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Sepsis Alerts in Emergency Departments: A Systematic Review of Accuracy and Quality Measure Impact.
Hwang, Matthew I; Bond, William F; Powell, Emilie S.
  • Hwang MI; University of Illinois College of Medicine at Peoria, Peoria, Illinois.
  • Bond WF; University of Illinois College of Medicine at Peoria, OSF HealthCare, Jump Simulation and Department of Emergency Medicine, Peoria, Illinois.
  • Powell ES; Northwestern University Feinberg School of Medicine, Northwestern Memorial Hospital, Department of Emergency Medicine, Chicago, Illinois.
West J Emerg Med ; 21(5): 1201-1210, 2020 Aug 24.
Article in English | MEDLINE | ID: covidwho-1456475
ABSTRACT

INTRODUCTION:

For early detection of sepsis, automated systems within the electronic health record have evolved to alert emergency department (ED) personnel to the possibility of sepsis, and in some cases link them to suggested care pathways. We conducted a systematic review of automated sepsis-alert detection systems in the ED.

METHODS:

We searched multiple health literature databases from the earliest available dates to August 2018. Articles were screened based on abstract, again via manuscript, and further narrowed with set inclusion criteria 1) adult patients in the ED diagnosed with sepsis, severe sepsis, or septic shock; 2) an electronic system that alerts a healthcare provider of sepsis in real or near-real time; and 3) measures of diagnostic accuracy or quality of sepsis alerts. The final, detailed review was guided by QUADAS-2 and GRADE criteria. We tracked all articles using an online tool (Covidence), and the review was registered with PROSPERO registry of reviews. A two-author consensus was reached at the article choice stage and final review stage. Due to the variation in alert criteria and methods of sepsis diagnosis confirmation, the data were not combined for meta-analysis.

RESULTS:

We screened 693 articles by title and abstract and 20 by full text; we then selected 10 for the study. The articles were published between 2009-2018. Two studies had algorithm-based alert systems, while eight had rule-based alert systems. All systems used different criteria based on systemic inflammatory response syndrome (SIRS) to define sepsis. Sensitivities ranged from 10-100%, specificities from 78-99%, and positive predictive value from 5.8-54%. Negative predictive value was consistently high at 99-100%. Studies showed some evidence for improved process-of-care markers, including improved time to antibiotics. Length of stay improved in two studies. One low quality study showed improved mortality.

CONCLUSION:

The limited evidence available suggests that sepsis alerts in the ED setting can be set to high sensitivity. No high-quality studies showed a difference in mortality, but evidence exists for improvements in process of care. Significant further work is needed to understand the consequences of alert fatigue and sensitivity set points.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: Sepsis / Decision Support Systems, Clinical / Early Diagnosis / Emergency Service, Hospital Type of study: Diagnostic study / Experimental Studies / Prognostic study / Qualitative research / Reviews / Systematic review/Meta Analysis Limits: Humans Language: English Journal: West J Emerg Med Year: 2020 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Sepsis / Decision Support Systems, Clinical / Early Diagnosis / Emergency Service, Hospital Type of study: Diagnostic study / Experimental Studies / Prognostic study / Qualitative research / Reviews / Systematic review/Meta Analysis Limits: Humans Language: English Journal: West J Emerg Med Year: 2020 Document Type: Article