Improvement in Process of Care and Outcome After a Multicenter Severe Sepsis Educational Program in Spain
Abstract
CONTEXT: Concern exists that current guidelines for care of patients with severe sepsis and septic shock are followed variably, possibly due to a lack of adequate education. OBJECTIVE: To determine whether a national educational program based on the Surviving Sepsis Campaign guidelines affected processes of care and hospital mortality for severe sepsis. DESIGN, SETTING, AND PATIENTS: Before and after design in 59 medical-surgical intensive care units (ICUs) located throughout Spain. All ICU patients were screened daily and enrolled if they fulfilled severe sepsis or septic shock criteria. A total of 854 patients were enrolled in the preintervention period (November-December 2005), 1465 patients during the postintervention period (March-June 2006), and 247 patients during the long-term follow-up period 1 year later (November-December 2006) in a subset of 23 ICUs. INTERVENTION: The educational program consisted of training physicians and nursing staff from the emergency department, wards, and ICU in the definition, recognition, and treatment of severe sepsis and septic shock as outlined in the guidelines. Treatment was organized in 2 bundles: a resuscitation bundle (6 tasks to begin immediately and be accomplished within 6 hours) and a management bundle (4 tasks to be completed within 24 hours). MAIN OUTCOME MEASURES: Hospital mortality, differences in adherence to the bundles' process-of-care variables, ICU mortality, 28-day mortality, hospital length of stay, and ICU length o…
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 [11]
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
Flagged first, then by citations
Cited by
Sorted by citations
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
Effectiveness of treatments for severe sepsis: A prospective, multicenter, observational study.
Ferrer, R., Artigas, A., Suarez, D., Palencia, E., Levy, M. M., Arenzana, A., Pérez, X. L., & Sirvent, J.-M. (2009) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.200812-1912oc · Cited by 433 · see citing works
Self-citationHorng, S., Sontag, D. A., Halpern, Y., Jernite, Y., Shapiro, N. I., & Nathanson, L. A. (2017) · PLOS ONE · doi:10.1371/journal.pone.0174708 · Cited by 310 · 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
Phua, J., Koh, Y., Du, B., Tang, Y.-Q., Divatia, J. V., Tan, C. C., Gomersall, C. D., Faruq, M. O., Shrestha, B. R., Gia Binh, N., et al. (2011) · BMJ · doi:10.1136/bmj.d3245 · Cited by 239 · see citing works
Bloos, F., Thomas-Rüddel, D., Rüddel, H., Engel, C., Schwarzkopf, D., Marshall, J. C., Harbarth, S., Simon, P., Riessen, R., Keh, D., et al. (2014) · Critical Care · doi:10.1186/cc13755 · Cited by 240 · see citing works
Culture-Negative Severe Sepsis
Gupta, S., Sakhuja, A., Kumar, G., McGrath, E., Nanchal, R. S., & Kashani, K. B. (2016) · Chest · doi:10.1016/j.chest.2016.08.1460 · Cited by 232 · see citing works
for the MEDUSA study group, Bloos, F., Rüddel, H., Thomas-Rüddel, D., Schwarzkopf, D., Pausch, C., Harbarth, S., Schreiber, T., Gründling, M., Marshall, J., et al. (2017) · Intensive Care Medicine · doi:10.1007/s00134-017-4782-4 · Cited by 200 · see citing works
Impact of source control in patients with severe sepsis and septic shock.
Martínez, M. L., Ferrer, R., Torrents, E., Guillamat-Prats, R., Gomà, G., Suárez, D., Álvarez-Rocha, L., Pozo Laderas, J. C., Martín-Loeches, I., Levy, M. M., et al. (2017) · Critical Care Medicine · doi:10.1097/ccm.0000000000002011 · Cited by 192 · see citing works
Self-citationIntegrating 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
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
Makam, A. N., Nguyen, O. K., & Auerbach, A. D. (2015) · Journal of Hospital Medicine · doi:10.1002/jhm.2347 · Cited by 99 · 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
National Performance on the Medicare SEP-1 Sepsis Quality Measure
Barbash, I. J., Davis, B., & Kahn, J. M. (2019) · Critical Care Medicine · doi:10.1097/ccm.0000000000003613 · Cited by 86 · see citing works
Application of a framework to assess the usefulness of alternative sepsis criteria
Seymour, C. W., Coopersmith, C. M., Deutschman, C. S., Gesten, F., Klompas, M., Levy, M., Martin, G. S., Osborn, T. M., Rhee, C., Warren, D. K., et al. (2016) · Critical Care Medicine · doi:10.1097/ccm.0000000000001724 · Cited by 71 · 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
Lau, B. D., Arnaoutakis, G. J., Streiff, M. B., Howley, I. W., Poruk, K. E., Beaulieu, R., Ellison, T. A., Van Arendonk, K. J., Kraus, P. S., Hobson, D. B., et al. (2016) · Annals of Surgery · doi:10.1097/sla.0000000000001512 · Cited by 53 · see citing works
Use of explicit ICD9-CM codes to identify adult severe sepsis: impacts on epidemiological estimates
Bouza, C., Lopez-Cuadrado, T., & Amate-Blanco, J. M. (2016) · Critical Care · doi:10.1186/s13054-016-1497-9 · Cited by 47 · see citing works
Matthaeus-Kraemer, C. T., Thomas-Rueddel, D. O., Schwarzkopf, D., Rueddel, H., Poidinger, B., Reinhart, K., & Bloos, F. (2016) · Journal of Critical Care · doi:10.1016/j.jcrc.2016.06.034 · Cited by 31 · see citing works
Enhancing sepsis management through machine learning techniques: A review
Ocampo-Quintero, N., Vidal-Cortés, P., del Río Carbajo, L., Fdez-Riverola, F., Reboiro-Jato, M., & Glez-Peña, D. (2022) · Medicina Intensiva · doi:10.1016/j.medin.2020.04.003 · Cited by 20 · see citing works
- Authors
- Ferrer, R.
- Affiliations
- Universitat Autònoma de Barcelona (ES)
- Corresponding author
- Ferrer, Ricard
- ORCID
- Ferrer, Ricard
- Venue
- JAMA
- Publisher
- American Medical Association (AMA)
- Published
- 2008-05-21
- Volume
- 299
- Issue
- 19
- Pages
- 2294
- ISSN
- 0002-9955 (ISSN-L)
- 0098-7484
- OpenAlex
- W2103053257
- Cited by
- 741
- References
- 1
- Citations by year
- 2026: 10 · 2025: 9 · 2024: 18 · 2023: 17 · 2022: 16 · 2021: 31 · 2020: 34 · 2019: 31 · 2018: 29 · 2017: 51 · 2016: 45 · 2015: 63 · 2014: 53 · 2013: 71 · 2012: 77
- FWCI
- 46.02
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- JAMA (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Epidemiology › Sepsis Diagnosis and Treatment
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Hyperglycemia and glycemic control in critically ill and hospitalized patients
- Health Sciences › Medicine › Emergency Medicine › Cardiac Arrest and Resuscitation
- Keywords
- severe sepsis, septic shock, Surviving Sepsis Campaign guidelines, clinical practice guideline adherence, educational intervention, process of care, in-hospital mortality, ICU mortality, intensive care unit, length of hospital stay, broad-spectrum antibiotics, mechanical ventilation, quality improvement
- MeSH terms
- Aged, Female, Humans, Inservice Training, Intensive Care Units, Length of Stay, Male, Middle Aged, Outcome and Process Assessment, Health Care, Severity of Illness Index, Shock, Septic, Spain, Hospital Mortality, Practice Guidelines as Topic, Sepsis, Guideline Adherence
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