The Astrodem study uses techniques developed for studying galaxies to analyse GP data, looking for the early warning signs of dementia. It is hoped that this innovative approach will lead to better tools to help GPs diagnose dementia earlier and offer better support to their patients.
Why is this work needed?
Diagnosing dementia as soon as possible allows a person to maximise their quality of life, benefit from treatments and plan for the future. However, currently, only 50-60% of people with dementia receive a diagnosis.
What will happen?
Researchers will analyse data from 96,000 GP records to identify common, early indicators of dementia, using the same statistical techniques that have been developed to catalogue galaxies. They will use a broad range of data from GP records, such as the number of appointments a patient has had, or whether they attended with a family member. These details will be combined with other clinical information known to predict dementia, and could provide a wide range of indicators to help GPs identify those at high risk of developing the condition.
What are the benefits likely to be?
This study is currently still in progress. This research is intended to develop a tool that identifies and ranks dementia indicators, which can then be added to the computer software GPs use when diagnosing their patients. It would alert GPs to any potential early signs of dementia when they appear and prompt timely, sensitive conversations about the condition. This could lead to improved rates of early diagnosis, allowing better support and treatment for people with, and at risk of, dementia.
What type of data is involved?
This research uses GP data from the Clinical Practice Research Datalink (CPRD).
What is the legal basis for accessing the data?
The data is de-personalised before being accessed by the researchers.
Who is funding and collaborating on this work?
This research is being carried out at the University of Sussex and funded by the Wellcome Trust. Nine months of preliminary data collection was funded by the University of Sussex’s STFC Impact Acceleration Account. The project has also been supported by the Sussex Research Development Fund.
Where can I go for more information?
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