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dc.contributor.authorFaisal, Muhammad
dc.contributor.authorMohammed, A. Mohammed
dc.contributor.authorRichardson, D.
dc.contributor.authorSteyerberg, E.W.
dc.contributor.authorFiori, M.
dc.contributor.authorBeatson, K.
dc.date.accessioned2021-09-15T15:10:54Z
dc.date.accessioned2021-10-04T09:36:59Z
dc.date.available2021-09-15T15:10:54Z
dc.date.available2021-10-04T09:36:59Z
dc.identifier.citationFaisal M, Mohammed AM, Richardson D et al (2021) 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. BMC Health Services Research. 21(1): 957.
dc.identifier.urihttp://hdl.handle.net/10454/18599
dc.descriptionYes
dc.description.abstractThe 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. 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. 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. 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.
dc.description.sponsorshipThe Health Foundation (Award No 7380) and the National Institute for Health Research (NIHR) Yorkshire and Humber Patient Safety Translational Research Centre (NIHR Yorkshire and Humber PSTRC) (Award No PSTRC-2016-006)
dc.language.isoenen
dc.rights(c) 2021 The Authors. This is an Open Access article distributed under the Creative Commons CC-BY license (http://creativecommons.org/licenses/by/4.0/)
dc.subjectResearch Development Fund Publication Prize Award
dc.subjectNational early warning score
dc.subjectCovid-19
dc.subjectHospital admissions
dc.titlePredictive 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
dc.status.refereedYes
dc.typeArticle
dc.type.versionPublished version
dc.description.publicnotesResearch Development Fund Publication Prize Award winner, Aug 2021.
dc.identifier.doihttps://doi.org/10.1186/s12913-021-06951-x
dc.rights.licenseCC-BY
dc.date.updated2021-09-15T15:11:03Z
refterms.dateFOA2021-10-04T09:38:04Z
dc.openaccess.statusopenAccess
dc.date.accepted2021-08-27


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