Performance of 21 Early Warning System scores in predicting in-hospital deterioration among undifferentiated admitted patients managed by ambulance services (2024)

Performance of 21 Early Warning System scores in predicting in-hospital deterioration among undifferentiated admitted patients managed by ambulance services (1)

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Original research

Performance of 21 Early Warning System scores in predicting in-hospital deterioration among undifferentiated admitted patients managed by ambulance services

  1. http://orcid.org/0000-0003-1575-6794Gigi Guan1,2,
  2. Crystal Man Ying Lee3,
  3. Stephen Begg4,
  4. Angela Crombie5,
  5. George Mnatzaganian1
  1. 1Rural Department of Community Health, La Trobe University, Bendigo, Victoria, Australia
  2. 2Department of Rural Health, The University of Melbourne, Melbourne, Victoria, Australia
  3. 3School of Population Health, Curtin University, Perth, Western Australia, Australia
  4. 4Violet Vines Marshman Centre for Rural Health Research, La Trobe University, Bendigo, Victoria, Australia
  5. 5Research & Innovation, Bendigo Health, Bendigo, Victoria, Australia
  1. Correspondence to Mr Gigi Guan, Rural Department of Community Health, La Trobe University, Bendigo, Victoria, Australia; gigiaus{at}gmail.com

Abstract

Background The optimal Early Warning System (EWS) scores for identifying patients at risk of clinical deterioration among those transported by ambulance services remain uncertain. This retrospective study compared the performance of 21 EWS scores to predict clinical deterioration using vital signs (VS) measured in the prehospital or emergency department (ED) setting.

Methods Adult patients transported to a single ED by ambulances and subsequently admitted to the hospital between 1 January 2019 and 18 April 2019 were eligible for inclusion. The primary outcome was 30-day mortality; secondary outcomes included 3-day mortality, admission to intensive care or coronary care units, length of hospital stay and emergency call activations. The discriminative ability of the EWS scores was assessed using the area under the receiver operating characteristic curve (AUROC). Subanalyses compared the performance of EWS scores between surgical and medical patient types.

Results Of 1414 patients, 995 (70.4%) (53.1% male, mean age 68.7±17.5 years) were included. In the ED setting, 30-day mortality was best predicted by VitalPAC EWS (AUROC 0.71, 95% CI (0.65 to 0.77)) and National Early Warning Score (0.709 (0.65 to 0.77)). All EWS scores calculated in the prehospital setting had AUROC <0.70. Rapid Emergency Medicine Score (0.83 (0.73 to 0.92)) and New Zealand EWS (0.88 (0.81 to 0.95)) best predicted 3-day mortality in the prehospital and ED settings, respectively. EWS scores calculated using either prehospital or ED VS were more effective in predicting 3-day mortality in surgical patients, whereas 30-day mortality was best predicted in medical patients. Among the EWS scores that achieved AUROC ≥0.70, no statistically significant differences were detected in their discriminatory abilities to identify patients at risk of clinical deterioration.

Conclusions EWS scores better predict 3-day as opposed to 30-day mortality and are more accurate when estimated using VS measured in the ED. The discriminatory performance of EWS scores in identifying patients at higher risk of clinical deterioration may vary by patient type.

  • emergency department
  • acute care
  • assessment
  • diagnosis
  • pre-hospital care

Data availability statement

No data are available.

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    • emergency department
    • acute care
    • assessment
    • diagnosis
    • pre-hospital care

    Data availability statement

    No data are available.

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    Footnotes

    • Handling editor Kirsty Challen

    • Contributors GG—conceptualisation, data curation, formal analysis, methodology, validation, writing (original draft) and writing (review and editing), research guarantor. CMYL—conceptualisation, validation and writing (review and editing). SB—writing (review and editing). AC—data curation and writing (review and editing). GM—conceptualisation, data curation, formal analysis, methodology, validation, writing (original draft) and writing (review and editing).

    • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

    • Competing interests None declared.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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    Performance of 21 Early Warning System scores in predicting in-hospital deterioration among undifferentiated admitted patients managed by ambulance services (2024)

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