A team led by Oxford University’s Nuffield Department of Primary Health Sciences is working on a new risk prediction model to identify patients at risk from the Covid-19 virus.
It has the backing of NHS Digital, which said it can be used to support GPs and specialists in providing more targeted advice.
The model has been developed in a project commissioned by the Office of the Chief Medical Officer for England, with the New and Emerging Respiratory Virus Threats Advisory Group establishing the parameters.
Routinely collected anonymised electronic health records of 8 million adults in the UK, accessed through the University of Oxford’s QResearch database, and linked datasets will be analysed to identify factors that can be used to predict those at highest risk of infection from Covid-19. These include age, sex, ethnicity, deprivation, smoking status, body mass index, pre-existing medical conditions and current medications.
Algorithms from the data analysis will be developed in conjunction with clinical and data experts at NHS Digital and will drive a clinical risk prediction model to be applied across various health and care settings. Individualised risk assessment could be used to improve shared decision making between clinicians and patients, as well as discussions on how to reduce risk.
The research team are planning to utilise datasets from across all four nations of the UK to validate their model and offer a unified approach to evidence based risk stratification policy.
Sophisticated approach
Professor Julia Hippisley-Cox, the principal investigator of the project, said: "Driven by real patient data, this risk assessment tool could enable a more sophisticated approach to identifying and managing those most at risk of infection and more serious Covid-19 disease.
"Importantly, it will provide better information for GPs to identify and verify individuals in the community who, in consultation with their doctor, may take steps to reduce their risk, or may be advised to shield."
Professor Chris Whitty, the chief medical officer for England, said: "The level of threat posed by Covid-19 varies across the population, and as more is learned about the disease and the risk factors involved, we can start to make risk assessment more nuanced. When developed, this risk prediction tool will improve our ability to target shielding, if it is needed, to those most at risk."
Other organisations contributing to the project include the Universities of Cambridge, Edinburgh, Swansea, Leicester, Nottingham and Liverpool with the London School of Hygiene and Tropical Medicine, Queen’s University Belfast, Queen Mary University of London, University College London, the Department of Health and Social Care and NHS England.
Image by Felipe Esquival Reed, CC BY SA 4.0
