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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Acta Anaesthesiologi...arrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Acta Anaesthesiologica Scandinavica
Article . 2019 . Peer-reviewed
License: Wiley Online Library User Agreement
Data sources: Crossref
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Research.fi
Article . 2020 . Peer-reviewed
Data sources: Research.fi
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Prehospital National Early Warning Score predicts early mortality

Authors: Nurmi Jouni; Pirneskoski Jussi; Olkkola Klaus T.; Kuisma Markku; Nurmi Jouni; Pirneskoski Jussi; Olkkola Klaus T.; +1 Authors

Prehospital National Early Warning Score predicts early mortality

Abstract

BackgroundNational Early Warning Score (NEWS) has been shown to be the best early warning score to predict in‐hospital mortality but there is limited information on its predictive value in a prehospital setting. The aim of the current study was to investigate the diagnostic accuracy of NEWS in a prehospital setting using large population‐based databases in terms of short‐term mortality.MethodsWe calculated the NEWS scores from retrospective prehospital electronic patient record data and analysed their possible relationship to mortality. We included all patient records for patients 18 years or older with sufficient prehospital data to calculate NEWS from 17 August 2008 to 18 December 2015 encountered by the emergency medical services (EMS) in the Hospital District of Helsinki and Uusimaa, Finland. The primary outcome measure was death within 1 day of EMS dispatch.Results35 800 patients were included. Their mean (SD) age was 65.8 (19.9) years. The median value of NEWS was 3 (IQR 1‐6). The primary outcome of death within 1 day of EMS dispatch occurred in 378 (1.1%) cases. Area under receiver operating characteristic curve (AUROC) for primary outcome of death within 1 day was 0.840 (95% CI 0.823‐0.858). AUROC for 1 day mortality in trauma subgroup was 0.901 (95% CI 0.859‐0.942).ConclusionPrehospital NEWS predicts mortality within 1 day of EMS dispatch with good diagnostic accuracy.

Keywords

Adult, Aged, 80 and over, Male, Emergency Medical Services, Middle Aged, Logistic Models, ROC Curve, Early Warning Score, Outcome Assessment, Health Care, Humans, Female, Hospital Mortality, Aged, Retrospective Studies

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
52
Top 10%
Top 10%
Top 1%
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