Literature DB >> 32468084

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

Daniel J Lane1,2, Hannah Wunsch3,4,5,6, Refik Saskin3, Sheldon Cheskes7,8,9, Steve Lin3,7,10, Laurie J Morrison7,10, Christopher J Oleynick11, Damon C Scales3,7,4,5.   

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.

Entities:  

Keywords:  Emergency; Emergency medical services; Infection; Predictors; Sepsis

Mesh:

Year:  2020        PMID: 32468084     DOI: 10.1007/s00134-020-06093-4

Source DB:  PubMed          Journal:  Intensive Care Med        ISSN: 0342-4642            Impact factor:   17.440


  2 in total

1.  Interpretable Machine Learning for Early Prediction of Prognosis in Sepsis: A Discovery and Validation Study.

Authors:  Chang Hu; Lu Li; Weipeng Huang; Tong Wu; Qiancheng Xu; Juan Liu; Bo Hu
Journal:  Infect Dis Ther       Date:  2022-04-10

2.  A nomogram to predict prolonged stay of obesity patients with sepsis in ICU: Relevancy for predictive, personalized, preventive, and participatory healthcare strategies.

Authors:  Yang Chen; Mengdi Luo; Yuan Cheng; Yu Huang; Qing He
Journal:  Front Public Health       Date:  2022-08-11
  2 in total

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