scholarly journals Predictive accuracy of computer-aided versions of the on-admission National Early Warning Score in estimating the risk of COVID-19 for unplanned admission to hospital: a retrospective development and validation study

2020 ◽  
Author(s):  
Muhammad Faisal ◽  
Mohammed A Mohammed ◽  
Donald Richardson ◽  
Ewout W. Steyerberg ◽  
Massimo Fiori ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Model Validation ◽  
Validation Study ◽  
Early Warning ◽  
Model Development ◽  
Early Warning Score ◽  
Validation Dataset ◽  
Unplanned Admission ◽  
National Early Warning Score ◽  
Development And Validation

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.

scholarly journals 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 ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. e031596 ◽  
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.

scholarly journals Predictive accuracy of enhanced versions of the on-admission National Early Warning Score in estimating the risk of COVID-19 for unplanned admission to hospital: a retrospective development and validation study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Muhammad Faisal ◽  
Mohammed Amin Mohammed ◽  
Donald Richardson ◽  
Ewout W. Steyerberg ◽  
Massimo Fiori ◽  
...  
Keyword(s):  
Early Warning ◽  
Prediction Models ◽  
Hospital Admissions ◽  
Predictive Accuracy ◽  
Model Performance ◽  
Early Warning Score ◽  
Scale Model ◽  
Validation Dataset ◽  
Early Assessment ◽  
National Early Warning Score

Abstract Background The novel coronavirus SARS-19 produces ‘COVID-19’ in patients with symptoms. COVID-19 patients admitted to the hospital require early assessment and care including isolation. The National Early Warning Score (NEWS) and its updated version NEWS2 is a simple physiological scoring system used in hospitals, which may be useful in the early identification of COVID-19 patients. We investigate the performance of multiple enhanced NEWS2 models in predicting the risk of COVID-19. Methods Our cohort included unplanned 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). We used logistic regression to build multiple prediction models for the risk of COVID-19 using the first electronically recorded NEWS2 within ± 24 hours of admission. Model M0’ included NEWS2; model M1’ included NEWS2 + age + sex, and model M2’ extends model M1’ with subcomponents of NEWS2 (including diastolic blood pressure + oxygen flow rate + oxygen scale). Model performance was evaluated according to discrimination (c statistic), calibration (graphically), and clinical usefulness at NEWS2 ≥ 5. Results The prevalence of COVID-19 was higher in SH (11.0 %=277/2520) than YH (8.7 %=343/3924) with a higher first NEWS2 scores ( SH 3.2 vs YH 2.8) but similar in-hospital mortality (SH 8.4 % vs YH 8.2 %). The c-statistics for predicting the risk of COVID-19 for models M0’,M1’,M2’ in the development dataset were: M0’: 0.71 (95 %CI 0.68–0.74); M1’: 0.67 (95 %CI 0.64–0.70) and M2’: 0.78 (95 %CI 0.75–0.80)). For the validation datasets the c-statistics were: M0’ 0.65 (95 %CI 0.61–0.68); M1’: 0.67 (95 %CI 0.64–0.70) and M2’: 0.72 (95 %CI 0.69–0.75) ). The calibration slope was similar across all models but Model M2’ had the highest sensitivity (M0’ 44 % (95 %CI 38-50 %); M1’ 53 % (95 %CI 47-59 %) and M2’: 57 % (95 %CI 51-63 %)) and specificity (M0’ 75 % (95 %CI 73-77 %); M1’ 72 % (95 %CI 70-74 %) and M2’: 76 % (95 %CI 74-78 %)) for the validation dataset at NEWS2 ≥ 5. Conclusions Model M2’ appears to be reasonably accurate for predicting the risk of COVID-19. It may be clinically useful as an early warning system at the time of admission especially to triage large numbers of unplanned hospital admissions.

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

BMJ Open ◽  
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.

scholarly journals Use of the first National Early Warning Score recorded within 24 hours of admission to estimate the risk of in-hospital mortality in unplanned COVID-19 patients: a retrospective cohort study

BMJ Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. e043721
Author(s):  
Donald Richardson ◽  
Muhammad Faisal ◽  
Massimo Fiori ◽  
Kevin Beatson ◽  
Mohammed Mohammed
Keyword(s):  
Hospital Mortality ◽  
Early Warning ◽  
Absolute Risk ◽  
Cross Sectional Study ◽  
Early Warning Score ◽  
Cross Sectional ◽  
Risk Of Mortality ◽  
Baseline Mortality ◽  
Acute Hospitals ◽  
National Early Warning Score

ObjectivesAlthough the National Early Warning Score (NEWS) and its latest version NEWS2 are recommended for monitoring deterioration in patients admitted to hospital, little is known about their performance in COVID-19 patients. We aimed to compare the performance of the NEWS and NEWS2 in patients with COVID-19 versus those without during the first phase of the pandemic.DesignA retrospective cross-sectional study.SettingTwo acute hospitals (Scarborough and York) are combined into a single dataset and analysed collectively.ParticipantsAdult (≥18 years) non-elective admissions discharged between 11 March 2020 and 13 June 2020 with an index or on-admission NEWS2 electronically recorded within ±24 hours of admission to predict mortality at four time points (in-hospital, 24 hours, 48 hours and 72 hours) in COVID-19 versus non-COVID-19 admissions.ResultsOut of 6480 non-elective admissions, 620 (9.6%) had a diagnosis of COVID-19. They were older (73.3 vs 67.7 years), more often male (54.7% vs 50.1%), had higher index NEWS (4 vs 2.5) and NEWS2 (4.6 vs 2.8) scores and higher in-hospital mortality (32.1% vs 5.8%). The c-statistics for predicting in-hospital mortality in COVID-19 admissions was significantly lower using NEWS (0.64 vs 0.74) or NEWS2 (0.64 vs 0.74), however, these differences reduced at 72hours (NEWS: 0.75 vs 0.81; NEWS2: 0.71 vs 0.81), 48 hours (NEWS: 0.78 vs 0.81; NEWS2: 0.76 vs 0.82) and 24hours (NEWS: 0.84 vs 0.84; NEWS2: 0.86 vs 0.84). Increasing NEWS2 values reflected increased mortality, but for any given value the absolute risk was on average 24% higher (eg, NEWS2=5: 36% vs 9%).ConclusionsThe index or on-admission NEWS and NEWS2 offers lower discrimination for COVID-19 admissions versus non-COVID-19 admissions. The index NEWS2 was not proven to be better than the index NEWS. For each value of the index NEWS/NEWS2, COVID-19 admissions had a substantially higher risk of mortality than non-COVID-19 admissions which reflects the increased baseline mortality risk of COVID-19.

scholarly journals National Early Warning Score 2 (NEWS2) better predicts critical Coronavirus Disease 2019 (COVID-19) illness than COVID-GRAM, a multi-centre study

Infection ◽  
2021 ◽  
Author(s):  
Giuseppe Vittorio De Socio ◽  
Anna Gidari ◽  
Francesco Sicari ◽  
Michele Palumbo ◽  
Daniela Francisci
Keyword(s):  
Early Warning ◽  
Operating Characteristic ◽  
Primary Outcome ◽  
Characteristic Curve ◽  
Early Warning Score ◽  
Multi Centre Study ◽  
Umbria Region ◽  
Chinese Cohort ◽  
National Early Warning Score ◽  
Specific Score

Abstract Purpose Clinical scores to rapidly assess the severity illness of Coronavirus Disease 2019 (COVID-19) could be considered of help for clinicians. Recently, a specific score (named COVID-GRAM) for severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection, based on a nationwide Chinese cohort, has been proposed. We routinely applied the National Early Warning Score 2 (NEWS2) to predict critical COVID-19. Aim of this study is to compare NEWS2 and COVID-GRAM score. Methods We retrospectively analysed data of 121 COVID-19 patients admitted in two Clinics of Infectious Diseases in the Umbria region, Italy. The primary outcome was critical COVID-19 illness defined as admission to the intensive care unit, invasive ventilation, or death. Accuracy of the scores was evaluated with the area under the receiver-operating characteristic curve (AUROC). Differences between scores were confirmed used Hanley–McNeil test. Results The NEWS2 AUROC curve measured 0.87 (standard error, SE 0.03; 95% CI 0.80–0.93; p < 0.0001). The COVID-GRAM score AUROC curve measured 0.77 (SE 0.04; 95% CI 0.68–0.85; p < 0.0001). Hanley–McNeil test showed that NEWS2 better predicted severe COVID-19 (Z = 2.03). Conclusions The NEWS2 showed superior accuracy to COVID-GRAM score for prediction of critical COVID-19 illness.

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