Usher Institute

Covid-19 risk tool helps guide treatment

People who are admitted to hospital with Covid-19 can be divided into four distinct groups, according to data from the world’s largest study of patients with the disease.

Researchers identified the groups using clinical information and tests carried out upon arrival at hospital to predict the patients’ risk of death – ranging from low to very high.

A Covid-19 risk identification tool – the most accurate to date – has been designed using the groupings to help clinical staff choose the best course of treatment for patients admitted to hospital.


The tool was built by the ISARIC Coronavirus Clinical Characterisation Consortium involving researchers from Universities of Edinburgh, Glasgow, Liverpool and Imperial College London.

It was built using data from some 35,000 patients admitted to hospital between February and May 2020 who met the criteria for one of the four groups.

The tool was then tested and confirmed to be accurate using data from a further 22,000 patients hospitalised from the end of May to the end of June 2020.

Blood tests

Some of the data used to identify which group a person falls into – and, therefore, their risk of dying – included age, sex, the number of pre-existing conditions, respiratory rate on admission, and the results of two blood tests.

One in every hundred patients in the low-risk group was found to be at risk of dying. It was 10 in a hundred patients in the intermediate-risk group, 31 in a hundred in the high-risk group and 62 in a hundred in the very high-risk group. 

Home or hospital

The categorisations make new treatment pathways possible, researchers say.

For example, it might be more appropriate for those who fall into the low-risk subgroup to be treated at home.

In contrast, people in the high or very high risk groups could benefit from more aggressive treatment, such as the use of antivirals and early admission to critical care.

Accurate predictions

Until now there has not been an accurate risk tool for Covid-19 patients. Existing tools for pneumonia or sepsis do not offer accurate predictions due to the differences between diseases.

Previous attempts to build a risk prediction tool for Covid-19 have had limited success due to small sample sizes and lack of formal validation.

One limitation of this new tool, however, is that it can only be used on hospital patients and not within the community.


The work is the latest result from ISARIC – a global network of clinicians and scientists who have been preparing to prevent disease and death from severe outbreaks since 2012 in readiness for a pandemic such as this. It involved 260 hospitals across England, Wales and Scotland.

The ISARIC 4C study includes two thirds of all people admitted to hospital with Covid-19.  

As doctors, we want to identify groups of patients most at risk of dying from Covid-19. If we can do that at the front door of the hospital, then treatment can be better planned. This easy-to-use tool will help doctors make decisions to provide patients with the optimal care.

Professor Ewen HarrisonChair in Surgery and Data Science, University of Edinburgh and Honorary Consultant Surgeon, NHS Lothian

This accurate and simple risk identification tool, applicable across all groups within society, will help detect at risk individuals quickly on arrival to hospital. As importantly, we will be able to reassure and potentially treat at home those patients who fall within the low risk group.

Dr Stephen KnightNIHR Clinical Research Fellow, University of Edinburgh

Protecting the most vulnerable from Covid-19 is a priority, which is why we’re supporting valuable research like this to help doctors make the best possible decisions for NHS patients, and I am delighted to see my former University leading the way on it. We look forward to seeing how this new tool can help clinicians target treatments more effectively for coronavirus patients admitted to hospital now and in the future, potentially saving countless lives.

Lord BethellUK Government Minister for Innovation

The research - published in the BMJ - was funded by UK Research and Innovation (UKRI) and by the Department of Health and Social Care through the National Institute for Health Research (NIHR) as part of the UK Government’s Covid-19 rapid research response.  

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Original article