The realistic performance achievable with mycobacterial automated culture systems in high and low prevalence settings.
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.
van Kampen
Sanne C
SC
VU University Amsterdam, Faculty of Earth and Life Sciences, De Boelenlaan 1085, 1081 HV Amsterdam, The Netherlands.
Anthony
Richard M
RM
Klatser
Paul R
PR
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*
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