B3685 - Diagnostic prediction model for coeliac disease - 06/01/2021

B number: 
Principal applicant name: 
Martha Elwenspoek | University of Bristol (United Kingdom)
Penny Whiting
Title of project: 
Diagnostic prediction model for coeliac disease
Proposal summary: 

Coeliac disease (CD) is an autoimmune disorder, triggered by the protein gluten, and affects 1% of the UK population. Some patients with CD may be asymptomatic, others present with non-specific symptoms, making the diagnosis difficult. Only 30% are thought to be diagnosed. Treatment for CD is lifetime adherence to a gluten free diet. Untreated CD may lead to malnutrition, anaemia, osteoporosis, infertility in women, lymphoma and small bowel cancer. Guidelines recommend that adults and children “at high risk” of CD should be offered testing. However, it is not clear which groups are at sufficiently high risk to justify routine testing.
We have performed a systematic review to identify symptoms and risk factors related to CD. We will use ALSPAC data from children who were tested for CD to determine which combination of symptoms and risk factors best predict CD diagnosis. The results from this study may help GPs decide who should be offered testing for CD.

Impact of research: 
Appropriate identification and treatment of CD can have significant benefits for patients in terms of symptoms, quality of life, and long-term health outcomes, as well as reducing healthcare and societal economic costs.
Date proposal received: 
Thursday, 17 December, 2020
Date proposal approved: 
Thursday, 17 December, 2020
Epidemiology, Coeliac disease, Computer simulations/modelling/algorithms, Statistical methods, Coeliac disease