The Surviving Sepsis Campaign Bundle: 2018 Update
Abstract
INTRODUCTION The “sepsis bundle” has been central to the implementation of the Surviving Sepsis Campaign (SSC) from the first publication of its evidence-based guidelines in 2004 through subsequent editions (1–6). Developed separately from the guidelines publication by the SSC, the bundles have been the cornerstone of sepsis quality improvement since 2005 (7–11). As noted when they were introduced, the bundle elements were designed to be updated as indicated by new evidence and have evolved accordingly. In response to the publication of “Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock: 2016” (12,13), a revised “hour-1 bundle” has been developed and is presented below (Fig. 1).Figure 1.: Hour-1 Surviving Sepsis Campaign Bundle of Care.*The compelling nature of the evidence in the literature, which has demonstrated an association between compliance with bundles and improved survival in patients with sepsis and septic shock, led to the adoption of the SSC measures by the National Quality Forum (NQF) and subsequently both by the New York State (NYS) Department of Health (14) and the Centers for Medicare and Medicaid Services (CMS) (15) in the USA for mandated public reporting. The important relationship between the bundles and survival was confirmed in a publication from this NYS initiative (16). Paramount in the management of patients with sepsis is the concept that sepsis is a medical emergency. As with polytrauma, acute myocardial…
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Implementing standardized bundles can help ensure timely and proper sepsis care and has been associated with reductions in mortality.
“Implementing standardized bundles can help ensure timely and proper sepsis care and has been associated with reductions in mortality [11-13].”
From Integrating a Machine Learning System Into Clinical Workflows: Qualitative Study (2020) · cites this paper as [12]
The citation lists haven’t been fetched yet
References 46 · Cited by 679
- Authors
- Levy, M. M., Evans, L. E., & Rhodes, A.
- Affiliations
- Brown University (US)
- New York University (US)
- St George's, University of London (GB)
- Venue
- Critical Care Medicine
- Published
- 2018-06
- Volume
- 46
- Issue
- 6
- Pages
- 997-1000
- ISSN
- 0090-3493 (ISSN-L)
- OpenAlex
- W2913544880
- Cited by
- 679
- References
- 46
- Citations by year
- 2026: 32 · 2025: 43 · 2024: 55 · 2023: 59 · 2022: 80 · 2021: 123 · 2020: 142 · 2019: 116 · 2018: 28 · 2017: 1
- FWCI
- 79.96
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Critical Care Medicine (publishedVersion)
- PubMed (publishedVersion)
- St George's Online Research Archive (St George's University of London) (acceptedVersion)
- Topics
- Health Sciences › Medicine › Epidemiology › Sepsis Diagnosis and Treatment
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Abdominal Surgery and Complications
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Hyperglycemia and glycemic control in critically ill and hospitalized patients
- Keywords
- sepsis, septic shock, Surviving Sepsis Campaign, quality improvement, fluid resuscitation, source control, hemodynamic monitoring, emergency department care, public reporting, sepsis mortality, guideline implementation, Centers for Medicare and Medicaid Services, National Quality Forum, sepsis education
- MeSH terms
- Anti-Bacterial Agents, Fluid Therapy, Health Promotion, Humans, Microbial Sensitivity Tests, Vasoconstrictor Agents, Sepsis, Lactic Acid, Patient Care Bundles
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review