Evaluation and Improvement of the National Early Warning Score (NEWS2) for COVID-19: a multi-hospital study

Ewan Carr, Rebecca Bendayan, Daniel Bean, Matt Stammers, Wenjuan Wang, Huayu Zhang, Thomas Searle, Zeljko Kraljevic, Anthony Shek, Hang TT Phan, Kings London, Rishi K Gupta, Anthony J Shinton, Mike Wyatt, Ting Shi, Xin Zhang, Andrew Pickles, Daniel Stahl, Rosita Zakeri, Mahdad NoursadeghiKevin O'Gallagher, Matt Rogers, Amos Folarin, Andreas Karwath, Kristin E Wickstrom, Alvaro Kohn-Luque, Luke Slater, Victor Roth Cardoso, Christopher Bourdeaux, Aleksander Rygh Holten, Simon Ball, Chris McWilliams, Lukasz Roguski, Florina Borca, James Bachelor, Erik Koldberg Amundsen, Xiaodong Wu, Georgios V Gkoutos, Jiaxing Sun, Ashwin Pinto, Bruce Guthrie, Cormac Breen, Abdel Douiri, Honghan Wu, Vasa Curcin, James T Teo, Ajay M Shah, Richard J B Dodson

Research output: Contribution to journalArticlepeer-review

Abstract

Background
The National Early Warning Score (NEWS2) is currently recommended in
the United Kingdom for risk-stratification of COVID patients, but little is
known about its ability to detect severe cases. We aimed to evaluate NEWS2
for prediction of severe COVID outcome and identify and validate a set of
blood and physiological parameters routinely-collected at hospital admission
to improve upon use of NEWS2 alone for medium-term risk stratification.

Methods
Training cohorts comprised 1276 patients admitted to King’s College
Hospital NHS Foundation Trust with COVID-19 disease from 1st March to
30th April 2020. External validation cohorts included 6237 patients from five
UK NHS Trusts (Guys and St Thomas’ Hospitals, University Hospitals
Southampton, University Hospitals Bristol and Weston NHS Foundation
Trust, University College London Hospitals, University Hospitals
Birmingham), one hospital in Norway (Oslo University Hospital), and two
hospitals in Wuhan, China (Wuhan Sixth Hospital and Taikang Tongji
Hospital). The outcome was severe COVID disease (transfer to intensive care
unit or death) at 14 days after hospital admission. Age, physiological
measures, blood biomarkers, sex, ethnicity and comorbidities (hypertension,
diabetes, cardiovascular, respiratory and kidney diseases) measured at
hospital admission were considered in the models.

Results
A baseline model of ‘NEWS2 + age’ had poor-to-moderate discrimination for
severe COVID infection at 14 days (area under receiver operating
characteristic curve (AUC) in training cohort = 0.700; 95% CI: 0.680, 0.722;
Brier score = 0.192; 95% CI: 0.186, 0.197). A supplemented model adding
eight routinely-collected blood and physiological parameters (supplemental
oxygen flow rate, urea, age, oxygen saturation, CRP, estimated GFR,
neutrophil count, neutrophil/lymphocyte ratio) improved discrimination
(AUC = 0.735; 95% CI: 0.715, 0.757) and these improvements were
replicated across seven UK and non-UK sites. However, there was evidence
of miscalibration with the model tending to underestimate risks in most sites.

Conclusions
NEWS2 score had poor-to-moderate discrimination for medium-term COVID
outcome which raises questions about its use as a screening tool at hospital
admission. Risk stratification was improved by including readily available
blood and physiological parameters measured at hospital admission, but there
was evidence of miscalibration in external sites. This highlights the need for a
better understanding of the use of early warning scores for COVID.

Keywords: NEWS2 score, Blood parameters, COVID-19, prediction model.
Original languageEnglish
Article number23
JournalBMC Medicine
Volume19
Issue number23
DOIs
Publication statusPublished - 21 Jan 2021

Keywords

  • NEWS2 score
  • Blood parameters
  • COVID-19
  • prediction model

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