New COVID-19 Risk Assessment Tool to Predict Case Mortality Rates
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A tool built by the ISARIC Coronavirus Clinical Characterisation Consortium will enable those who are admitted to the hospital with COVID-19 to be divided into four distinct risk groups and offered treatment accordingly, the world's largest study of patients with the disease shows.
The groups were identified using clinical information and tests carried out on patients on arrival at hospitals across England, Wales, and Scotland. A patient's risk of death, from being low to very high, was predicted using the collected data.
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This tool has numerous potential benefits since an influx of patients with COVID-19 means that hospitals need a quick risk stratification tool that can identify patients at the highest risk of death and inform treatment decisions.
Developing a pragmatic risk stratification score
Since existing tools have cut short amid the pandemic, a team of U.K. researchers developed a pragmatic risk stratification score. The data was collected from more than 35,000 adults with COVID-19 that were admitted to 260 hospitals between 6 February and 20 May 2020.
Some of the information collected were age, sex, the number of pre-existing conditions, the respiratory rate on admission, and the results of two blood tests. These were then used to give a score ranging from 0-21 points.
For example, those with a score of 15 or more had a 62% chance of mortality compared to those with a score of three or less, which had 1%. Those who scored nine or more were at a high risk of death, "which could prompt aggressive treatment, including the initiation of steroids and early escalation to critical care if appropriate", the researchers wrote. Those with a 1% mortality rate were found to "be suitable for management in the community"
From the end of May to the end of June 2020, the tool was tested and confirmed to be accurate after being used on another 22,000 patients, confirming the good news.
According to Dr. Stephen Knight, co-lead author and NIHR Clinical Research Fellow at the University of Edinburgh, "This accurate and simple risk identification tool, applicable across all groups within society, will help detect at-risk individuals quickly on arrival to [the] hospital. As importantly, we will be able to reassure and potentially treat at home those patients who fall within the low-risk group."
The study was published in the latest issue of the British Medical Journal.
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