Researchers at King’s College London have conducted a large scale study into the accuracy of National Early Warning Risk Score (NEWS) 2 as a scoring system for predicting severe COVID-19 outcomes in patients.
The researchers found poor-to-moderate accuracy for identifying patients at risk of being transferred to intensive care units, however they said predicting severe outcomes was improved by considering routinely-collected blood and physiological parameters.
Analysing data from 1,276 COVID-19 patients admitted to King’s College Hospital NHS FT during the first wave in March-April 2020, the researchers evaluated how well patients’ NEWS2 scores measured at hospital admission, who would then have severe COVID-19 outcomes.
By combining NEWS2 and age to predict outcomes showed moderate success in the short-term (three days), but poor-to-moderate success for medium-term (14 days) outcomes.
The researchers then validated their initial findings with data from over 6,000 patients across eight other hospitals globally; five in the UK, one in Norway, and two in China.
Researchers found that accuracy in predicting severe outcomes was improved by considering routinely-collected blood and physiological parameters from patients including age, oxygen saturation and neutrophil count, which is an important infection-fighting immune system cell. In models that supplemented NEWS2 with these parameters the ability to predict severe outcomes was improved.
Dr Ewan Carr, Statistician Research Fellow at the Institute of Psychiatry, Psychology & Neuroscience (IoPPN), King’s College London, and co-lead author, said:“We have conducted the largest study to date evaluating the accuracy of NEWS2 for predicting medium-term COVID outcomes. NEWS2 is widely used in UK NHS trusts but little is known about how well it can predict severe COVID outcomes and so evaluating its accuracy is important as we look to improve patient care now and in future.
“By collecting data from nine hospitals globally, our results have robust external validation. We found consistency across sites both in the performance of NEWS2 alone, as well as for the supplemented model. In short, NEWS2 tended to have poor-moderate performance but was improved by adding common blood and physiological measures.”
The research team used CogStack, an existing platform developed by NIHR Maudsley BRC and in use at King’s College Hospital NHS Foundation Trust, which allows for extraction and processing of data from patient’s electronic health records.
Professor Richard Dobson, Head of the Department of Biostatistics & Health Informatics, NIHR Maudsley BRC, said: “The CogStack platform allows us to extract information from deep within hospital records at King’s College Hospital NHS Foundation Trust in real time in order really explore complex questions such as this. This includes being able to extract information relating to co-morbidities, for example, that may only be mentioned in passing in the physician narrative.”
The study was a collaboration between the National Institute for Health Research, Maudsley Biomedical Research Centre, UK Health Data Research Alliance and the Medical Research Council.
