NEWS2 was shown to be poor at identifying COVID-19 patients at risk of needing intensive care.
The National Early Warning Risk Score (NEWS) 2 only has poor-to-moderate accuracy in identifying patients at risk of being transferred to intensive care units (ICUs) or dying, according to a new study.
The study, published today in BMC Medicine, analysed data from 1,276 COVID-19 patients admitted to King’s College Hospital NHS Foundation Trust during the first wave in March-April 2020.
The researchers evaluated how well patients’ NEWS2 scores measured at hospital admission anticipated who would have severe COVID-19 outcomes, which means either being transferred to ICU or dying.
In all UK sites, combining NEWS2 and age to predict outcomes showed moderate success in the short term (three days), but for poor-to-moderate success for medium-term (14 days) outcomes.
Dr Ewan Carr, Statistician Research Fellow at the Institute of Psychiatry, Psychology & Neuroscience, 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.
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.
In trial models that supplemented NEWS2 with these parameters the ability to predict severe outcomes was improved.
Dr James Teo, a Consultant Neurologist at King’s College Hospital and Clinical Director of Data Science and lead of CogStack platform, said: “Our results for the first time validates NEWS2, and shows how it could be improved by adding common blood and physiological parameters.
“Thankfully, this NHS scoring system is easy to adapt and implement in real-world clinical practice, compared to other complex risk-scoring models.