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.

Figure 1

Calculated risk of a negative patient sample being tested false positive due to cross contamination (Y axis) for different prevalence rates of positive MTB samples (X axis) showing the effect of the different laboratory cross contamination rates - 1.1% (dashed line) and 7.3% (solid line). The upper graph shows the exponential effect of the prevalence of true positives on contamination risk and the low graph shows an enlarged view of the clinically relevant area of the curve from the upper graph.