title: Biochemical phenotypes to discriminate microbial subpopulations and improve outbreak detection.
abstract: BACKGROUND
  Clinical microbiology laboratories worldwide constitute an invaluable resource for monitoring emerging threats and the spread of antimicrobial resistance. We studied the growing number of biochemical tests routinely performed on clinical isolates to explore their value as epidemiological markers.
  
  
  RESULTS
  Microbiology laboratory results from January 2009 through December 2011 from a 793-bed hospital stored in WHONET were examined. Variables included patient location, collection date, organism, and 47 biochemical and 17 antimicrobial susceptibility test results reported by Vitek 2. To identify biochemical tests that were particularly valuable (stable with repeat testing, but good variability across the species) or problematic (inconsistent results with repeat testing), three types of variance analyses were performed on isolates of K. pneumonia: descriptive analysis of discordant biochemical results in same-day isolates, an average within-patient variance index, and generalized linear mixed model variance component analysis.
  
  
  RESULTS
  4,200 isolates of K. pneumoniae were identified from 2,485 patients, 32% of whom had multiple isolates. The first two variance analyses highlighted SUCT, TyrA, GlyA, and GGT as "nuisance" biochemicals for which discordant within-patient test results impacted a high proportion of patient results, while dTAG had relatively good within-patient stability with good heterogeneity across the species. Variance component analyses confirmed the relative stability of dTAG, and identified additional biochemicals such as PHOS with a large between patient to within patient variance ratio. A reduced subset of biochemicals improved the robustness of strain definition for carbapenem-resistant K. pneumoniae. Surveillance analyses suggest that the reduced biochemical profile could improve the timeliness and specificity of outbreak detection algorithms.
  
  
  CONCLUSIONS
  The statistical approaches explored can improve the robust recognition of microbial subpopulations with routinely available biochemical test results, of value in the timely detection of outbreak clones and evolutionarily important genetic events.
authors.affiliation: WHO Collaborating Centre for Surveillance of Antimicrobial Resistance, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
authors.forename: Alicia
authors.initials: A
authors.lastname: Galar
authors.affiliation: Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, United States of America.
authors.forename: Martin
authors.initials: M
authors.lastname: Kulldorff
authors.affiliation: Department of Medicine, Mount Sinai Hospital, Toronto, Ontario, Canada.
authors.forename: Wallis
authors.initials: W
authors.lastname: Rudnick
authors.affiliation: WHO Collaborating Centre for Surveillance of Antimicrobial Resistance, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
authors.forename: Thomas F
authors.initials: TF
authors.lastname: O'Brien
authors.affiliation: WHO Collaborating Centre for Surveillance of Antimicrobial Resistance, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
authors.forename: John
authors.initials: J
authors.lastname: Stelling
mesh_terms: D000704:Analysis of Variance; D015415:Biomarkers; D016000:Cluster Analysis; D021821:Communicable Diseases, Emerging / Q000175:diagnosis*; D004196:Disease Outbreaks; D004352:Drug Resistance, Microbial / Q000235:genetics*; D006801:Humans; D007710:Klebsiella Infections / Q000175:diagnosis* / Q000453:epidemiology*; D007711:Klebsiella pneumoniae / Q000235:genetics*; D010641:Phenotype
chemical_list: D015415:Biomarkers