The population in the UK is ageing and we are seeing more patients with more than two long-term health conditions, termed “multimorbidity”. Multimorbidity is associated with taking lots of medicines (“polypharmacy”). Some medicines are used to treat symptoms (such as pain or depression) whilst others try to prevent diseases happening further down the line, called “preventative” medicines. Most studies for new medicines don’t include women, very old people or those that have many different conditions. Therefore it is hard to know whether these medicines are working as well in these groups meaning some patients are at risk of experiencing medication-harm (side effects) and may not be getting the benefit that we believe them to be.
Chronic pain is defined as requiring the use of pain-relieving drugs more than three times per year. Many people suffer from multiple medical conditions alongside chronic pain and evidence shows this makes the management of their care more challenging. We therefore need to develop better ways to manage patients with multimorbidity, pain and polypharmacy. We want to develop a computer approach (“machine learning”) that can look at patient records and cluster the commonly occurring diseases together to tell us which clusters of diseases and pain are most common, whether this differs for different age groups, and what factors lead to the development of a disease cluster. Understanding how pain influences multimorbidity will allow us to re-design clinic services to better meet the needs of the population and to try to reduce medication-related harm.
Background: The management of patients with multimorbidity and chronic pain is increasingly complex. Reliance on guidelines for the management of single diseases is a major contributor to increasing healthcare burden and polypharmacy. The use of multiple specialists to manage individual disease is costly and burdensome for both the health care service and patients alike, with oversight and support falling to the over-stretched GP. Understanding the scale of multimorbidity in people with chronic pain, and in particular the key determinants of different disease trajectories along with the factors associated with worse outcomes, is critical to the design of sustainable future services for those with complex care needs that will reduce pill burden, inform future drug development, and provide better models of care.
Aim: To better understand the prevalence and distribution of different patterns of diseases in people with chronic pain and multimorbidity in the UK and the effect on healthcare utilisation and prognosis. Understand the patterns of medicines prescription in relation to pain and multimorbidity, and the key determinants and age distribution of different disease clusters.
Design: Observational descriptive cohort study involving electronic health care records. A novel machine learning cluster algorithm, based on Frequent Pattern Mining, similar to “supermarket basket analysis”, will be used to cluster commonly co-occurring conditions. Comparison to exploratory hierarchical cluster analysis and Cox regression will be undertaken validate the accuracy of the machine approach.
Outcomes: Primary outcome will be time to accumulation of subsequent disease state(s) following acquiring an initial diagnosis of multimorbidity. Secondary outcomes include a) understanding the patterns of prescribing associated with different disease acquisition b) identify the determinants of multimorbidity (gender, socioeconomic status, smoking, alcohol, etc) in order to better understand the biological basis for disease clustering.
Primary outcome will be time to accumulation of subsequent disease state(s) following acquiring an initial diagnosis of multimorbidity (second long-term condition) in people who acquire chronic pain at some point in their lifetime.
Secondary outcomes include a) understanding the patterns of analgesic prescribing associated with different disease acquisition and how this influences the multimorbid clusters; identify the determinants of multimorbidity (gender, socioeconomic status, smoking, alcohol, etc) in order to better understand the biological basis for disease clustering.
For analyses relating to multimorbidity incidence, the case cohort will be dynamic and cases will enter at the point at which they acquire a second long-term disease.
Lauren Walker - Chief Investigator - University of Liverpool
Lauren Walker - Corresponding Applicant - University of Liverpool
Alexandar Vincent Paulraj - Collaborator - University of Liverpool
Christian Mallen - Collaborator - Keele University
Frans Coenen - Collaborator - University of Liverpool
Girvan Burnside - Collaborator - University of Liverpool
Kate Fleming - Collaborator - University of Liverpool
Munir Pirmohamed - Collaborator - University of Liverpool
Pieta Schofield - Collaborator - University of Liverpool
HES Accident and Emergency;HES Admitted Patient Care;Patient Level Index of Multiple Deprivation