title: The realistic performance achievable with mycobacterial automated culture systems in high and low prevalence settings.
abstract: BACKGROUND
  Diagnostic tests are generally used in situations with similar pre-test probability of disease to where they were developed. When these tests are applied in situations with very different pre-test probabilities of disease, it is informative to model the likely implications of known characteristics of test performance in the new situation. This is the case for automated Mycobacterium tuberculosis (MTB) liquid culture systems for tuberculosis case detection which were developed and are widely used in low burden settings but are only beginning to be applied on a large scale in high burden settings.
  
  
  METHODS
  Here we model the performance of MTB liquid culture systems in high and low tuberculosis (TB) prevalence settings using detailed published data concentrating on the likely frequency of cross-contamination events.
  
  
  RESULTS
  Our model predicts that as the TB prevalence in the suspect population increases there is an exponential increase in the risk of MTB cross-contamination events expected in otherwise negative samples, even with equivalent technical performance of the laboratories. Quality control and strict cross-contamination measures become increasingly critical as the burden of MTB infection among TB suspects increases. Even under optimal conditions the realistically achievable specificity of these systems in high burden settings will likely be significantly below that obtained in low TB burden laboratories.
  
  
  CONCLUSIONS
  Liquid culture systems can play a valuable role in TB case detection in laboratories in high burden settings, but laboratory workers, policy makers and clinicians should be aware of the increased risks, independent of laboratory proficiency, of cross-contamination events in high burden settings.
authors.affiliation: VU University Amsterdam, Faculty of Earth and Life Sciences, De Boelenlaan 1085, 1081 HV Amsterdam, The Netherlands.
authors.forename: Sanne C
authors.initials: SC
authors.lastname: van Kampen
authors.forename: Richard M
authors.initials: RM
authors.lastname: Anthony
authors.forename: Paul R
authors.initials: PR
authors.lastname: Klatser
mesh_terms: D001331:Automation / Q000379:methods*; D001431:Bacteriological Techniques / Q000379:methods*; D003951:Diagnostic Errors; D006801:Humans; D009169:Mycobacterium tuberculosis / Q000302:isolation & purification*; D015995:Prevalence; D014376:Tuberculosis / Q000175:diagnosis* / Q000453:epidemiology*
references.citation: J Clin Microbiol. 2002 Nov;40(11):4004-9
references.pmid: 12409366
references.citation: Int J Tuberc Lung Dis. 2009 Sep;13(9):1051-3
references.pmid: 19723391
references.citation: Int J Tuberc Lung Dis. 2009 Jul;13(7):799-800
references.pmid: 19555526
references.citation: Infect Control Hosp Epidemiol. 1998 May;19(5):345-7
references.pmid: 9613697
references.citation: Clin Infect Dis. 2000 Dec;31(6):1390-5
references.pmid: 11096008
references.citation: Arch Pathol Lab Med. 2001 Sep;125(9):1213-6
references.pmid: 11520275
references.citation: Clin Chem Lab Med. 2002 Sep;40(9):888-92
references.pmid: 12435105
references.citation: Intern Med J. 2002 Nov;32(11):512-9
references.pmid: 12412933
references.citation: Int J Tuberc Lung Dis. 2009 Apr;13(4):419-20
references.pmid: 19335944
references.citation: J Clin Microbiol. 2006 Aug;44(8):2967-9
references.pmid: 16891518