crohn's disease and ulcerative colitis over time in a district of Seoul, Korea.

Crohn's disease affects 200,000 people in the UK (~1 in 500), most are young (diagnosed < 35 years) with costs of direct medical care exceeding £500 million. Crohn's disease is caused by an auto-immune response and affects any part of the digestive tract, most commonly the last segment of the small bowel (the terminal ileum). Magnetic resonance imaging (MRI) plays a role in 3 areas: Crohn's disease diagnosis , monitoring treatment response & assessing development of complications. To evaluate the small bowel using MRI, Radiologists visually examine the scan slice-by-slice. The interpretation is time consuming and error-prone because of disease presentation variability and differentiation of diseased segments from collapsed segments. Deep learning for image analysis is based on a computer algorithm "learning" from human (Radiologist) generated training data. This method has been successfully applied to medical imaging, for example computer detection of lung cancer on chest X-rays. This pilot study investigates if a deep learning algorithm can identify and score segments of inflamed terminal ileum affected by Crohn's disease. To our knowledge this is the first project attempting to develop such an algorithm.The study will retrospectively review MR images obtained as part of standard care from patients being investigated for, Crohn's or being followed up with Crohn's disease. 226 patients' images will be used for the study. On fully anonymised images two Radiologists working at Northwick Park Hospital will score and outline normal and abnormal loops of terminal ileum. Imperial College computer science department will then develop a deep learning algorithm from imaging features of normal and abnormal loops. The study end-point is algorithm performance vs. images labelled by Radiologists. The eventual aim is to develop an algorithm that assists Radiologists in the accurate diagnosis and follow-up of patients with Crohn's disease.

The IMAGINE Cohort Study will identify and recruit a cohort of 8000 patients with IBS, IBD and healthy controls (2000 of each) who will be assessed in terms of their psychological status, dietary intake, gut microbiome, metabolomic and inflammatory markers and genotype, health-related quality of life, and health care resource use and associated costs. The cohort and healthy controls will be followed prospectively for up to 5 years after the completion of study enrolment.

This study aims to assess the prevalence of exocrine pancreatic insufficiency (EPI) in a population of patients with active Crohn's disease. Studies already describe a prevalence rate around 18-66%. In this multicentric prospective study, we plan to compare EPI's prevalence at week 0 and week 14 of an induction phase of a biological therapy. The biological therapy will be initiate for an active Crohn's disease. Secondary outcomes will be: malnutrition's prevalence, and Crohn's disease activity level.

The investigators plan to compare contrast-enhanced ultrasound to CT and MRI for the detection and quantification of intestinal inflammation in the the setting of pediatric small bowel Crohns disease