Implementation of a bundle of quality indicators for the early management of severe sepsis and septic shock is associated with decreased mortality*
Abstract
OBJECTIVE: The purpose of this study was to examine the outcome implications of implementing a severe sepsis bundle in an emergency department as a quality indicator set with feedback to modify physician behavior related to the early management of severe sepsis and septic shock. DESIGN: Two-year prospective observational cohort. SETTING: Academic tertiary care facility. PATIENTS: Patients were 330 patients presenting to the emergency department who met criteria for severe sepsis or septic shock. INTERVENTIONS: Five quality indicators comprised the bundle for severe sepsis management in the emergency department: a) initiate central venous pressure (CVP)/central venous oxygen saturation (Scvo2) monitoring within 2 hrs; b) give broad-spectrum antibiotics within 4 hrs; c) complete early goal-directed therapy at 6 hrs; d) give corticosteroid if the patient is on vasopressor or if adrenal insufficiency is suspected; and e) monitor for lactate clearance. MEASUREMENTS AND MAIN RESULTS: Patients had a mean age of 63.8 +/- 18.5 yrs, Acute Physiology and Chronic Health Evaluation II score 29.6 +/- 10.6, emergency department length of stay 8.5 +/- 4.4 hrs, hospital length of stay 11.3 +/- 12.9 days, and in-hospital mortality 35.2%. Bundle compliance increased from zero to 51.2% at the end of the study period. During the emergency department stay, patients with the bundle completed received more CVP/Scvo2 monitoring (100.0 vs. 64.8%, p < .01), more antibiotics (100.0 vs. 89.7%, p = .04),…
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?
Early detection and treatment of sepsis is essential to decrease patient mortality.
“Early detection and treatment of sepsis is essential to decrease patient mortality [10].”
From Integrating a Machine Learning System Into Clinical Workflows: Qualitative Study (2020) · cites this paper as [10]
Cites
Kumar, A., Roberts, D., Wood, K. E., Light, B., Parrillo, J. E., Sharma, S., Suppes, R., Feinstein, D., Zanotti, S., Taiberg, L., et al. (2006) · Critical Care Medicine · doi:10.1097/01.ccm.0000217961.75225.e9 · Cited by 6,109 · see citing works
Annane, D. (2000) · JAMA · doi:10.1001/jama.283.8.1038 · Cited by 1,000 · cited 1× · see citing works
“Thus, we do not know how many patients receiving corticosteroid in our study actually have relative adrenal insufficiency and are the patients with the most benefit from corticosteroid administration (9, 31).”
Background
Flagged first, then by citations
Cited by
Sorted by citations
Surviving Sepsis Campaign: International Guidelines for Management of Severe Sepsis and Septic Shock
Dellinger, R. P., Levy, M. M., Rhodes, A., Annane, D., Gerlach, H., Opal, S. M., Sevransky, J. E., Sprung, C. L., Douglas, I. S., Jaeschke, R., et al. (2013) · Critical Care Medicine · doi:10.1097/ccm.0b013e31827e83af · Cited by 9,694 · see citing works
Levy, M. M., Dellinger, R. P., Townsend, S. R., Linde-Zwirble, W. T., Marshall, J. C., Bion, J., Schorr, C., Artigas, A., Ramsay, G., Beale, R., et al. (2010) · Intensive Care Medicine · doi:10.1007/s00134-009-1738-3 · Cited by 868 · see citing works
A targeted real-time early warning score (TREWScore) for septic shock.
Henry, K. E., Hager, D. N., Pronovost, P. J., & Saria, S. (2015) · Science Translational Medicine · doi:10.1126/scitranslmed.aab3719 · Cited by 598 · see citing works
Shimabukuro, D. W., Barton, C. W., Feldman, M. D., Mataraso, S. J., & Das, R. (2017) · BMJ Open Respiratory Research · doi:10.1136/bmjresp-2017-000234 · Cited by 382 · see citing works
Prediction of sepsis patients using machine learning approach: a meta-analysis
Islam, M. M., Nasrin, T., Walther, B. A., Wu, C.-C., Yang, H.-C., & Li, Y.-C. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.12.027 · Cited by 259 · see citing works
Damiani, E., Donati, A., Serafini, G., Rinaldi, L., Adrario, E., Pelaia, P., Busani, S., & Girardis, M. (2015) · PLOS ONE · doi:10.1371/journal.pone.0125827 · Cited by 275 · see citing works
Baelani, I., Jochberger, S., Laimer, T., Otieno, D., Kabutu, J., Wilson, I., Baker, T., & Dünser, M. W. (2011) · Critical Care · doi:10.1186/cc9410 · Cited by 251 · see citing works
A computational approach to early sepsis detection
Calvert, J. S., Price, D. A., Chettipally, U. K., Barton, C. W., Feldman, M. D., Hoffman, J. L., Jay, M., & Das, R. (2016) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2016.05.003 · Cited by 244 · see citing works
Barton, C., Chettipally, U., Zhou, Y., Jiang, Z., Lynn-Palevsky, A., Le, S., Calvert, J., & Das, R. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.04.027 · Cited by 175 · see citing works
Integrating a Machine Learning System Into Clinical Workflows: Qualitative Study
Sandhu, S., Lin, A. L., Brajer, N., Sperling, J., Ratliff, W., Bedoya, A. D., Balu, S., O'Brien, C., & Sendak, M. P. (2020) · Journal of Medical Internet Research · doi:10.2196/22421 · Cited by 168 · see citing works
McCoy, A., & Das, R. (2017) · BMJ Open Quality · doi:10.1136/bmjoq-2017-000158 · Cited by 148 · see citing works
Pontes-Arruda, A., Martins, L. F., de Lima, S. M., Isola, A. M., Toledo, D., Rezende, E., Maia, M., Magnan, G. B., & the Investigating Nutritional Therapy with EPA, G. A. A. R. I. S. T. (. S. G. (2011) · Critical Care · doi:10.1186/cc10267 · Cited by 148 · see citing works
Hooper, M. H., Weavind, L., Wheeler, A. P., Martin, J. B., Gowda, S. S., Semler, M. W., Hayes, R. M., Albert, D. W., Deane, N. B., Nian, H., et al. (2012) · Critical Care Medicine · doi:10.1097/ccm.0b013e318250a887 · Cited by 121 · see citing works
Viale, P., Tedeschi, S., Scudeller, L., Attard, L., Badia, L., Bartoletti, M., Cascavilla, A., Cristini, F., Dentale, N., Fasulo, G., et al. (2017) · Clinical Infectious Diseases · doi:10.1093/cid/cix548 · Cited by 81 · see citing works
Nationwide survey on resource availability for implementing current sepsis guidelines in Mongolia.
Bataar, O., Lundeg, G., Tsenddorj, G., Jochberger, S., Grander, W., Baelani, I., Wilson, I., Baker, T., & Dünser, M. W. (2010) · Bulletin of the World Health Organization · doi:10.2471/blt.10.077073 · Cited by 62 · see citing works
Machine learning and artificial intelligence: applications in healthcare epidemiology
Hamilton, A. J., Strauss, A. T., Martinez, D. A., Hinson, J. S., Levin, S., Lin, G., & Klein, E. Y. (2021) · Antimicrobial Stewardship & Healthcare Epidemiology · doi:10.1017/ash.2021.192 · Cited by 58 · see citing works
Holt, S. G., Yo, J. H., Karschimkus, C., Volpato, F., Christov, S., Smith, E. R., Hewitson, T. D., Worth, L. J., & Champion De Crespigny, P. (2020) · Internal Medicine Journal · doi:10.1111/imj.14748 · Cited by 26 · see citing works
Dellinger, R. P., Levy, M. M., Carlet, J. M., Bion, J., Parker, M. M., Jaeschke, R., Reinhart, K., Angus, D. C., Brun-Buisson, C., Beale, R., et al. (2008) · Critical Care Medicine · doi:10.1097/01.ccm.0000298158.12101.41 · Cited by 9 · see citing works
- Authors
- Nguyen, H. B., Corbett, S. W., Steele, R., Banta, J., Clark, R. T., Hayes, S. R., Edwards, J., Cho, T. W., & Wittlake, W. A.
- Affiliations
- Loma Linda University (US)
- Venue
- Critical Care Medicine
- Published
- 2007-04
- Volume
- 35
- Issue
- 4
- Pages
- 1105-1112
- ISSN
- 0090-3493 (ISSN-L)
- OpenAlex
- W2106534536
- Cited by
- 555
- References
- 2
- Citations by year
- 2026: 3 · 2025: 10 · 2024: 7 · 2023: 11 · 2022: 8 · 2021: 17 · 2020: 14 · 2019: 21 · 2018: 23 · 2017: 32 · 2016: 33 · 2015: 51 · 2014: 39 · 2013: 49 · 2012: 47
- FWCI
- 33.88
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Critical Care Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Epidemiology › Sepsis Diagnosis and Treatment
- Health Sciences › Medicine › Surgery › Hemodynamic Monitoring and Therapy
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Abdominal Surgery and Complications
- Keywords
- severe sepsis, septic shock, quality improvement bundle, quality indicators, early goal-directed therapy, central venous pressure monitoring, central venous oxygen saturation, broad-spectrum antibiotics, corticosteroids, vasopressors, adrenal insufficiency, lactate clearance, in-hospital mortality, emergency department, physician behavior, clinician feedback, bundle compliance
- MeSH terms
- Aged, Clinical Protocols, Emergency Service, Hospital, Female, Hospitals, University, Humans, Length of Stay, Male, Middle Aged, Prospective Studies, Severity of Illness Index, Shock, Septic, Treatment Outcome, Hospital Mortality, Sepsis, APACHE, Guideline Adherence, Quality Indicators, Health Care
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
