Literature DB >> 30623422

Prehospital National Early Warning Score predicts early mortality.

Jussi Pirneskoski1,2, Markku Kuisma2, Klaus T Olkkola1, Jouni Nurmi2.   

Abstract

BACKGROUND: National 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.
METHODS: We 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.
RESULTS: 35 800 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).
CONCLUSION: Prehospital NEWS predicts mortality within 1 day of EMS dispatch with good diagnostic accuracy.
© 2019 The Acta Anaesthesiologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.

Entities:  

Mesh:

Year:  2019        PMID: 30623422     DOI: 10.1111/aas.13310

Source DB:  PubMed          Journal:  Acta Anaesthesiol Scand        ISSN: 0001-5172            Impact factor:   2.105


  16 in total

1.  Predictive value of the New Zealand Early Warning Score for early mortality in low-acuity patients discharged at scene by paramedics: an observational study.

Authors:  Verity Frances Todd; Melanie Moylan; Graham Howie; Andy Swain; Aroha Brett; Tony Smith; Bridget Dicker
Journal:  BMJ Open       Date:  2022-07-14       Impact factor: 3.006

Review 2.  Can Prehospital Data Improve Early Identification of Sepsis in Emergency Department? An Integrative Review of Machine Learning Approaches.

Authors:  Manushi D Desai; Mohammad S Tootooni; Kathleen L Bobay
Journal:  Appl Clin Inform       Date:  2022-02-02       Impact factor: 2.342

3.  Prehospital Point-Of-Care Lactate Increases the Prognostic Accuracy of National Early Warning Score 2 for Early Risk Stratification of Mortality: Results of a Multicenter, Observational Study.

Authors:  Francisco Martín-Rodríguez; Raúl López-Izquierdo; Juan F Delgado Benito; Ancor Sanz-García; Carlos Del Pozo Vegas; Miguel Ángel Castro Villamor; José Luis Martín-Conty; Guillermo J Ortega
Journal:  J Clin Med       Date:  2020-04-18       Impact factor: 4.241

4.  Changing role of EMS -analyses of non-conveyed and conveyed patients in Finland.

Authors:  Jani Paulin; Jouni Kurola; Sanna Salanterä; Hans Moen; Nischal Guragain; Mari Koivisto; Niina Käyhkö; Venla Aaltonen; Timo Iirola
Journal:  Scand J Trauma Resusc Emerg Med       Date:  2020-05-29       Impact factor: 2.953

5.  Association between National Early Warning Scores in primary care and clinical outcomes: an observational study in UK primary and secondary care.

Authors:  Lauren J Scott; Niamh M Redmond; Alison Tavaré; Hannah Little; Seema Srivastava; Anne Pullyblank
Journal:  Br J Gen Pract       Date:  2020-05-28       Impact factor: 5.386

6.  Random forest machine learning method outperforms prehospital National Early Warning Score for predicting one-day mortality: A retrospective study.

Authors:  Jussi Pirneskoski; Joonas Tamminen; Antti Kallonen; Jouni Nurmi; Markku Kuisma; Klaus T Olkkola; Sanna Hoppu
Journal:  Resusc Plus       Date:  2020-12-05

7.  Pre-hospital triage performance and emergency medical services nurse's field assessment in an unselected patient population attended to by the emergency medical services: a prospective observational study.

Authors:  Carl Magnusson; Johan Herlitz; Christer Axelsson
Journal:  Scand J Trauma Resusc Emerg Med       Date:  2020-08-17       Impact factor: 2.953

8.  Implementation of the National Early Warning Score in patients with suspicion of sepsis: evaluation of a system-wide quality improvement project.

Authors:  Anne Pullyblank; Alison Tavaré; Hannah Little; Emma Redfern; Hein le Roux; Matthew Inada-Kim; Kate Cheema; Adam Cook
Journal:  Br J Gen Pract       Date:  2020-05-28       Impact factor: 5.386

9.  A validation of machine learning-based risk scores in the prehospital setting.

Authors:  Douglas Spangler; Thomas Hermansson; David Smekal; Hans Blomberg
Journal:  PLoS One       Date:  2019-12-13       Impact factor: 3.240

10.  Machine learning model predicts short-term mortality among prehospital patients: A prospective development study from Finland.

Authors:  Joonas Tamminen; Antti Kallonen; Sanna Hoppu; Jari Kalliomäki
Journal:  Resusc Plus       Date:  2021-02-05
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.