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

Research output: Contribution to journalArticlepeer-review

Authors

  • Ewan Carr
  • Rebecca Bendayan
  • Daniel Bean
  • Matt Stammers
  • Wenjuan Wang
  • Huayu Zhang
  • Thomas Searle
  • Zeljko Kraljevic
  • Anthony Shek
  • Hang T T Phan
  • Walter Muruet
  • 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
  • Kristin E Wickstrøm
  • Alvaro Köhn-Luque
  • Christopher Bourdeaux
  • Aleksander Rygh Holten
  • Simon Ball
  • Chris McWilliams
  • Lukasz Roguski
  • Florina Borca
  • James Batchelor
  • Erik Koldberg Amundsen
  • Xiaodong Wu
  • Jiaxing Sun
  • Ashwin Pinto
  • Bruce Guthrie
  • Cormac Breen
  • Abdel Douiri
  • Honghan Wu
  • Vasa Curcin
  • James T Teo
  • Ajay M Shah
  • Richard J B Dobson

External organisations

  • University Hospital Birmingham
  • Midlands Health Data Research, U.K.

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.

Bibliographic note

Publisher Copyright: © 2021, The Author(s).

Details

Original languageEnglish
Article number23
Number of pages16
JournalBMC medicine
Volume19
Issue number1
Early online date21 Jan 2021
Publication statusE-pub ahead of print - 21 Jan 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, NEWS2 score, Prediction model, Blood parameters, COVID-19

ASJC Scopus subject areas