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 Noursadeghi; Kevin 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

doi:10.1186/s12916-020-01893-3

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 journal › Article › peer-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 language	English
Article number	23
Journal	BMC Medicine
Volume	19
Issue number	23
DOIs	https://doi.org/10.1186/s12916-020-01893-3
Publication status	Published - 21 Jan 2021

Keywords / Materials (for Non-textual outputs)

NEWS2 score
Blood parameters
COVID-19
prediction model

Access to Document

10.1186/s12916-020-01893-3Licence: Creative Commons: Attribution (CC-BY)

revised_manuscript_unlinkedAccepted author manuscript, 536 KBLicence: Creative Commons: Attribution (CC-BY)

https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-020-01893-3Licence: Creative Commons: Attribution (CC-BY)

Cite this

Carr, E., Bendayan, R., Bean, D., Stammers, M., Wang, W., Zhang, H., Searle, T., Kraljevic, Z., Shek, A., Phan, H. TT., London, K., Gupta, R. K., Shinton, A. J., Wyatt, M., Shi, T., Zhang, X., Pickles, A., Stahl, D., Zakeri, R., ... Dodson, R. J. B. (2021). Evaluation and Improvement of the National Early Warning Score (NEWS2) for COVID-19: a multi-hospital study. BMC Medicine, 19(23), Article 23. https://doi.org/10.1186/s12916-020-01893-3

@article{07003bf8bb78483f8fa73f26b0cb86e3,

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

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{\textquoteright}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{\textquoteright} 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 {\textquoteleft}NEWS2 + age{\textquoteright} 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. ",

keywords = "NEWS2 score, Blood parameters, COVID-19, prediction model",

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

year = "2021",

month = jan,

day = "21",

doi = "10.1186/s12916-020-01893-3",

language = "English",

volume = "19",

journal = "BMC Medicine",

issn = "1741-7015",

publisher = "BioMed Central",

number = "23",

}

Carr, E, Bendayan, R, Bean, D, Stammers, M, Wang, W, Zhang, H, Searle, T, Kraljevic, Z, Shek, A, Phan, HTT, London, K, Gupta, RK, Shinton, AJ, Wyatt, M, Shi, T, Zhang, X, Pickles, A, Stahl, D, Zakeri, R, Noursadeghi, M, O'Gallagher, K, Rogers, M, Folarin, A, Karwath, A, Wickstrom, KE, Kohn-Luque, A, Slater, L, Cardoso, VR, Bourdeaux, C, Holten, AR, Ball, S, McWilliams, C, Roguski, L, Borca, F, Bachelor, J, Amundsen, EK, Wu, X, Gkoutos, GV, Sun, J, Pinto, A, Guthrie, B, Breen, C, Douiri, A, Wu, H, Curcin, V, Teo, JT, M Shah, A & Dodson, RJB 2021, 'Evaluation and Improvement of the National Early Warning Score (NEWS2) for COVID-19: a multi-hospital study', BMC Medicine, vol. 19, no. 23, 23. https://doi.org/10.1186/s12916-020-01893-3

TY - JOUR

T1 - Evaluation and Improvement of the National Early Warning Score (NEWS2) for COVID-19

T2 - a multi-hospital study

AU - Carr, Ewan

AU - Bendayan, Rebecca

AU - Bean, Daniel

AU - Stammers, Matt

AU - Wang, Wenjuan

AU - Zhang, Huayu

AU - Searle, Thomas

AU - Kraljevic, Zeljko

AU - Shek, Anthony

AU - Phan, Hang TT

AU - London, Kings

AU - Gupta, Rishi K

AU - Shinton, Anthony J

AU - Wyatt, Mike

AU - Shi, Ting

AU - Zhang, Xin

AU - Pickles, Andrew

AU - Stahl, Daniel

AU - Zakeri, Rosita

AU - Noursadeghi, Mahdad

AU - O'Gallagher, Kevin

AU - Rogers, Matt

AU - Folarin, Amos

AU - Karwath, Andreas

AU - Wickstrom, Kristin E

AU - Kohn-Luque, Alvaro

AU - Slater, Luke

AU - Cardoso, Victor Roth

AU - Bourdeaux, Christopher

AU - Holten, Aleksander Rygh

AU - Ball, Simon

AU - McWilliams, Chris

AU - Roguski, Lukasz

AU - Borca, Florina

AU - Bachelor, James

AU - Amundsen, Erik Koldberg

AU - Wu, Xiaodong

AU - Gkoutos, Georgios V

AU - Sun, Jiaxing

AU - Pinto, Ashwin

AU - Guthrie, Bruce

AU - Breen, Cormac

AU - Douiri, Abdel

AU - Wu, Honghan

AU - Curcin, Vasa

AU - Teo, James T

AU - M Shah, Ajay

AU - Dodson, Richard J B

PY - 2021/1/21

Y1 - 2021/1/21

N2 - 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.

AB - 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.

KW - NEWS2 score

KW - Blood parameters

KW - COVID-19

KW - prediction model

U2 - 10.1186/s12916-020-01893-3

DO - 10.1186/s12916-020-01893-3

M3 - Article

SN - 1741-7015

VL - 19

JO - BMC Medicine

JF - BMC Medicine

IS - 23

M1 - 23

ER -

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

Abstract

Keywords / Materials (for Non-textual outputs)

Access to Document

Other files and links

Fingerprint

Cite this