Skip to main content

Advertisement

SpringerLink
Log in

Epidemiology and patient predictors of infection and sepsis in the prehospital setting

  • Original
  • Published:
Intensive Care Medicine Aims and scope Submit manuscript

A Correction to this article was published on 30 June 2020

This article has been updated

Abstract

Purpose

Paramedics are often the first healthcare contact for patients with infection and sepsis and may identify them earlier with improved knowledge of the clinical signs and symptoms that identify patients at higher risk.

Methods

A 1-year (April 2015 and March 2016) cohort of all adult patients transported by EMS in the province of Alberta, Canada, was linked to hospital administrative databases. The main outcomes were infection, or sepsis diagnosis among patients with infection, in the Emergency Department. We estimated the probability of these outcomes, conditional on signs and symptoms that are commonly available to paramedics.

Results

Among 131,745 patients transported by EMS, the prevalence of infection was 9.7% and sepsis was 2.1%. The in-hospital mortality rate for patients with sepsis was 28%. The majority (62%) of patients with infections were classified by one of three dispatch categories (“breathing problems,” “sick patient,” or “inter-facility transfer”), and the probability of infection diagnosis was 17–20% for patients within these categories. Patients with elevated temperature measurements had the highest probability for infection diagnosis, but altered Glasgow Coma Scale (GCS), low blood pressure, or abnormal respiratory rate had the highest probability for sepsis diagnosis.

Conclusion

Dispatch categories and elevated temperature identify patients with higher probability of infection, but abnormal GCS, low blood pressure, and abnormal respiratory rate identify patients with infection who have a higher probability of sepsis. These characteristics may be considered by paramedics to identify higher-risk patients prior to arrival at the hospital.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Change history

  • 30 June 2020

    The original version of this article unfortunately contained a mistake.

References

  1. Singer M, Deutschman CS, Seymour CW et al (2016) The third international consensus definitions for sepsis and septic shock (sepsis-3). JAMA 315:801–810. https://doi.org/10.1001/jama.2016.0287

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Seventienth World Health Assembly (2017) Improving the prevention, diagnosis and clinical management of sepsis, pp 1–4

  3. Rhodes A, Evans LE, Alhazzani W et al (2017) Surviving sepsis campaign: international guidelines for management of sepsis and septic shock: 2016. Crit Care Med 45:486–552. https://doi.org/10.1097/CCM.0000000000002255

    Article  PubMed  Google Scholar 

  4. Wang HE, Weaver MD, Shapiro NI, Yealy DM (2010) Opportunities for emergency medical services care of sepsis. Resuscitation 81:193–197. https://doi.org/10.1016/j.resuscitation.2009.11.008

    Article  PubMed  Google Scholar 

  5. Groenewoudt M, Roest AA, Leijten FMM, Stassen PM (2014) Septic patients arriving with emergency medical services: a seriously ill population. Eur J Emerg Med 21:330–335. https://doi.org/10.1097/MEJ.0000000000000091

    Article  PubMed  Google Scholar 

  6. Gray A, Ward K, Lees F et al (2012) The epidemiology of adults with severe sepsis and septic shock in Scottish Emergency Departments. Emerg Med J 30:397–401. https://doi.org/10.1136/emermed-2012-201361

    Article  PubMed  Google Scholar 

  7. Herlitz J, Bång A, Wireklint-Sundström B et al (2012) Suspicion and treatment of severe sepsis. An overview of the prehospital chain of care. Scand J Trauma Resusc Emerg Med 20:42. https://doi.org/10.1186/1757-7241-20-42

    Article  PubMed  PubMed Central  Google Scholar 

  8. Alberta Health Services (AHS) Emergency Medical Services (EMS) (2014) AHS medical control protocols (MCP). http://www.protocols.ahsems.com. Accessed 11 Sep 2015

  9. Lane DJ, Blanchard IE, Cheskes S et al (2020) Strategy to identify paramedic transported sepsis cases in an Emergency Department Administrative Database. Prehosp Emerg Care 24:23–31. https://doi.org/10.1080/10903127.2019.1611978

    Article  PubMed  Google Scholar 

  10. Jolley RJ, Quan H, Jetté N et al (2015) Validation and optimisation of an ICD-10-coded case definition for sepsis using administrative health data. BMJ Open 5:e009487–11. https://doi.org/10.1136/bmjopen-2015-009487

    Article  Google Scholar 

  11. Shankar-Hari M, Phillips GS, Levy ML et al (2016) Developing a new definition and assessing new clinical criteria for septic shock: for the third international consensus definitions for sepsis and septic shock (sepsis-3). JAMA 315:775–787. https://doi.org/10.1001/jama.2016.0289

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Harrell F (2015) Regression modeling strategies. With applications to linear models, logistic and ordinal regression, and survival analysis. Springer, Cham. https://doi.org/10.1007/978-3-319-19425-7

    Book  Google Scholar 

  13. R Core Team (2016) R: a language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria. https://www.R-project.org/

  14. Yoshida K, Bartel A, Bohn J, Chipman JJ, McGowan LD, Barrett M, Christensen RHB, Gbouzill (2020) Tableone: create “Table 1” to describe baseline characteristics, version 0.11.1

  15. Wasey JO (2017) ICD: tools for working with ICD-9 and ICD-10 codes, and finding comorbidities

  16. von Elm E, Altman DG, Egger M et al (2007) The strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. PLoS Med 4:1623–1627. https://doi.org/10.1371/journal.pmed.0040296

    Article  Google Scholar 

  17. Benchimol EI, Smeeth L, Guttmann A et al (2015) The reporting of studies conducted using observational routinely-collected health data (RECORD) statement. PLoS Med 12:e1001885–22. https://doi.org/10.1371/journal.pmed.1001885

    Article  Google Scholar 

  18. Seymour CW, Rea TD, Kahn JM et al (2012) Severe sepsis in pre-hospital emergency care. Am J Respir Crit Care Med 186:1264–1271. https://doi.org/10.1164/rccm.201204-0713oc

    Article  PubMed  PubMed Central  Google Scholar 

  19. Seymour CW, Cooke CR, Heckbert SR et al (2014) Prehospital intravenous access and fluid resuscitation in severe sepsis: an observational cohort study. Crit Care 18:533. https://doi.org/10.1186/s13054-014-0533-x

    Article  PubMed  PubMed Central  Google Scholar 

  20. Sterling SA, Miller WR, Pryor J et al (2015) The impact of timing of antibiotics on outcomes in severe sepsis and septic shock: a systematic review and meta-analysis*. Crit Care Med 43:1907–1915. https://doi.org/10.1097/CCM.0000000000001142

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Sterling SA, Puskarich MA, Jones AE (2014) Prehospital treatment of sepsis: what really makes the “golden hour” golden? Crit Care 18:697. https://doi.org/10.1186/s13054-014-0697-4

    Article  PubMed  PubMed Central  Google Scholar 

  22. Seymour CW, Liu VX, Iwashyna TJ et al (2016) Assessment of clinical criteria for sepsis: for the third international consensus definitions for sepsis and septic shock (sepsis-3). JAMA 315:762–774. https://doi.org/10.1001/jama.2016.0288

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Lane DJ, Lin S, Scales DC (2019) Classification versus prediction of mortality risk using the SIRS and qSOFA scores in patients with infection transported by paramedics. Prehosp Emerg Care. https://doi.org/10.1080/10903127.2019.1624901

    Article  PubMed  Google Scholar 

  24. Müller B, Becker KL, Schächinger H et al (2000) Calcitonin precursors are reliable markers of sepsis in a medical intensive care unit. Crit Care Med 28:977–983. https://doi.org/10.1097/00003246-200004000-00011

    Article  PubMed  Google Scholar 

  25. Trzeciak S, Dellinger RP, Chansky ME et al (2007) Serum lactate as a predictor of mortality in patients with infection. Intensive Care Med 33:970–977. https://doi.org/10.1007/s00134-007-0563-9

    Article  CAS  PubMed  Google Scholar 

  26. Sierra R, Rello J, Bailén MAA et al (2004) C-reactive protein used as an early indicator of infection in patients with systemic inflammatory response syndrome. Intensive Care Med 30:2038–2045. https://doi.org/10.1007/s00134-004-2434-y

    Article  PubMed  Google Scholar 

  27. Komorowski M (2020) Clinical management of sepsis can be improved by artificial intelligence: yes. Intensive Care Med. https://doi.org/10.1007/s00134-019-05898-2

    Article  PubMed  Google Scholar 

  28. Wallgren UM, Castrén M, Svensson AEV, Kurland L (2014) Identification of adult septic patients in the prehospital setting. Eur J Emerg Med 21:260–265. https://doi.org/10.1097/MEJ.0000000000000084

    Article  PubMed  Google Scholar 

  29. Jouffroy R, Saade A, Ellouze S et al (2017) Prehospital triage of septic patients at the SAMU regulation: comparison of qSOFA, MRST, MEWS and PRESEP scores. Am J Emerg Med. https://doi.org/10.1016/j.ajem.2017.10.030

    Article  PubMed  Google Scholar 

  30. Lane Daniel J, Hannah Wunsch, Refik Saskin, Sheldon Cheskes, Steve Lin, Morrison Laurie J, Scales Damon C (2020) Screening strategies to identify sepsis in the prehospital setting: a validation study. CMAJ 192(10):E230–E239. https://doi.org/10.1503/cmaj.190966

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Coopersmith CM, De Backer D, Deutschman CS et al (2018) Surviving sepsis campaign: research priorities for sepsis and septic shock. Intensive Care Med 44:1400–1426

    Article  PubMed  PubMed Central  Google Scholar 

  32. Seymour CW, Kahn JM, Cooke CR et al (2010) Prediction of critical illness during out-of-hospital emergency care. JAMA 304:747–754. https://doi.org/10.1001/jama.2010.1140

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Royal College of Physicians (2015) National early warning score (NEWS). https://www.rcplondon.ac.uk/projects/outputs/national-early-warning-score-news. Accessed 28 Sep 2017

  34. Lane DJ, Wunsch H, Saskin R et al (2019) Assessing severity of illness in patients transported to hospital by paramedics: external validation of 3 prognostic scores. Prehosp Emerg Care. https://doi.org/10.1080/10903127.2019.1632998

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Health Sciences Building, 155 College Street, Suite 425, Toronto, ON, M5T 3M6, Canada

    Daniel J. Lane, Hannah Wunsch, Refik Saskin, Steve Lin & Damon C. Scales

  2. Rescu, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, ON, Canada

    Daniel J. Lane, Sheldon Cheskes, Steve Lin, Laurie J. Morrison & Damon C. Scales

  3. Department of Critical Care Medicine, Sunnybrook Health Sciences Centre, Toronto, ON, Canada

    Hannah Wunsch & Damon C. Scales

  4. Interdepartmental Division of Critical Care, University of Toronto, Toronto, ON, Canada

    Hannah Wunsch & Damon C. Scales

  5. Department of Anesthesia, University of Toronto, Toronto, ON, Canada

    Hannah Wunsch

  6. Division of Emergency Medicine, Department of Family and Community Medicine, University of Toronto, Toronto, ON, Canada

    Sheldon Cheskes

  7. Sunnybrook Centre for Prehospital Medicine, Sunnybrook Health Sciences Centre, Toronto, ON, Canada

    Sheldon Cheskes

  8. Division of Emergency Medicine, Department of Medicine, University of Toronto, Toronto, ON, Canada

    Steve Lin & Laurie J. Morrison

  9. Cumming School of Medicine, University of Calgary, Calgary, AB, Canada

    Christopher J. Oleynick

Authors
  1. Daniel J. Lane
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hannah Wunsch
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Refik Saskin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sheldon Cheskes
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Steve Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Laurie J. Morrison
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Christopher J. Oleynick
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Damon C. Scales
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Daniel J. Lane.

Ethics declarations

Conflicts of interest

The authors have no conflicts of interest to declare related to this manuscript.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 17684 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lane, D.J., Wunsch, H., Saskin, R. et al. Epidemiology and patient predictors of infection and sepsis in the prehospital setting. Intensive Care Med 46, 1394–1403 (2020). https://doi.org/10.1007/s00134-020-06093-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00134-020-06093-4

Keywords

Search

Navigation