Automated electronic medical record sepsis detection in the emergency department

Academic Article

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 ≤ 90mmHg, 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 throughMarch 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. © 2014 Nguyen et al.
  • Authors

    Published In

  • PeerJ  Journal
  • Digital Object Identifier (doi)

    Author List

  • Nguyen SQ; Mwakalindile E; Booth JS; Hogan V; Morgan J; Prickett CT; Donnelly JP; Wang HE
  • Volume

  • 2014
  • Issue

  • 1