Automated electronic medical record sepsis detection in the emergency department
Open accessPublished version of a preprint
Abstract
Background. While often first treated in the emergency department (ED), identification of sepsis is difficult. Electronic medical record (EMR) clinical decision tools offer a novel strategy for identifying patients with sepsis. The objective of this study was to test the accuracy of an EMR-based, automated sepsis identification system. Methods. We tested an EMR-based sepsis identification tool at a major academic, urban ED with 64,000 annual visits. The EMR system collected vital sign and laboratory test information on all ED patients, triggering a "sepsis alert" for those with ≥2 SIRS (systemic inflammatory response syndrome) criteria (fever, tachycardia, tachypnea, leukocytosis) plus ≥1 major organ dysfunction (SBP ≤ 90 mm Hg, lactic acid ≥2.0 mg/dL). We confirmed the presence of sepsis through manual review of physician, nursing, and laboratory records. We also reviewed a random selection of ED cases that did not trigger a sepsis alert. We evaluated the diagnostic accuracy of the sepsis identification tool. Results. From January 1 through March 31, 2012, there were 795 automated sepsis alerts. We randomly selected 300 cases without a sepsis alert from the same period. The true prevalence of sepsis was 355/795 (44.7%) among alerts and 0/300 (0%) among non-alerts. The positive predictive value of the sepsis alert was 44.7% (95% CI [41.2-48.2%]). Pneumonia and respiratory infections (38%) and urinary tract infection (32.7%) were the most common infections among the 355 patients with true sepsis (true positives). Among false-positive sepsis alerts, the most common medical conditions were gastrointestinal (26.1%), traumatic (25.7%), and cardiovascular (20.0%) conditions. Rates of hospital admission were: true-positive sepsis alert 91.0%, false-positive alert 83.0%, no sepsis alert 5.7%. Conclusions. This ED EMR-based automated sepsis identification system was able to detect cases with sepsis. Automated EMR-based detection may provide a viable strategy for identifying sepsis in the ED.
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Interventional studies using traditional SIRS and SOFA alarm systems have not shown significant changes in clinical outcomes.
“Interventional studies using traditional SIRS and SOFA alarm systems have not shown significant changes in clinical outcomes [48–50].”
From Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy (2020) · cites this paper as [48]
The Methods (Selection of subjects) say the SIRS-based alerts were evaluated after the fact and were not communicated to clinicians. The Limitations state that the study assessed detection accuracy, not clinical implementation. Our copy does not report an interventional study of SIRS or SOFA alarms or whether such alarms changed clinical outcomes. (LLM, conf=0.79)
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Research works printed but not linked: 16
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- Authors
- Nguyen, S. Q., Mwakalindile, E., Booth, J. S., Hogan, V., Morgan, J., Prickett, C. T., Donnelly, J. P., & Wang, H. E.
- Affiliations
- University of Alabama (US)
- University of Alabama at Birmingham (US)
- University of Alabama at Birmingham Hospital (US)
- Venue
- PeerJ
- Publisher
- PeerJ
- Published
- 2014-04-10
- Volume
- 2
- Pages
- e343
- ISSN
- 2167-8359 (ISSN-L)
- PMC
- PMC3994640
- OpenAlex
- W2083009186
- Cited by
- 68
- References
- 0
- Citations by year
- 2026: 3 · 2025: 1 · 2024: 3 · 2023: 4 · 2022: 3 · 2021: 7 · 2020: 5 · 2019: 8 · 2018: 9 · 2017: 8 · 2016: 10 · 2015: 6 · 2014: 1
- FWCI
- 6.31
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- PeerJ (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Epidemiology › Sepsis Diagnosis and Treatment
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- sepsis detection, electronic health records, clinical decision support, emergency department, automated alerts, systemic inflammatory response syndrome, diagnostic accuracy, positive predictive value, false positive alerts, sepsis prevalence, hospitalization
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