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Cited by 52Sepsis Diagnosis and TreatmentElectronic Health Records SystemsEmergency and Acute Care Studies

Diagnostic accuracy of a screening electronic alert tool for severe sepsis and septic shock in the emergency department

Open access
Alsolamy, Sami, Al Salamah, Majid, Al Thagafi, Majed, Al-Dorzi, Hasan M, Marini, Abdellatif M, Aljerian, Nawfal, Al-Enezi, Farhan, Al-Hunaidi, Fatimah +3 more·2014·BMC Medical Informatics and Decision Making
DOI 10.1186/s12911-014-0105-7PMC PMC4261595
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Abstract

BACKGROUND: Early recognition of severe sepsis and septic shock is challenging. The aim of this study was to determine the diagnostic accuracy of an electronic alert system in detecting severe sepsis or septic shock among emergency department (ED) patients. METHODS: An electronic sepsis alert system was developed as a part of a quality-improvement project for severe sepsis and septic shock. The system screened all adult ED patients for a combination of systemic inflammatory response syndrome and organ dysfunction criteria (hypotension, hypoxemia or lactic acidosis). This study included all patients older than 14 years who presented to the ED of a tertiary care academic medical center from Oct. 1, 2012 to Jan. 31, 2013. As a comparator, emergency medicine physicians or the critical care physician identified the patients with severe sepsis or septic shock. In the ED, vital signs were manually entered into the hospital electronic heath record every hour in the critical care area and every two hours in other areas. We also calculated the time from the alert to the intensive care unit (ICU) referral. RESULTS: Of the 49,838 patients who presented to the ED, 222 (0.4%) were identified to have severe sepsis or septic shock. The electronic sepsis alert had a sensitivity of 93.18% (95% CI, 88.78% - 96.00%), specificity of 98.44 (95% CI, 98.33% - 98.55%), positive predictive value of 20.98% (95% CI, 18.50% - 23.70%) and negative predictive value of 99.97% (95% CI, 99.95% - 99.98%) for severe sepsis and septic shock. The alert preceded ICU referral by a median of 4.02 hours (Q1 - Q3: 1.25-8.55). CONCLUSIONS: Our study shows that electronic sepsis alert tool has high sensitivity and specificity in recognizing severe sepsis and septic shock, which may improve early recognition and management.

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  • Current approaches to identify septic patients have centered around biomarkers and (automated) clinical decision rules such as the SIRS and (q)SOFA criteria.

    “Current approaches to identify septic patients have centered around biomarkers and (automated) clinical decision rules such as the SIRS and (q)SOFA criteria [38, 39].”

    From Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy (2020) · cites this paper as [39]

    The Methods (List 1; page unavailable) describe an automated sepsis alert using SIRS criteria and a lactate threshold, and the Discussion describes other SIRS-based screening tools. The paper does not address (q)SOFA or establish the broader claim that approaches have centered on biomarkers. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 1

  1. [14]Langley GJ Moen R Nolan KM Nolan TW Norman CL Provost LP The Improvement Guide 2009 San Franciso, California John Wiley & Sons

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Authors
Alsolamy, S., Al Salamah, M., Al Thagafi, M., Al-Dorzi, H. M., Marini, A. M., Aljerian, N., Al-Enezi, F., Al-Hunaidi, F., Mahmoud, A. M., Alamry, A., & Arabi, Y. M.
Affiliations
  • King Saud bin Abdulaziz University for Health Sciences (SA)
  • King Abdullah International Medical Research Center (SA)
Corresponding author
Arabi, Yaseen M
ORCID
Venue
BMC Medical Informatics and Decision Making
Publisher
Springer Science and Business Media LLC
Published
2014-12
Volume
14
Issue
1
Pages
105-105
ISSN
1472-6947 (ISSN-L)
DOI
10.1186/s12911-014-0105-7
PMC
PMC4261595
OpenAlex
W2147002030
Cited by
52
References
18
Citations by year
2025: 2 · 2024: 2 · 2023: 1 · 2022: 5 · 2021: 7 · 2020: 4 · 2019: 5 · 2018: 8 · 2017: 5 · 2016: 8 · 2015: 4 · 2014: 1
FWCI
4.55
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Medical Informatics and Decision Making (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
King Abdullah International Medical Research Center
Topics
  • Health Sciences › Medicine › Epidemiology › Sepsis Diagnosis and Treatment
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
  • Health Sciences › Medicine › Emergency Medicine › Emergency and Acute Care Studies
Keywords
severe sepsis, septic shock, electronic alert system, sepsis screening, diagnostic accuracy, emergency department, early recognition, systemic inflammatory response syndrome, organ dysfunction, electronic health records, quality improvement
MeSH terms
Adult, Emergency Service, Hospital, Humans, Mass Screening, Prospective Studies, Sensitivity and Specificity, Shock, Septic, Systemic Inflammatory Response Syndrome, Sepsis, Critical Pathways, Decision Support Systems, Clinical, Early Diagnosis, Quality Improvement, Organ Dysfunction Scores

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