title: Evaluation of syndromic algorithms for detecting patients with potentially transmissible infectious diseases based on computerised emergency-department data.
abstract: BACKGROUND
  The objective of this study was to ascertain the performance of syndromic algorithms for the early detection of patients in healthcare facilities who have potentially transmissible infectious diseases, using computerised emergency department (ED) data.
  
  
  METHODS
  A retrospective cohort in an 810-bed University of Lyon hospital in France was analysed. Adults who were admitted to the ED and hospitalised between June 1, 2007, and March 31, 2010 were included (N=10895). Different algorithms were built to detect patients with infectious respiratory, cutaneous or gastrointestinal syndromes. The performance parameters of these algorithms were assessed with regard to the capacity of our infection-control team to investigate the detected cases.
  
  
  RESULTS
  For respiratory syndromes, the sensitivity of the detection algorithms was 82.70%, and the specificity was 82.37%. For cutaneous syndromes, the sensitivity of the detection algorithms was 78.08%, and the specificity was 95.93%. For gastrointestinal syndromes, the sensitivity of the detection algorithms was 79.41%, and the specificity was 81.97%.
  
  
  CONCLUSIONS
  This assessment permitted us to detect patients with potentially transmissible infectious diseases, while striking a reasonable balance between true positives and false positives, for both respiratory and cutaneous syndromes. The algorithms for gastrointestinal syndromes were not specific enough for routine use, because they generated a large number of false positives relative to the number of infected patients. Detection of patients with potentially transmissible infectious diseases will enable us to take precautions to prevent transmission as soon as these patients come in contact with healthcare facilities.
authors.affiliation: Hospices Civils de Lyon, Hôpital de la Croix-Rousse, Unité d'hygiène et d'épidémiologie, F-69317 Lyon, France. solweig.gerbier-colomban@chu-lyon.fr.
authors.forename: Solweig
authors.initials: S
authors.lastname: Gerbier-Colomban
authors.forename: Quentin
authors.initials: Q
authors.lastname: Gicquel
authors.forename: Anne-Laure
authors.initials: AL
authors.lastname: Millet
authors.forename: Christophe
authors.initials: C
authors.lastname: Riou
authors.forename: Jacqueline
authors.initials: J
authors.lastname: Grando
authors.forename: Stefan
authors.initials: S
authors.lastname: Darmoni
authors.forename: Véronique
authors.initials: V
authors.lastname: Potinet-Pagliaroli
authors.forename: Marie-Hélène
authors.initials: MH
authors.lastname: Metzger
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D003141:Communicable Diseases / Q000145:classification / Q000175:diagnosis* / Q000453:epidemiology; D042241:Early Diagnosis; D004636:Emergency Service, Hospital / Q000592:standards / Q000706:statistics & numerical data*; D005260:Female; D005602:France; D006801:Humans; D008297:Male; D016347:Medical Records Systems, Computerized / Q000592:standards / Q000706:statistics & numerical data*; D008875:Middle Aged; D011159:Population Surveillance; D012189:Retrospective Studies; D012680:Sensitivity and Specificity