The optimal threshold for prompt clinical review: An external validation study of the national early warning score

AbstractObjectivesTo consider the potential of the National Early Warning Score (NEWS2) for COVID-19 risk prediction on unplanned admission to hospital.DesignLogistic regression model development and validation study using a cohort of unplanned emergency medical admission to hospital.SettingYork Hospital (YH) as model development dataset and Scarborough Hospital (SH) as model validation dataset.ParticipantsUnplanned adult medical admissions discharged over 3 months (11 March 2020 to 13 June 2020) from two hospitals (YH for model development; SH for external model validation) based on admission NEWS2 electronically recorded within ±24 hours of admission. We used logistic regression modelling to predict the risk of COVID-19 using NEWS2 (Model M0’) versus enhanced cNEWS models which included age + sex (model M1’) + subcomponents (including diastolic blood pressure + oxygen flow rate + oxygen scale) of NEWS2 (model M2’). The ICD-10 code ‘U071’ was used to identify COVID-19 admissions. Model performance was evaluated according to discrimination (c statistic), calibration (graphically), and clinical usefulness at NEWS2 ≥5.ResultsThe prevalence of COVID-19 was higher in SH (11.0%=277/2520) than YH (8.7%=343/3924) with higher index NEWS2 (3.2 vs 2.8) but similar in-hospital mortality (8.4% vs 8.2%). The c-statistics for predicting COVID-19 for cNEWS models (M1’,M2’) was substantially better than NEWS2 alone (M0’) in development (M2’: 0.78 (95%CI 0.75-0.80) vs M0’ 0.71 (95%CI 0.68-0.74)) and validation datasets (M2’: 0.72 (95%CI 0.69-0.75) vs M0’ 0.65 (95%CI 0.61-0.68)). Model M2’ had better calibration than Model M0’ with improved sensitivity (M2’: 57% (95%CI 51%-63%) vs M0’ 44% (95%CI 38%-50%)) and similar specificity (M2’: 76% (95%CI 74%-78%) vs M0’ 75% (95%CI 73%-77%)) for validation dataset at NEWS2≥5.ConclusionsModel M2’ is reasonably accurate for predicting the on-admission risk of COVID-19. It may be clinically useful for an early warning system at the time of admission especially to triage large numbers of unplanned hospital admissions.

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

10.1101/2020.04.24.20078006 ◽

2020 ◽

Cited By ~ 8

Author(s):

Ewan Carr ◽

Rebecca Bendayan ◽

Daniel Bean ◽

Matt Stammers ◽

Wenjuan Wang ◽

...

Keyword(s):

Risk Stratification ◽

Early Warning ◽

External Validation ◽

Early Warning Score ◽

Minimal Set ◽

Estimated Gfr ◽

Risk Stratification Model ◽

Risk Scoring ◽

National Early Warning Score ◽

The Uk

AbstractObjectivesTo evaluate the National Early Warning Score (NEWS2), currently recommended in the UK for risk-stratification of severe COVID-19 outcomes, and subsequently identify and validate a minimal set of common parameters taken at hospital admission that improve the score.DesignRetrospective observational cohort with internal and multi-hospital external validation.SettingSecondary care.InterventionsNot applicable.ParticipantsMain outcome measuresResultsTraining and temporal external validation cohorts comprised 1464 patients admitted to King’s College Hospital NHS Foundation Trust (KCH) with COVID-19 disease from 1st March to 30th April 2020. External validation cohorts included 3869 patients from two UK NHS Trusts (Guys and St Thomas’ Hospitals, GSTT and University Hospitals Southampton, UHS) and two hospitals in Wuhan, China (Wuhan Sixth Hospital and Taikang Tongji Hospital).The primary outcome was patient status at 14 days after symptom onset categorised as severe disease (transferred to intensive care unit or death). Age, physiological measures, blood biomarkers, sex, ethnicity and comorbidities (hypertension, diabetes, cardiovascular, respiratory and kidney diseases) were included.ConclusionsNEWS2 score on admission was a weak predictor of severe COVID-19 infection (AUC = 0.628). Adding age and common blood tests (CRP, neutrophil count, estimated GFR and albumin) provided substantial improvements to a risk stratification model, particularly in relation to sensitivity, but performance was only moderate (AUC = 0.753). Improvement over NEWS2 remained robust and generalisable in GSTT (AUC = 0.817), UHS (AUC = 0.835) and Wuhan hospitals (AUC = 0.918).Adding age and a minimal set of blood parameters to NEWS2 improves the detection of patients likely to develop severe COVID-19 outcomes. This finding was replicated across NHS and non-UK hospitals. Adding a few common parameters to a pre-existing acuity score allows rapid and easy implementation of this risk-scoring system.Key MessagesThe National Early Warning Score (NEWS2), currently recommended for severe COVID-19 disease in the UK shows overall poor discrimination for severe outcomes (transfer to ICU or death). It can be improved by the addition of a small number of blood and physiological parameters routinely measured at hospital admission.The addition of age and a minimal set of common blood tests (C-reactive protein, neutrophil count, estimated GFR and albumin) provided substantial improvements in a risk stratification model.Although predictive performance varied from hospital to hospital, the improvement over NEWS2 alone was consistent across different patient cohorts.The proposed addition of a limited number of dichotomised parameters is easily derived from a pre-existing acuity score would be substantially easier to implement in a short-time scale compared to novel high-dimensional risk-scoring systems.

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Performance of externally validated enhanced computer-aided versions of the National Early Warning Score in predicting mortality following an emergency admission to hospital in England: a cross-sectional study

BMJ Open ◽

10.1136/bmjopen-2019-031596 ◽

2019 ◽

Vol 9 (11) ◽

pp. e031596 ◽

Cited By ~ 5

Author(s):

Muhammad Faisal ◽

Donald Richardson ◽

Andy Scally ◽

Robin Howes ◽

Kevin Beatson ◽

...

Keyword(s):

Early Warning ◽

Clinical Decision Making ◽

External Validation ◽

Model Development ◽

Emergency Admission ◽

Early Warning Score ◽

Cross Sectional ◽

Risk Of Death ◽

Computer Aided ◽

National Early Warning Score

ObjectivesIn the English National Health Service, the patient’s vital signs are monitored and summarised into a National Early Warning Score (NEWS) to support clinical decision making, but it does not provide an estimate of the patient’s risk of death. We examine the extent to which the accuracy of NEWS for predicting mortality could be improved by enhanced computer versions of NEWS (cNEWS).DesignLogistic regression model development and external validation study.SettingTwo acute hospitals (YH—York Hospital for model development; NH—Northern Lincolnshire and Goole Hospital for external model validation).ParticipantsAdult (≥16 years) medical admissions discharged over a 24-month period with electronic NEWS (eNEWS) recorded on admission are used to predict mortality at four time points (in-hospital, 24 hours, 48 hours and 72 hours) using the first electronically recorded NEWS (model M0) versus a cNEWS model which included age+sex (model M1) +subcomponents of NEWS (including diastolic blood pressure) (model M2).ResultsThe risk of dying in-hospital following emergency medical admission was 5.8% (YH: 2080/35 807) and 5.4% (NH: 1900/35 161). The c-statistics for model M2 in YH for predicting mortality (in-hospital=0.82, 24 hours=0.91, 48 hours=0.88 and 72 hours=0.88) was higher than model M0 (in-hospital=0.74, 24 hours=0.89, 48 hours=0.86 and 72 hours=0.85) with higher Positive Predictive Value (PPVs) for in-hospital mortality (M2 19.3% and M0 16.6%). Similar findings were seen in NH. Model M2 performed better than M0 in almost all major disease subgroups.ConclusionsAn externally validated enhanced computer-aided NEWS model (cNEWS) incrementally improves on the performance of a NEWS only model. Since cNEWS places no additional data collection burden on clinicians and is readily automated, it may now be carefully introduced and evaluated to determine if it can improve care in hospitals that have eNEWS systems.

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Experiences of a National Early Warning Score (NEWS) intervention in care homes during the COVID-19 pandemic: a qualitative interview study

BMJ Open ◽

10.1136/bmjopen-2020-045469 ◽

2021 ◽

Vol 11 (7) ◽

pp. e045469

Author(s):

Rachel Stocker ◽

Siân Russell ◽

Jennifer Liddle ◽

Robert O Barker ◽

Adam Remmer ◽

...

Keyword(s):

Early Warning ◽

Healthcare Professionals ◽

Care Home ◽

Qualitative Interview ◽

Interview Study ◽

Care Homes ◽

Early Warning Score ◽

Service Utilisation ◽

Qualitative Interview Study ◽

National Early Warning Score

BackgroundThe COVID-19 pandemic has taken a heavy toll on the care home sector, with residents accounting for up to half of all deaths in Europe. The response to acute illness in care homes plays a particularly important role in the care of residents during a pandemic. Digital recording of a National Early Warning Score (NEWS), which involves the measurement of physical observations, started in care homes in one area of England in 2016. Implementation of a NEWS intervention (including equipment, training and support) was accelerated early in the pandemic, despite limited evidence for its use in the care home setting.ObjectivesTo understand how a NEWS intervention has been used in care homes in one area of North-East England during the COVID-19 pandemic, and how it has influenced resident care, from the perspective of stakeholders involved in care delivery and commissioning.MethodsA qualitative interview study with care home (n=10) and National Health Service (n=7) staff. Data were analysed using thematic analysis.ResultsUse of the NEWS intervention in care homes in this area accelerated during the COVID-19 pandemic. Stakeholders felt that NEWS, and its associated education and support package, improved the response of care homes and healthcare professionals to deterioration in residents’ health during the pandemic. Healthcare professionals valued the ability to remotely monitor resident observations, which facilitated triage and treatment decisions. Care home staff felt empowered by NEWS, providing a common clinical language to communicate concerns with external services, acting as an adjunct to staff intuition of resident deterioration.ConclusionsThe NEWS intervention formed an important part of the care home response to COVID-19 in the study area. Positive staff perceptions now need to be supplemented with data on the impact on resident health and well-being, workload, and service utilisation, during the pandemic and beyond.

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