title: Diagnostic accuracy and effectiveness of automated electronic sepsis alert systems: A systematic review.
abstract: BACKGROUND
  Although timely treatment of sepsis improves outcomes, delays in administering evidence-based therapies are common.
  
  
  OBJECTIVE
  To determine whether automated real-time electronic sepsis alerts can: (1) accurately identify sepsis and (2) improve process measures and outcomes.
  
  
  METHODS
  We systematically searched MEDLINE, Embase, The Cochrane Library, and Cumulative Index to Nursing and Allied Health Literature from database inception through June 27, 2014.
  
  
  METHODS
  Included studies that empirically evaluated 1 or both of the prespecified objectives.
  
  
  METHODS
  Two independent reviewers extracted data and assessed the risk of bias. Diagnostic accuracy of sepsis identification was measured by sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and likelihood ratio (LR). Effectiveness was assessed by changes in sepsis care process measures and outcomes.
  
  
  RESULTS
  Of 1293 citations, 8 studies met inclusion criteria, 5 for the identification of sepsis (n = 35,423) and 5 for the effectiveness of sepsis alerts (n = 6894). Though definition of sepsis alert thresholds varied, most included systemic inflammatory response syndrome criteria ± evidence of shock. Diagnostic accuracy varied greatly, with PPV ranging from 20.5% to 53.8%, NPV 76.5% to 99.7%, LR+ 1.2 to 145.8, and LR- 0.06 to 0.86. There was modest evidence for improvement in process measures (ie, antibiotic escalation), but only among patients in non-critical care settings; there were no corresponding improvements in mortality or length of stay. Minimal data were reported on potential harms due to false positive alerts.
  
  
  CONCLUSIONS
  Automated sepsis alerts derived from electronic health data may improve care processes but tend to have poor PPV and do not improve mortality or length of stay.
authors.affiliation: Division of General Internal Medicine, University Texas Southwestern Medical Center, Dallas, Texas.
authors.forename: Anil N
authors.initials: AN
authors.lastname: Makam
authors.affiliation: Division of General Internal Medicine, University Texas Southwestern Medical Center, Dallas, Texas.
authors.forename: Oanh K
authors.initials: OK
authors.lastname: Nguyen
authors.affiliation: Divsion of Hospital Medicine, University of California San Francisco, San Francisco, California.
authors.forename: Andrew D
authors.initials: AD
authors.lastname: Auerbach
mesh_terms: D056902:Clinical Alarms / Q000592:standards / Q000706:statistics & numerical data*; D016206:Databases, Bibliographic; D020000:Decision Support Systems, Clinical; D003937:Diagnosis, Differential; D042241:Early Diagnosis; D006801:Humans; D050316:Medical Order Entry Systems / Q000592:standards* / Q000706:statistics & numerical data; D017063:Outcome Assessment, Health Care / Q000706:statistics & numerical data; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D018805:Sepsis / Q000175:diagnosis*