Cited by 29Sepsis Diagnosis and TreatmentInflammation biomarkers and pathwaysAcute Kidney Injury Research

Comparison of Sepsis Definitions as Automated Criteria

Yu, Sean C., Betthauser, Kevin D., Gupta, Aditi, Lyons, Patrick G., Lai, Albert M., Kollef, Marin H., Payne, Philip R. O., Michelson, Andrew P.·2021·Critical Care Medicine

Abstract

Objectives Assess the impact of heterogeneity among established sepsis criteria (Sepsis-1, Sepsis-3, Centers for Disease Control and Prevention Adult Sepsis Event, and Centers for Medicare and Medicaid severe sepsis core measure 1) through the comparison of corresponding sepsis cohorts. Design Retrospective analysis of data extracted from electronic health record. Setting Single, tertiary-care center in St. Louis, MO. Patients Adult, nonsurgical inpatients admitted between January 1, 2012, and January 6, 2018. Interventions None. Measurements and main results In the electronic health record data, 286,759 encounters met inclusion criteria across the study period. Application of established sepsis criteria yielded cohorts varying in prevalence: Centers for Disease Control and Prevention Adult Sepsis Event (4.4%), Centers for Medicare and Medicaid severe sepsis core measure 1 (4.8%), International Classification of Disease code (7.2%), Sepsis-3 (7.5%), and Sepsis-1 (11.3%). Between the two modern established criteria, Sepsis-3 (n = 21,550) and Centers for Disease Control and Prevention Adult Sepsis Event (n = 12,494), the size of the overlap was 7,763. The sepsis cohorts also varied in time from admission to sepsis onset (hr): Sepsis-1 (2.9), Sepsis-3 (4.1), Centers for Disease Control and Prevention Adult Sepsis Event (4.6), and Centers for Medicare and Medicaid severe sepsis core measure 1 (7.6); sepsis discharge International Classification of Disease code rate: Sepsis-1 (37.…

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Crossref · 10.1097/ccm.0000000000004875

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  • Imprecise retrospective definitions unavailable in real time, such as billing codes, may complicate the validity of outcome labels.

    “Imprecise retrospective definitions unavailable in real time, such as billing codes, may complicate the validity of outcome labels [38, 39].”

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References 31 · Cited by 29

Authors
Yu, S. C., Betthauser, K. D., Gupta, A., Lyons, P. G., Lai, A. M., Kollef, M. H., Payne, P. R. O., & Michelson, A. P.
Affiliations
  • Washington University in St. Louis (US)
  • Barnes-Jewish Hospital (US)
  • BJC HealthCare (US)
Venue
Critical Care Medicine
Published
2021-02-12
Volume
49
Issue
4
Pages
e433-e443
ISSN
0090-3493 (ISSN-L)
OpenAlex
W3129441165
Cited by
29
References
31
Citations by year
2026: 3 · 2025: 8 · 2024: 2 · 2023: 3 · 2022: 9 · 2021: 4
FWCI
3.24
Type
journal article
Language
en
Open access
closed
Locations
  • Critical Care Medicine (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Epidemiology › Sepsis Diagnosis and Treatment
  • Life Sciences › Immunology and Microbiology › Immunology › Inflammation biomarkers and pathways
  • Health Sciences › Medicine › Nephrology › Acute Kidney Injury Research
Keywords
sepsis definitions, Sepsis-3, sepsis prevalence, in-hospital mortality, electronic health record data, hospitalized adults
MeSH terms
Humans, Retrospective Studies, Severity of Illness Index, Shock, Septic, United States, Databases, Factual, Outcome Assessment, Health Care, Sepsis, International Classification of Diseases

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