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** HEALTH RESEARCH SHOWCASE THURSDAY 29 MAY 2025 **

News

You are here: Home > Imaging > Study outlines feasibility of multiple disease risk prediction model for primary care

Study outlines feasibility of multiple disease risk prediction model for primary care

23 May 2024 · Listed under Imaging, Translational Data Science

A team of researchers have found that a single, integrated health check carried out in a primary care setting can accurately predict risks for diseases across multiple organs.

Doctor takes patient's blood pressure
Shutterstock.com

Currently, GPs are often limited to assessing the risk of diseases one at a time, a process that is time-consuming.

The new study, published in the BMJ Evidence-based Medicine, used data from nearly 230,000 participants in the UK Biobank. It was supported by the National Institute for Health and Care Research (NIHR) Oxford Biomedical Research Centre (BRC).

The researchers showed that information that is already being collected as part of the primary care health check could feasibly be combined into a single calculator providing 10-year risk estimates for multiple diseases across related organ systems of heart, brain, liver and kidney.

As well as confirming that risk factors like high blood pressure, diabetes and high cholesterol were linked to the risk of diseases in these organs, the researchers also found that other factors not usually considered as part of standard risk assessments – like mental health, inflammation, sleep quality or medication use – were also significant.

The lead author on the paper, Celeste McCracken of the University of Oxford’s Radcliffe Department of Medicine, said: “Our findings suggest that primary care providers could use a single set of easily collected information to simultaneously generate multiple disease risk scores. This could significantly streamline the process, potentially improving early disease detection and prevention efforts.”

The study also provided evidence around the potential accuracy of remote health risk scoring. “This data shows that it is possible to derive decent multiorgan risk estimates from information that can be collected remotely. We understand the NHS is resource-constrained, and this could have huge implications for people in hard-to-reach places,” McCracken added.

← Importance of early blood glucose control for people with type 2 diabetes highlighted
AI analysis of heart scans predicts risk of developing heart problems ten years in advance →

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