Using astrophysics to close the “diagnosis gap” for dementia in UK general practice

Study Code / Acronym
ASTRODEM
Award Number
202133/Z/16/Z
Award Type
Seed Awards in Science
Status / Stage
Completed
Dates
1 October 2016 -
31 March 2019
Duration (calculated)
02 years 05 months
Funder(s)
Wellcome Trust
Funding Amount
£94,008.00
Funder/Grant study page
Wellcome Trust
Contracted Centre
University of Sussex
Contracted Centre Webpage
Principal Investigator
Dr Elizabeth Ford
PI Contact
E.M.Ford@bsms.ac.uk
PI ORCID
0000-0001-5613-8509
WHO Catergories
Development of clinical assessment of cognition and function
Methodologies and approaches for risk reduction research
Disease Type
Dementia (Unspecified)

CPEC Review Info
Reference ID306
ResearcherReside Team
Published12/06/2023

Data

Study Code / AcronymASTRODEM
Award Number202133/Z/16/Z
Status / StageCompleted
Start Date20161001
End Date20190331
Duration (calculated) 02 years 05 months
Funder/Grant study pageWellcome Trust
Contracted CentreUniversity of Sussex
Contracted Centre Webpage
Funding Amount£94,008.00

Abstract

Dementia is one of the greatest public health challenges of our era. Timely diagnosis allows patients to benefit from current therapies, plan for the future, and maximise their quality of life. However, there is a “diagnosis gap” in UK general practice, with less than two-thirds of expected patients receiving a dementia diagnosis. Increasing diagnosis rates is a strategic aim of the UK government and NHS.

Aims

We aim to close this diagnosis gap in a novel collaboration between primary care epidemiology and astrophysics. We will use a very large set of UK general practice electronic patient records (96,000 patients; 50% with dementia) spanning up to 10 years per patient. We will use a probabilistic programming framework to apply statistical techniques to model dementia onset in this cohort, allowing for the inherent variability and duration of disease development. To achieve a clinically valuable model, these multi-dimensional data will require sophisticated analysis techniques that are not currently available in medical statistics and epidemiology, but which astrophysicists use daily.
Our goal is to develop a statistical model to predict risk of dementia from patients’ general practice records, which will help GPs and other NHS bodies better estimate and identify cases in UK general practice.