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The NHS algorithm used to determine which Covid patients are most at risk of dying is not accurate

The NHS algorithm used to recognize people most at risk of dying or being admitted to intensive care performs ‘poorly’ in Covid-19 patients

  • NEWS2 is an algorithm almost universally used in the NHS
  • It gives a patient a score that indicates how likely they are to deteriorate
  • But researchers at King’s College London found ‘poor to moderate’ performance when used in hospitalized Covid-19 patients

An algorithm used by the NHS to predict which patients are most likely to deteriorate is imprecise when used in Covid-19 patients, a new study shows.

The algorithm has been in use for years and is endorsed by NHS England. It gives each patient a score that predicts how their condition will change.

Researchers from King’s College London examined how well the algorithm predicted the health outcomes of 1,276 Covid-19 patients hospitalized in March and April 2020.

The data reveals ‘poor to moderate’ accuracy for identifying patients at risk of being admitted to the ICU or dying two weeks later.

It performed ‘moderately’ in the short term, three days later, the study authors say.

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Researchers at King's College London studied how good the algorithm was at predicting 1,276 Covid-19 patients hospitalized in March and April 2020 and found it performed at a moderate to moderate level (file)

Researchers at King’s College London studied how good the algorithm was at predicting 1,276 Covid-19 patients hospitalized in March and April 2020 and found it performed at a moderate to moderate level (file)

Doctors use NEWS2 – UK National Early Warning Risk Score – to prioritize care for those most at risk of rapid deterioration.

It takes into account many characteristics of a patient’s current condition, including their respiration rate, oxygen saturation, blood pressure and temperature.

The algorithm is used by 100 percent of ambulance trusts and 76 percent of acute trusts in England, NHS England says.

The first analysis performed on 1,276 patients in London then compared more than 6,000 Covid patients in eight hospitals worldwide, with five in the UK, one in Norway and two in Wuhan, China.

The NEWS2 algorithm is used by 100 percent of ambulance trusts and 76 percent of acute trusts in England, NHS England says.

The NEWS2 algorithm is used by 100 percent of ambulance trusts and 76 percent of acute trusts in England, NHS England says.

The NEWS2 algorithm is used by 100 percent of ambulance trusts and 76 percent of acute trusts in England, NHS England says.

They directly compared the NEWS2 rating on admission to their final condition, focusing specifically on admissions to intensive care or death.

Dr. Ewan Carr, co-author of the study from KCL, said: “We conducted the largest study to date to evaluate the accuracy of NEWS2 for predicting COVID outcomes in the medium term.

‘NEWS2 is widely used in UK NHS trusts, but little is known about how well it can predict severe COVID outcomes and it is therefore important to evaluate its accuracy as we aim to improve patient care now and in the future.

‘By collecting data from nine hospitals worldwide, our results have been robustly validated externally.’

The researchers found that the accuracy of the system was improved when additional information was entered – specifically, age, oxygen saturation, and neutrophil count, a type of white blood cell that fights viruses.

Dr. James Teo, a neurologist at King’s College Hospital and co-author of the study, said, “Our results validate NEWS2 for the first time and show how it can be improved by adding common blood and physiological parameters.

“Fortunately, this NHS scoring system is easy to adapt and implement in practice, compared to other complex risk scoring models.”

A spokesperson for the NHS said: “ As this study rightly points out, the National Early Warning Scores system works best in addition to, but not in place of, clinical judgment and when also taking into account important other clinical factors and pre-existing conditions. .

“The NHS has been clear throughout the pandemic that clinical teams should not rely solely on NEWS2, which is exactly what the staff have done.”

Wearing face masks will stop the spread of Covid-19

Wearing face masks is an effective way to stop the spread of the coronavirus, a groundbreaking study shows.

Researchers in the US have found that a 10 percent increase in self-reported mask wearing is associated with a 3-fold increase in the likelihood of keeping the R-number below 1.

R is the number of people to whom an infected person transmits the virus on average. If the R-value is less than 1, it means that the epidemic is decreasing.

However, the scientists warn that while facial covers can help control the transmission of Covid-19 in the community, they should not be a substitute for other measures of the coronavirus, such as social distancing.

The US-based experts said their findings have been published in the journal Lancet Digital Health, suggest that communities with many reported mask wears and physical distances are most likely to be able to control transmission.

They used a computer model to determine what interventions are needed to bring the R-rate below 1, which is necessary to consider the pandemic as ‘under control’.

The scientists found that the chances of this happening are 3.53 times higher when wearing a mask increases by 10 percent, regardless of what the baseline is.

This map shows the percentage of each geographic region of the US and shows the percentage of people who say they are 'very likely' or 'somewhat likely' to wear a mask when visiting family or friends

This map shows the percentage of each geographic region of the US and shows the percentage of people who say they are 'very likely' or 'somewhat likely' to wear a mask when visiting family or friends

This map shows the percentage of each geographic region of the US and shows the percentage of people who say they are ‘very likely’ or ‘somewhat likely’ to wear a mask when visiting family or friends

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