Evaluation and Improvement of the National Early Warning Score (NEWS2) for COVID-19: a multi-hospital study
Authors: Carr Ewan , Bendayan Rebecca , Bean Daniel , Stammers Matt , Wang Wenjuan , Zhang Huayu , Searle Thomas , Kraljevic Zeljko , Shek Anthony , Phan Hang T T , Muruet Walter , Gupta Rishi K , Shinton Anthony J , Wyatt Mike , Shi Ting , Zhang Xin , Pickles Andrew , Stahl Daniel , Zakeri Rosita , Noursadeghi Mahdad , O’Gallagher Kevin , Rogers Matt , Folarin Amos , Bourdeaux Christopher , McWilliams Chris , Roguski Lukasz , Borca Florina , Batchelor James , Wu Xiaodong , Sun Jiaxing , Pinto Ashwin , Guthrie Bruce , Breen Cormac , Douiri Abdel , Wu Honghan , Curcin Vasa , Teo James T , Shah Ajay M , Dobson Richard J B 
Published: April 2020

Background

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

Methods

Training cohorts comprised 1276 patients admitted to King’s College Hospital NHS Foundation Trust with COVID-19 disease from 1stMarch to 30thApril 2020. External validation cohorts included 5037 patients from four UK NHS Trusts (Guys and St Thomas’ Hospitals, University Hospitals Southampton, University Hospitals Bristol and Weston NHS Foundation Trust, University College London Hospitals), 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 (AUC in training sample = 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 five 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.

Key messages

The National Early Warning Score (NEWS2), currently recommended for stratification of severe COVID-19 disease in the UK, showed poor-to-moderate discrimination for medium-term outcomes (14-day transfer to ICU or death) among COVID-19 patients.

Risk stratification was improved by the addition of routinely-measured blood and physiological parameters routinely at hospital admission (supplemental oxygen, urea, oxygen saturation, CRP, estimated GFR, neutrophil count, neutrophil/lymphocyte ratio) which provided moderate improvements in a risk stratification model for 14-day ICU/death.

This improvement over NEWS2 alone was maintained across multiple hospital trusts but the model tended to be miscalibrated with risks of severe outcomes underestimated in most sites.

We benefited from existing pipelines for informatics at KCH such as CogStack that allowed rapid extraction and processing of electronic health records. This methodological approach provided rapid insights and allowed us to overcome the complications associated with slow data centralisation approaches.