title: Developing the surveillance algorithm for detection of failure to recognize and treat severe sepsis.
abstract: OBJECTIVE
  To develop and test an automated surveillance algorithm (sepsis "sniffer") for the detection of severe sepsis and monitoring failure to recognize and treat severe sepsis in a timely manner.
  
  
  METHODS
  We conducted an observational diagnostic performance study using independent derivation and validation cohorts from an electronic medical record database of the medical intensive care unit (ICU) of a tertiary referral center. All patients aged 18 years and older who were admitted to the medical ICU from January 1 through March 31, 2013 (N=587), were included. The criterion standard for severe sepsis/septic shock was manual review by 2 trained reviewers with a third superreviewer for cases of interobserver disagreement. Critical appraisal of false-positive and false-negative alerts, along with recursive data partitioning, was performed for algorithm optimization.
  
  
  RESULTS
  An algorithm based on criteria for suspicion of infection, systemic inflammatory response syndrome, organ hypoperfusion and dysfunction, and shock had a sensitivity of 80% and a specificity of 96% when applied to the validation cohort. In order, low systolic blood pressure, systemic inflammatory response syndrome positivity, and suspicion of infection were determined through recursive data partitioning to be of greatest predictive value. Lastly, 117 alert-positive patients (68% of the 171 patients with severe sepsis) had a delay in recognition and treatment, defined as no lactate and central venous pressure measurement within 2 hours of the alert.
  
  
  CONCLUSIONS
  The optimized sniffer accurately identified patients with severe sepsis that bedside clinicians failed to recognize and treat in a timely manner.
authors.affiliation: Medical Scientist Training Program, Mayo Graduate School, Mayo Clinic, Rochester MN; Multidisciplinary Epidemiology and Translational Research in Intensive Care, Mayo Clinic, Rochester MN.
authors.forename: Andrew M
authors.initials: AM
authors.lastname: Harrison
authors.affiliation: Multidisciplinary Epidemiology and Translational Research in Intensive Care, Mayo Clinic, Rochester MN.
authors.forename: Charat
authors.initials: C
authors.lastname: Thongprayoon
authors.affiliation: Multidisciplinary Epidemiology and Translational Research in Intensive Care, Mayo Clinic, Rochester MN; Department of Anesthesiology, Mayo Clinic, Rochester MN.
authors.forename: Rahul
authors.initials: R
authors.lastname: Kashyap
authors.affiliation: Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester MN.
authors.forename: Christopher G
authors.initials: CG
authors.lastname: Chute
authors.affiliation: Multidisciplinary Epidemiology and Translational Research in Intensive Care, Mayo Clinic, Rochester MN; Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester MN.
authors.forename: Ognjen
authors.initials: O
authors.lastname: Gajic
authors.affiliation: Multidisciplinary Epidemiology and Translational Research in Intensive Care, Mayo Clinic, Rochester MN; Department of Anesthesiology, Mayo Clinic, Rochester MN.
authors.forename: Brian W
authors.initials: BW
authors.lastname: Pickering
authors.affiliation: Multidisciplinary Epidemiology and Translational Research in Intensive Care, Mayo Clinic, Rochester MN; Department of Anesthesiology, Mayo Clinic, Rochester MN. Electronic address: herasevich.vitaly@mayo.edu.
authors.forename: Vitaly
authors.initials: V
authors.lastname: Herasevich
mesh_terms: D000465:Algorithms; D057286:Electronic Health Records; D006801:Humans; D007362:Intensive Care Units / Q000706:statistics & numerical data; D008910:Minnesota; D011159:Population Surveillance; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D018805:Sepsis / Q000175:diagnosis* / Q000628:therapy