People at highest risk of serious illness from SARS-CoV-2 infection may soon have access to better shielding advice based on a new data-driven risk prediction model being developed by an Oxford University-led team.
The Principal Investigator, Professor Julia Hippisley-Cox of the University of Oxford’s Nuffield Department of Primary Care Health Sciences, 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.”
The modelling tool came out of research funded by the NIHR Oxford Biomedical Research Centre (BRC) in early April.
Early BRC support helped to fund access to more timely data from NHS digital, as well as national intensive care data; this allowed the research team, which included the BRC’s Joint Theme Lead for Technology and Digital Health, Prof Peter Watkinson, to successfully bid for additional funds from the University of Oxford COVID-19 Rapid Response Fund, with support from Wellcome and Cancer Research UK.
Prof Hippisley-Cox says that this risk modelling project “would not have arisen without the seed funding from the BRC, which helped leverage funding and provided further opportunities”.
In the UK, government guidance on COVID-19 shielding identifies individuals based on three broad categories of risk, with those who are ‘clinically extremely vulnerable’ to the disease being advised to shield themselves from the virus.
This tool will provide more sophisticated and nuanced information to help make decisions, especially important as lockdown is gradually eased across the UK.
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 and serious illness from COVID-19.
These factors 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 that 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 model, which is supported by NHS Digital, 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 receive a COVID-19 vaccine when it becomes available.
Professor Keith Channon, Deputy Head of the University of Oxford’s Medical Sciences Division and former Director of the Oxford BRC, said: “Combining leading expertise in clinical epidemiology and analytical techniques with very large sets of NHS clinical data to develop this new tool illustrates the power of our University and NHS researchers working together, to benefit people at risk of COVID-19.”
The project was a commission from the Office of the Chief Medical Officer for England to the government’s New and Emerging Respiratory Virus Threats Advisory Group (NERVTAG), which established the parameters and brought together the team as a sub-group of NERVTAG. It is funded by the National Institute for Health Research (NIHR).
As well as the Oxford team, the project 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, NHS Digital and NHS England.
The research team are planning to use datasets from across all four nations of the UK to validate their model and offer a unified approach to evidence-based risk stratification policy.
The Chief Medical Officer for England, Professor Chris Whitty, 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.”