title: Clinical predictors for laboratory-confirmed influenza infections: exploring case definitions for influenza-like illness.
abstract: OBJECTIVE
  To identify clinical signs and symptoms (ie, "terms") that accurately predict laboratory-confirmed influenza cases and thereafter generate and evaluate various influenza-like illness (ILI) case definitions for detecting influenza. A secondary objective explored whether surveillance of data beyond the chief complaint improves the accuracy of predicting influenza.
  
  
  METHODS
  Retrospective, cross-sectional study.
  
  
  METHODS
  Large urban academic medical center hospital.
  
  
  METHODS
  A total of 1,581 emergency department (ED) patients who received a nasopharyngeal swab followed by rRT-PCR testing between August 30, 2009, and January 2, 2010, and between November 28, 2010, and March 26, 2011.
  
  
  METHODS
  An electronic surveillance system (GUARDIAN) scanned the entire electronic medical record (EMR) and identified cases containing 29 clinical terms relevant to influenza. Analyses were conducted using logistic regressions, diagnostic odds ratio (DOR), sensitivity, and specificity.
  
  
  RESULTS
  The best predictive model for identifying influenza for all ages consisted of cough (DOR=5.87), fever (DOR=4.49), rhinorrhea (DOR=1.98), and myalgias (DOR=1.44). The 3 best case definitions that included combinations of some or all of these 4 symptoms had comparable performance (ie, sensitivity=89%-92% and specificity=38%-44%). For children <5 years of age, the addition of rhinorrhea to the fever and cough case definition achieved a better balance between sensitivity (85%) and specificity (47%). For the fever and cough ILI case definition, using the entire EMR, GUARDIAN identified 37.1% more influenza cases than it did using only the chief complaint data.
  
  
  CONCLUSIONS
  A simplified case definition of fever and cough may be suitable for implementation for all ages, while inclusion of rhinorrhea may further improve influenza detection for the 0-4-year-old age group. Finally, ILI surveillance based on the entire EMR is recommended.
authors.affiliation: 1Rush University Medical Center,Department of Emergency Medicine,Chicago,Illinois.
authors.forename: Shital C
authors.initials: SC
authors.lastname: Shah
authors.affiliation: 1Rush University Medical Center,Department of Emergency Medicine,Chicago,Illinois.
authors.forename: Dino P
authors.initials: DP
authors.lastname: Rumoro
authors.affiliation: 1Rush University Medical Center,Department of Emergency Medicine,Chicago,Illinois.
authors.forename: Marilyn M
authors.initials: MM
authors.lastname: Hallock
authors.affiliation: 3Rush University Medical Center,Division of Infectious Diseases,Department of Medicine,Chicago,Illinois.
authors.forename: Gordon M
authors.initials: GM
authors.lastname: Trenholme
authors.affiliation: 1Rush University Medical Center,Department of Emergency Medicine,Chicago,Illinois.
authors.forename: Gillian S
authors.initials: GS
authors.lastname: Gibbs
authors.affiliation: 1Rush University Medical Center,Department of Emergency Medicine,Chicago,Illinois.
authors.forename: Julio C
authors.initials: JC
authors.lastname: Silva
authors.affiliation: 4Pangaea Information Technologies,Chicago,Illinois.
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Waddell
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002648:Child; D002675:Child, Preschool; D003430:Cross-Sectional Studies; D003661:Decision Support Techniques; D004636:Emergency Service, Hospital; D005260:Female; D006801:Humans; D007087:Illinois; D007223:Infant; D007231:Infant, Newborn; D007251:Influenza, Human / Q000150:complications / Q000175:diagnosis*; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D016017:Odds Ratio; D062486:Public Health Surveillance; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D055815:Young Adult