As people are living longer, dementia is more common. Previous research has looked at what factors might cause or increase the risk of dementia, but less is known about how dementia progresses (affects people over time) once they have developed it. One of the quickest ways to identify the progression (course) of dementia may be to use medical records held by the GP. Whilst some evidence exists on what might suggest disease progression in patients with dementia, it is not known whether these can be identified in patients medical records. This study will test whether we can identify markers of dementia progression (including events such as falls, symptoms such as anxiety, and development of other diseases) using information stored in GP medical records. We will study the records of a large number of patients with dementia and determine how common these potential markers of progression are after diagnosis. We will find out if we can group patients by their course over time (for example, good/poor course) based on these markers of progression, and find out what factors (for example, age and sex) predict a particular course. We will also examine how these markers relate to outcomes such as early death and hospital admission. This should allow dementia patients at risk of poor outcome to be spotted early. Future research can then target interventions for improving outcomes in patients at highest risk of faster progression of their disease.
The UK government has prioritised early recognition and treatment of dementia, with goals to prolong independence, delay nursing home and hospital admissions, and reduce mortality. It is recognised that this strategy necessitates primary care involvement in diagnosis and management of dementia. Whilst there is established research on case finding and diagnostics in dementia within primary care, there is a lack of research on the course and prognosis of dementia post-diagnosis. Such information, and what markers alter the course of the disease, is essential for patient management.
Potential resources for studying these issues in primary care are Electronic Health Record (EHR) databases. There are already ways to capture hard endpoints for this population including all-cause mortality, all-cause hospital admissions, and nursing home admissions. The challenge in using EHR for research on dementia is the need to identify, within records, discrete markers of disease progression toward these endpoints for patients with dementia. Evidence exists on what some of these markers may be (e.g. cognitive status, neuropsychiatric symptoms), but what is not known is whether these markers can be reliably detected using EHR.
This study will test the feasibility of establishing markers of disease progression using EHR databases among patients with dementia. We will establish an incident cohort of dementia patients and determine the frequency of recording of markers after diagnosis. We will use latent transition analysis to group patients based on their trajectories of progression over time from diagnosis using these markers. We will investigate prognostic factors for poor trajectories and the relationship of poor trajectories to hard endpoints such as mortality and hospital admission. The novel outputs (validated disease progression markers, mapping of dementia course, defined prognostic factors) will have direct benefits for people with dementia (e.g. identification of patients at risk of progression) and their primary care management.
All-cause mortality
All-cause hospital admission
Nursing home admission
Palliative care
Kelvin Jordan - Chief Investigator - Keele University
Kelvin Jordan - Corresponding Applicant - Keele University
Athula Sumathipala - Collaborator - Keele University
Carolyn Chew-Graham - Collaborator - Keele University
James Bailey - Collaborator - Keele University
Kate Walters - Collaborator - University College London ( UCL )
Martin Frisher - Collaborator - Keele University
Michelle Marshall - Collaborator - Keele University
Nwe Thein - Collaborator - Midlands Partnership NHS Foundation Trust
Paul Campbell - Collaborator - Keele University
Rashi Negi - Collaborator - Midlands Partnership NHS Foundation Trust
Richard Hayward - Collaborator - Keele University
Scott Weich - Collaborator - University of Sheffield
Swaran Singh - Collaborator - University of Warwick
Trishna Rathod-Mistry - Collaborator - Keele University
HES Admitted Patient Care;ONS Death Registration Data;Patient Level Index of Multiple Deprivation