NHS Digital supports development of risk prediction model for more tailored COVID-19 shielding advice
Clinicians and GPs will soon be able to better identify patients who are at a higher risk of serious illness from SARS-CoV-2 infection based on a new data-driven risk prediction model.
Led by Oxford University, in collaboration with NHS Digital and a number of partner universities, this new model could be applied in a variety of health and care settings, including supporting GPs and specialists in consultations with their patients to provide more targeted advice based on individual levels of risk.
The model could also be used to inform mathematical modelling of the potential impact of national public health policies on shielding and preventing infection and potentially help identify those at highest risk to be vaccinated, when available.
NHS Digital is the national safe haven for health and care data and provided its deep expertise of health data structures to curate information, perform quality assurance and to assist the academic team with their understanding of the data, ensuring that it was used appropriately and to its maximum potential.
Professor Jonathan Benger, Interim Chief Medical Officer at NHS Digital yesterday said:
"NHS Digital is delighted to use our data expertise to contribute to this hugely important piece of work. This is a comprehensive analysis of large patient data sets that will provide policy makers with high quality, evidence-led insights."
Principal Investigator, Professor Julia Hippisley-Cox, Professor of Epidemiology and General Practice at the University of Oxford’s Nuffield Department of Primary Care Health Sciences yesterday 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."
In the UK, government guidance on COVID-19 identifies individuals based on three broad categories of risk, with those who are ‘clinically extremely vulnerable’ to the disease previously being advised to shield themselves from the virus.
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 which can be applied across various health and care settings. Individualised risk assessment could be used to improve shared decision-making between clinicians and patients based on more accurate information, as well as discussions on how to reduce risk.
The project was a commission from the Office of the Chief Medical Officer for England to NERVTAG (New and Emerging Respiratory Virus Threats Advisory Group), who established the parameters and brought together the team as a sub-group of NERVTAG.
This team is led by the University of Oxford and includes researchers from 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, NHS Digital and NHS England.
The research team are planning to utilise other datasets from across all four nations of the UK to validate their model and offer a unified approach to evidence-based risk stratification policy.
Chief Medical Officer for England, Professor Chris Whitty, yesterday 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."
The research is funded by the National Institute for Health Research Oxford Biomedical Research Centre, and the University of Oxford COVID-19 Rapid Response Fund with support from Wellcome and Cancer Research UK.
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