Developing a methodological framework for the clinical prediction of multimorbidity

Date of Approval
Application Number
20_000102
Technical Summary

The overarching aim of this study is to develop statistical methodology to enable the clinical prediction of multimorbidity. This will include evaluating currently available methodology, and developing new methodology where appropriate. This will be undertaken in two work packages (WP). WP1 will provide motivation for and highlight the importance of this project. We will show that the univariate Cox models which are currently used in clinical practice to predict the development of chronic conditions, are unable to predict the risk of more than one disease co-occurring. This motivates the need for multivariate techniques (shared frailty, copula models, marginal approach) and multistate models for the prediction of multimorbidity. WP2 will look at how to best predict multimorbidity using existing multivariate and multistate models, and seek to understand why such methods are not commonly implemented in practice. The work will be largely simulation based, with the CPRD data used in case studies to elicit the differences between the methods of interest. HES and ONS will be used to determine outcome events that do not appear in the primary care records, as would be done in univariate models that are currently used in practice.

Health Outcomes to be Measured

The primary outcome of interest in this study is multimorbidity, with a focus on developing a methodological framework to support the prediction of multimorbidity. In our case studies, we will consider multimorbidity to be the co-occurrence of two or more of:
Coronary Heart Disease (CHD)
Atrial Fibrillation (AF)
Stroke (including transient ischaemic attack)
Chronic Kidney Disease (CKD)
Type 2 Diabetes (T2D)
Cancer (colon, breast and lung)
Dementia

Collaborators

Glen Martin - Chief Investigator - University of Manchester
Alexander Pate - Corresponding Applicant - University of Manchester
Darren Ashcroft - Collaborator - University of Manchester
Gregory Lip - Collaborator - University of Liverpool
Iain Buchan - Collaborator - University of Liverpool
Jamie Sergeant - Collaborator - University of Manchester
Katherine McAllister - Collaborator - National Institute for Health and Clinical Excellence - NICE
Laura Bonnett - Collaborator - University of Liverpool
Mamas Mamas - Collaborator - Keele University
Martin O'Flaherty - Collaborator - University of Liverpool
Matthew Sperrin - Collaborator - University of Manchester
Michelle McDowell - Collaborator - Harding Center for Risk Literacy
Niels Peek - Collaborator - University of Manchester
Richard Riley - Collaborator - Keele University
Thomas Lawrence - Collaborator - National Institute for Health and Clinical Excellence - NICE
Tjeerd van Staa - Collaborator - University of Manchester

Linkages

HES Admitted Patient Care;ONS Death Registration Data;Patient Level Index of Multiple Deprivation