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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.
Faisal, Muhammad; Mohammed, Mohammed Amin; Richardson, Donald; Steyerberg, Ewout W; Fiori, Massimo; Beatson, Kevin.
  • Faisal M; Faculty of Health Studies, University of Bradford, Bradford, UK.
  • Mohammed MA; Bradford Institute for Health Research , Bradford, UK.
  • Richardson D; NIHR Yorkshire and Humber Patient Safety Translational Research Centre (YHPSTRC), Bradford, UK.
  • Steyerberg EW; Wolfson Centre for Applied Health Research, Bradford, UK.
  • Fiori M; Faculty of Health Studies, University of Bradford, Bradford, UK. M.A.Mohammed5@Bradford.ac.uk.
  • Beatson K; The Strategy Unit, NHS Midlands and Lancashire Commissioning Support Unit, Kingston House, B70 9LD, West Bromwich, UK. M.A.Mohammed5@Bradford.ac.uk.
BMC Health Serv Res ; 21(1): 957, 2021 Sep 13.
Article in English | MEDLINE | ID: covidwho-1405306
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Early Warning Score / COVID-19 Type of study: Cohort study / Experimental Studies / Observational study / Prognostic study Limits: Adult / Humans Language: English Journal: BMC Health Serv Res Journal subject: Health Services Research Year: 2021 Document Type: Article Affiliation country: S12913-021-06951-x

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Early Warning Score / COVID-19 Type of study: Cohort study / Experimental Studies / Observational study / Prognostic study Limits: Adult / Humans Language: English Journal: BMC Health Serv Res Journal subject: Health Services Research Year: 2021 Document Type: Article Affiliation country: S12913-021-06951-x