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Artificial intelligence for identifying new disease clusters in patients with immune-mediated inflammatory conditions: A Proof-of-Concept Study
Safe People
University of Bath
Academic Institute
Prasad Nishtala - Chief Investigator - University of BathPrasad Nishtala - Corresponding Applicant - University of BathAnita McGrogan - Collaborator - University of BathJenny Humphreys - Collaborator - University of ManchesterJohn Pauling - Collaborator - University of BathJulia Snowball - Collaborator - University of BathMahesan Niranjan - Collaborator - University of SouthamptonNeil McHugh - Collaborator - University of BathOlga Isupova - Collaborator - University of BathSandipan Roy - Collaborator - University of BathSarah Skeoch - Collaborator - University of BathVisakan Kadirkamanathan - Collaborator - University of SheffieldWilliam Tillett - Collaborator - University of Bath
Safe Projects
CPRD840
Immune-mediated inflammatory disease (IMID) is estimated to affect approximately 1 in 10 people in Europe and the United States. The most common IMIDs include rheumatoid arthritis (RA), inflammatory bowel disease, systemic lupus erythematosus, psoriasis, systemic sclerosis and psoriatic arthritis. Multimorbidity refers to multiple medical conditions in the same individual and is common amongst people with IMIDs. Although multimorbidity in the RA population is well known, in other IMIDs, it is not well understood. It is unclear which of the medical conditions appear after the IMID diagnosis, which is likely to change over the life course, and how a particular medical condition impacts health outcomes. Identifying clusters of medical conditions will help clinicians be more organised in treating and planning services for the IMID population with multimorbidity.
The overall prevalence of immune-mediated inflammatory conditions (IMIDs) is estimated to affect 1 in 10 people in Europe and the United States. The most common IMIDs include rheumatoid arthritis (RA), inflammatory bowel disease, systemic lupus erythematosus, psoriasis, systemic sclerosis, and psoriatic arthritis (PsA). Although the epidemiology of multimorbidity in the RA population is well characterised, it is poorly understood in other IMIDs. Our study's overarching aim is to leverage longitudinal primary care health data from a comprehensive electronic health record database, Clinical Practice Research Datalink (CPRD), to characterise the epidemiology of clusters of medical conditions amongst the IMID population. There are significant gaps in our understanding of which cluster of medical conditions occur commonly together in the IMID population and whether these clusters vary over the life course. There is an urgent, yet unmet need to accurately identify clusters of medical conditions in IMIDs and understand their life course.
25/02/2021
Safe Data
HES Admitted Patient Care
HES Outpatient
Patient Level Index of Multiple Deprivation
Safe Setting
Release