Principal Investigator: Professor Chris Kipps

Team:

Steven Chance, Oxford Brain Diagnostics Ltd

Robyn Davies, Cardiff and Vale University LHB

Gail Hayward, University of Oxford, Nuffield Department of Primary Care Health Sciences,

Margaret Glogowska, University of Oxford, Nuffield Department of Primary Care Health Sciences

Jane Wolstenholme, University of Oxford, Health Economics Research Centre

Filipa Landeiro, University of Oxford, Health Economics Research Centre

Angus Prosser, University of Southampton

Main Funding: NIHR i4i

Starts: 6 March 2022

Ends: 31 December 2026

Summary

Dementia is an umbrella term for a group of neurodegenerative diseases that cause cognitive and/or behavioural impairment that affect an individual’s function and daily living. Alzheimer’s disease (AD) is the most common dementia subtype, accounting for 60-70% of cases, followed by vascular dementia, mixed dementia, dementia with Lewy bodies, and frontotemporal dementia.

Timely and accurate diagnosis of dementia is essential for appropriate care planning and signposting to sources of support. Early diagnosis and implementation of carer and patient interventions has been shown to improve patient and carer outcomes including health-related quality of life (HR-QoL) and delayed patient institutionalisation.

With the promise of new therapeutic targets that may slow the progression of Alzheimer’s disease with early and targeted intervention, accurate and timely diagnosis is critical.

Diagnosis is however challenging. Difficult cases can leave patients with uncertain diagnoses for long periods of time, where a “wait and see” approach is often employed when diagnostic tests are inconclusive. This can cause significant stress to both individuals living with cognitive impairment or dementia and their families. Definite dementia subtype diagnosis can currently only be obtained through post-mortem histopathological confirmation.

Participants will be recruited through secondary and tertiary centres where diagnostic investigation or patient monitoring is underway. Participants will be followed for two years at 6 monthly intervals to determine change on standard cognitive and functional measures and collection of participant and companion reported health and social care events, with MRI examination at baseline and 24 months.

What do we hope to achieve?

We hope that the CONGA study will show that the CDM technology can be effective in identification of those who are likely to progress to Alzheimer’s disease.

Early detection of dementia and timely diagnosis for patients has been shown to improve patient and carer outcomes, including quality of life, and is critical for appropriate care planning and signposting to sources of support.

Accurate diagnosis and prediction of decline also allows identification for those who might be eligible for treatments.

If the study shows that CDM is effective in identifying dementia earlier and can predict who is most likely to decline, we will look to promote and implement the technology in current clinical pathways.