COVID-19 tool predicts risk of hospitalisation and death

UK researchers have developed a new risk model to predict a person’s risk of being admitted to hospital and dying from COVID-19.

The ‘QCOVID’ model has been shown to accurately estimate risk during the first wave of the pandemic in England, in new research involving the University of Liverpool.

The model uses readily available information about people, such as their age, ethnicity and whether they have certain pre-existing conditions to help identify individuals at highest risk of developing severe illness.

Developing QCOVID

The researchers used anonymous data from primary care, hospitals, COVID-19 test results and death registries to determine which factors were associated with poor outcomes during the first wave of COVID-19.

This data was used to create the QCOVID model, which provides a weighted, cumulative calculation of risk using the variables associated with poor COVID-19 outcomes.

The model was then tested in two independent sets of anonymised data, from January to April 2020 and from May 2020 to June 2020, to find out whether it accurately predicted severe outcomes due to COVID-19 during the first wave of the pandemic in England.

The research results, published in the BMJ, showed that the model performed well in predicting outcomes. People in the dataset whose calculated risk put them in the top 20% of predicted risk of death accounted for 94% of deaths from COVID-19.

Co-author Professor Calum Semple, Professor in Outbreak Medicine and Child Health at the University of Liverpool, said: “Used together the QCOVID and ISARIC 4C Mortality Score provide world class risk prediction for poor outcomes from COVID-19, allowing timely identification of people who can benefit from medical care.”

Ongoing development

The researchers plan to regularly update their model as levels of immunity change, more data become available and behaviour in the population changes, so that the model could also be used to support risk stratification for public health purposes as the infection rate changes over time.

The work was funded by the National Institute for Health Research (NIHR) following a commission by the Chief Medical Officer for England.

Deputy Chief Medical Officer for England Dr Jenny Harries said: “Continuing to improve our understanding of the virus and how it affects different members of the population is vital as prevalence continues to rise. This is why we commissioned and funded this research, and I’m pleased it is providing useful evidence to help us move towards a more nuanced understanding of COVID-19 risk.”

The research was led by the University of Oxford and involved researchers from the universities of Liverpool, Edinburgh, Swansea, Leicester, Nottingham and Cambridge with the London School of Hygiene & 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.

Research reference

Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from COVID-19 in adults: national derivation and validation cohort study, BMJ 2020371 doi: