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.
Current health services do not provide full support and continuity of care for the IMID population with multimorbidity. Typically, services are designed to treat and manage patients with a single medical condition. Accurately identifying new disease clusters in patients with IMIDs presents several challenges. However, recent advances in computer technology have opened opportunities to discover new disease clusters across IMIDs in ways not researched before. We will use advanced analytical techniques to identify new disease clusters that share common characteristics. We will determine this using anonymous Clinical Practice Research Datalink (CPRD), an extensive database of routinely collected UK patient data that provides the opportunity to examine the burden of 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.
The CPRD comprises 50 million patients, including 15 million currently registered patients in GP practices across the UK, with up to 15 years follow-up. We will define "multimorbidity" as the presence of two or more diseases in an individual without reference to any index disease, and the study will focus on patients with complex multiple long-term conditions. We will analyse 40 long-term conditions outlined by Barnett et al. to identify and interpret key and complex multimorbidity clusters. We propose a diverse range of clustering methods as a potential technique to characterise the complex interactions of multimorbidity. We will explore the clusters by age, sex and ethnicity in the IMID population.
Our study will identify disease clusters in the IMID population in the short-term, which will help tailor specific interventions to manage multimorbidity. In the long-term, the findings will inform the design of a longitudinal study using inception cohorts of IMID patients.
This descriptive study will not measure clinical outcomes.
A description of the following outcomes will be reported:
• Prevalence of clusters of long-term conditions (LTCs) in the IMID population.
• Types of LTCs clusters in the IMID population.
• Prevalence of clusters of LTCs in the IMID population by age, sex and ethnicity.
• The life course of the clusters of LTCs in the IMID population.
Prasad Nishtala - Chief Investigator - University of Bath
Prasad Nishtala - Corresponding Applicant - University of Bath
Anita McGrogan - Collaborator - University of Bath
Jenny Humphreys - Collaborator - University of Manchester
John Pauling - Collaborator - University of Bath
Julia Snowball - Collaborator - University of Bath
Mahesan Niranjan - Collaborator - University of Southampton
Neil McHugh - Collaborator - University of Bath
Olga Isupova - Collaborator - University of Bath
Sandipan Roy - Collaborator - University of Bath
Sarah Skeoch - Collaborator - University of Bath
Visakan Kadirkamanathan - Collaborator - University of Sheffield
William Tillett - Collaborator - University of Bath
HES Admitted Patient Care;HES Outpatient;Patient Level Index of Multiple Deprivation