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Nguyen S, Mwakalindile E, Booth JS, Hogan V, Morgan J, Prickett CT, Donnelly JP, Wang HE.2014. Automated electronic medical record sepsis detection in the Emergency Department. PeerJ PrePrints2:e279v1https://doi.org/10.7287/peerj.preprints.279v1
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 non-sepsis alert records. We evaluated the diagnostic accuracy of the sepsis identification tool. Results : From January 1 through March 31, 2012, we analyzed 795 automated sepsis alerts and 300 non-alerts. The true prevalence of sepsis was 293/795 (37%) among alerts and 0/300 (0%) among non-alerts. The positive predictive value was 36.9% (41.7-49.6). Respiratory infections (36.5%) and urinary tract infection (35.5%) were the most common infections among the 293 patients with true sepsis (true positives). Among false-positive sepsis alerts, the most common medical conditions were gastrointestinal (22.9%), traumatic (22.3%), and cardiovascular (17.5%). Conclusion : This ED EMR-based automated sepsis identification system was able to detect sepsis patients. Automated EMR-based detection may provide a viable strategy for identifying sepsis.
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