Evidence map›Paper›PMID 34511131›Full record

ArticleBMC health services research2021

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.

Muhammad Faisal, Mohammed Amin Mohammed, Donald Richardson, Ewout W Steyerberg, Massimo Fiori, Kevin Beatson

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Article in BMC health services research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2 citing papers in PubMed.

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6 authors.

Muhammad FaisalFaculty of Health Studies, University of Bradford, Bradford, UK.
Mohammed Amin MohammedFaculty of Health Studies, University of Bradford, Bradford, UK. M.A.Mohammed5@Bradford.ac.uk.
Donald RichardsonDepartment of Renal Medicine, York Teaching Hospitals NHS Foundation Trust, York, England, UK.
Ewout W SteyerbergDepartment of Public Health, Erasmus University, Rotterdam, The Netherlands.
Massimo FioriYork Teaching Hospitals NHS Foundation Trust, York, England, UK.
Kevin BeatsonYork Teaching Hospitals NHS Foundation Trust, York, England, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe 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.

methodsOur 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.

resultsThe 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.

conclusionsModel 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.

Indexed as

COVID-19Early Warning ScoreAdultHospitalsHumansPatient AdmissionRetrospective StudiesSARS-CoV-2Computer-aided national early warning scoreEmergency admissionNational early warning scoreNovel coronavirus SARS-19Vital signs

Identifiers

PMID34511131
PMCPMC8435351

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