Status
Completed
Title
CoMPuTE: Complex Multimorbidity Phenotypes, Trends, and Endpoints
What were the objectives of the study?
Multimorbidity (having multiple diseases at one time) is a growing problem in health and social care. Both research and actual recent events have shown that those suffering from multimorbidity have greater health and care needs and worse health outcomes. This is expensive for the health and social care system and often puts certain groups of people at a specific disadvantage. If we know more about what causes multimorbidity and how it develops, we will be better able to develop practical strategies to deal with it and to allocate funds fairly and usefully.
Often multimorbidity affects certain groups, especially the most vulnerable, but we do not yet have accurate ways of predicting the patterns. So far, most research has focused on how to define the problem. We aim to look at how multimorbidity actually develops and to see whether we can predict its development. Specifically, we do not understand how individual health journeys evolve: how do individual diseases, medicines, health behaviours, mental health, geography, income, etc. contribute to these patterns?
One of the reasons we do not know this is that the amount of data points is vast, and therefore difficult to compute. We now have the ability to program computers to learn as they go along, to more quickly and efficiently analyse large amounts of data.
This project aims to analyse data from a large set of patients using two different but complementary methods of computer learning. This study will look at a large set of general practice health care records (almost 40% of individuals with primary care records in the UK) using two different approaches to see which approach, or combination of approaches, most accurately predicts patterns of disease.
How was the research done?
Approach 1: ‘biological’ age
The first approach is inspired by using an estimate of ‘biological’ age. Unlike a person’s actual age (that is to say: the actual number of years one has lived), ‘biological’ age is a combination of actual age and other information, including current and past diseases, other measures of health such as blood pressure, and life changes such as menopause (which happen at different ages for different people).
Approach 2: ‘longitudinal’ age
The second approach uses ‘longitudinal age, that is: data collected over several years from each individual to identify patterns in the groupings of these diseases. This will help identify those groupings that are most common and help improve the delivery of health care and ensure equitable allocation of resources.
By using these two methods together we plan to determine if they accurately pinpoint and predict which patients will develop certain groups of diseases, what diseases those might be, and in what patterns they might appear.
Chief Investigator
Prof Rafael Perera
Lead Applicant Organisation Name
Sponsor
University of Oxford
Location of research
University of Oxford
Date on which research approved
08-Feb-2021
Project reference ID
OX125
Generic ethics approval reference
18/EM/0400
Are all data accessed are in anonymised form?
Yes
Brief summary of the dataset to be released (including any sensitive data)
GP data for individuals with multiple conditions (cardiac, respiratory, mental heal, and/or kidney conditions) and their measures, for example blood pressure, cholesterol, etc, each time they attend their GP; as well as linked hospital and mortality data for these conditions.
Implications and Impact
This initial programme of work is what is called ‘Proof of Concept’, that is: it is a small project to see if the data that we have can be analysed in the way we hope and what that might tell us about the patterns of disease.
We need to confirm that these methods work with the sort of data currently available. We also need to see if their results are useful for clinicians and decision makers and what the advantages and disadvantages of each method might be. Finally we need to see how they work together with each other, or with other additional types of analysis or data.
If the concept works, we plan to apply for funding for a follow-on larger project to develop tools to help policymakers decide what resources are needed for health and social care, and to help services provide better and more complete care for patients.
Funding Source
UK NIHR – AI and Multimorbidity Funding stream
Public Benefit Statement
Research Team
Anica Alvarez Nishio, PPI/E Lead
Prof Derrick Bennett, University of Oxford
Dr Ben Cairns, University of Oxford
Dr Tingting Zhu, University of Oxford
Prof Carl Heneghan, University of Oxford
Prof Kam Bhui, University of Oxford
Prof David Stensaltz, University of Oxford
Dr Hamish Patten, University of Oxford
Dr Maria Chistodoulou, University of Oxford
Prof Rafael Perera, University of Oxford
Glossary
Artificial Intelligence: Machine learning; programming computers to learn as they go along
Biological Age: The medical or effective age of one’s body based on chronological age, plus physical health, mental health, social group, addiction issues, medications, etc.
Biomarker: An indicator which can be measured to signal the presence or severity of a disease
Chronological Age: The number of years one has lived
Multimorbidity: Having more than one disease at a time
Approval Letter
Access Type
Trusted Research Environment (TRE)