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

Evaluation and improvement of the National Early Warning Score (NEWS2) for COVID-19: a multi-hospital study
Evaluation and improvement of the National Early Warning Score (NEWS2) for COVID-19: a multi-hospital study
Background: the National Early Warning Score (NEWS2) is currently recommended in the UK for the risk stratification of COVID-19 patients, but little is known about its ability to detect severe cases. We aimed to evaluate NEWS2 for the prediction of severe COVID-19 outcome and identify and validate a set of blood and physiological parameters routinely collected at hospital admission to improve upon the use of NEWS2 alone for medium-term risk stratification.

Methods: training cohorts comprised 1276 patients admitted to King’s College Hospital National Health Service (NHS) Foundation Trust with COVID-19 disease from 1 March to 30 April 2020. External validation cohorts included 6237 patients from five UK NHS Trusts (Guy’s 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-19 disease (transfer to intensive care unit (ICU) 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-19 infection at 14 days (area under receiver operating characteristic curve (AUC) in training cohort = 0.700, 95% confidence interval (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, C-reactive protein, estimated glomerular filtration rate, 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-19 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.
Aged, COVID-19/diagnosis, Cohort Studies, Early Warning Score, Electronic Health Records, Female, Humans, Male, Middle Aged, Pandemics, Prognosis, SARS-CoV-2/isolation & purification, State Medicine, United Kingdom/epidemiology
1741-7015
Carr, Ewan
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Bendayan, Rebecca
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Bean, Daniel
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Stammers, Matt
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Wang, Wenjuan
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Searle, Thomas
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Shek, Anthony
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Phan, Hang T.T.
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Muruet, Walter
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Shi, Ting
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Pickles, Andrew
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Stahl, Daniel
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O’Gallagher, Kevin
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Rogers, Matt
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Folarin, Amos
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Karwath, Andreas
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Wickstrøm, Kristin E.
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Köhn-Luque, Alvaro
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Slater, Luke
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Bourdeaux, Christopher
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Holten, Aleksander Rygh
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Ball, Simon
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McWilliams, Chris
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Roguski, Lukasz
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Borca, Florina
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Batchelor, James
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Amundsen, Erik Koldberg
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Wu, Xiaodong
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Gkoutos, Georgios V.
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Sun, Jiaxing
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Pinto, Ashwin
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Guthrie, Bruce
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Douiri, Abdel
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Wu, Honghan
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Curcin, Vasa
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et al.
Carr, Ewan
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Bendayan, Rebecca
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Bean, Daniel
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Stammers, Matt
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Zhang, Huayu
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Searle, Thomas
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Shek, Anthony
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Kraljevic, Zeljko
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Phan, Hang T.T.
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Muruet, Walter
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Gupta, Rishi K.
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Shinton, Anthony J.
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Wyatt, Mike
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Shi, Ting
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Zhang, Xin
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Stahl, Daniel
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Zakeri, Rosita
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Noursadeghi, Mahdad
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O’Gallagher, Kevin
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Rogers, Matt
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Folarin, Amos
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Karwath, Andreas
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Wickstrøm, Kristin E.
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Köhn-Luque, Alvaro
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Slater, Luke
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Cardoso, Victor Roth
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Bourdeaux, Christopher
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Holten, Aleksander Rygh
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Ball, Simon
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McWilliams, Chris
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Roguski, Lukasz
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Borca, Florina
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Batchelor, James
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Amundsen, Erik Koldberg
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Wu, Xiaodong
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Gkoutos, Georgios V.
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Sun, Jiaxing
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Pinto, Ashwin
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Guthrie, Bruce
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Breen, Cormac
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Douiri, Abdel
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Wu, Honghan
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Curcin, Vasa
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Teo, James T.
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Shah, Ajay M.
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Dobson, Richard J.B.
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Carr, Ewan, Bendayan, Rebecca, Bean, Daniel, Phan, Hang T.T., Shi, Ting and Batchelor, James , et al. (2021) Evaluation and improvement of the National Early Warning Score (NEWS2) for COVID-19: a multi-hospital study. BMC Medicine, 19, [23]. (doi:10.1186/s12916-020-01893-3).

Record type: Article

Abstract

Background: the National Early Warning Score (NEWS2) is currently recommended in the UK for the risk stratification of COVID-19 patients, but little is known about its ability to detect severe cases. We aimed to evaluate NEWS2 for the prediction of severe COVID-19 outcome and identify and validate a set of blood and physiological parameters routinely collected at hospital admission to improve upon the use of NEWS2 alone for medium-term risk stratification.

Methods: training cohorts comprised 1276 patients admitted to King’s College Hospital National Health Service (NHS) Foundation Trust with COVID-19 disease from 1 March to 30 April 2020. External validation cohorts included 6237 patients from five UK NHS Trusts (Guy’s 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-19 disease (transfer to intensive care unit (ICU) 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-19 infection at 14 days (area under receiver operating characteristic curve (AUC) in training cohort = 0.700, 95% confidence interval (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, C-reactive protein, estimated glomerular filtration rate, 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-19 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.

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Accepted/In Press date: 16 December 2020
e-pub ahead of print date: 21 January 2021
Published date: 21 January 2021
Keywords: Aged, COVID-19/diagnosis, Cohort Studies, Early Warning Score, Electronic Health Records, Female, Humans, Male, Middle Aged, Pandemics, Prognosis, SARS-CoV-2/isolation & purification, State Medicine, United Kingdom/epidemiology

Identifiers

Local EPrints ID: 447132
URI: http://eprints.soton.ac.uk/id/eprint/447132
ISSN: 1741-7015
PURE UUID: a1583caa-585d-4a1d-b3a8-04ef78aa2f93
ORCID for Matt Stammers: ORCID iD orcid.org/0000-0003-3850-3116
ORCID for James Batchelor: ORCID iD orcid.org/0000-0002-5307-552X

Catalogue record

Date deposited: 03 Mar 2021 17:36
Last modified: 21 Sep 2024 02:15

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Contributors

Author: Ewan Carr
Author: Rebecca Bendayan
Author: Daniel Bean
Author: Matt Stammers ORCID iD
Author: Wenjuan Wang
Author: Huayu Zhang
Author: Thomas Searle
Author: Anthony Shek
Author: Zeljko Kraljevic
Author: Hang T.T. Phan
Author: Walter Muruet
Author: Rishi K. Gupta
Author: Anthony J. Shinton
Author: Mike Wyatt
Author: Ting Shi
Author: Xin Zhang
Author: Andrew Pickles
Author: Daniel Stahl
Author: Rosita Zakeri
Author: Mahdad Noursadeghi
Author: Kevin O’Gallagher
Author: Matt Rogers
Author: Amos Folarin
Author: Andreas Karwath
Author: Kristin E. Wickstrøm
Author: Alvaro Köhn-Luque
Author: Luke Slater
Author: Victor Roth Cardoso
Author: Christopher Bourdeaux
Author: Aleksander Rygh Holten
Author: Simon Ball
Author: Chris McWilliams
Author: Lukasz Roguski
Author: Florina Borca
Author: James Batchelor ORCID iD
Author: Erik Koldberg Amundsen
Author: Xiaodong Wu
Author: Georgios V. Gkoutos
Author: Jiaxing Sun
Author: Ashwin Pinto
Author: Bruce Guthrie
Author: Cormac Breen
Author: Abdel Douiri
Author: Honghan Wu
Author: Vasa Curcin
Author: James T. Teo
Author: Ajay M. Shah
Author: Richard J.B. Dobson
Corporate Author: et al.

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