title: Prospective evaluation of an automated method to identify patients with severe sepsis or septic shock in the emergency department.
abstract: Sepsis is an often-fatal syndrome resulting from severe infection. Rapid identification and treatment are critical for septic patients. We therefore developed a probabilistic model to identify septic patients in the emergency department (ED). We aimed to produce a model that identifies 80 % of sepsis patients, with no more than 15 false positive alerts per day, within one hour of ED admission, using routine clinical data.
  
  
  
  We developed the model using retrospective data for 132,748 ED encounters (549 septic), with manual chart review to confirm cases of severe sepsis or septic shock from January 2006 through December 2008. A naïve Bayes model was used to select model features, starting with clinician-proposed candidate variables, which were then used to calculate the probability of sepsis. We evaluated the accuracy of the resulting model in 93,733 ED encounters from April 2009 through June 2010.
  
  
  
  The final model included mean blood pressure, temperature, age, heart rate, and white blood cell count. The area under the receiver operating characteristic curve (AUC) for the continuous predictor model was 0.953. The binary alert achieved 76.4 % sensitivity with a false positive rate of 4.7 %.
  
  
  
  We developed and validated a probabilistic model to identify sepsis early in an ED encounter. Despite changes in process, organizational focus, and the H1N1 influenza pandemic, our model performed adequately in our validation cohort, suggesting that it will be generalizable.
authors.affiliation: Pulmonary and Critical Care Medicine, Intermountain Medical Center, Murray, USA. samuel.brown@imail.org.|Pulmonary and Critical Care Medicine, University of Utah School of Medicine, Salt Lake City, USA. samuel.brown@imail.org.|Shock Trauma ICU, Intermountain Medical Center, 5121 S. Cottonwood Street, Murray, UT, 84107, USA. samuel.brown@imail.org.
authors.forename: Samuel M
authors.initials: SM
authors.lastname: Brown
authors.affiliation: Clinical Intelligence and Decision Support, Kaiser Permanente Southern California, Pasadena, USA.
authors.forename: Jason
authors.initials: J
authors.lastname: Jones
authors.affiliation: Pulmonary and Critical Care Medicine, Intermountain Medical Center, Murray, USA.|Homer Warner Center for Informatics Research, Intermountain Healthcare, Murray, USA.
authors.forename: Kathryn Gibb
authors.initials: KG
authors.lastname: Kuttler
authors.affiliation: Intensive Medicine Clinical Program, Intermountain Healthcare, Salt Lake City, USA.
authors.forename: Roger K
authors.initials: RK
authors.lastname: Keddington
authors.affiliation: Institute for Healthcare Delivery Research, Intermountain Healthcare, Salt Lake City, USA.
authors.forename: Todd L
authors.initials: TL
authors.lastname: Allen
authors.affiliation: Homer Warner Center for Informatics Research, Intermountain Healthcare, Murray, USA.
authors.forename: Peter
authors.initials: P
authors.lastname: Haug
mesh_terms: D000328:Adult; D000367:Age Factors; D001499:Bayes Theorem; D001794:Blood Pressure; D001831:Body Temperature; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D005260:Female; D006339:Heart Rate; D006801:Humans; D007958:Leukocyte Count; D008297:Male; D008875:Middle Aged; D012372:ROC Curve; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D018805:Sepsis / Q000175:diagnosis*; D012772:Shock, Septic / Q000175:diagnosis; D014218:Triage / Q000379:methods*
keywords: Automated detection; Bayesian classifier; Sepsis