Statistics from Altmetric.com
If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.
There is little disagreement that screening for latent tuberculosis (TB) infection (LTBI) prior to biologic therapy is necessary. Unfortunately, there is much less agreement with regard to how best to accomplish this task. A decade ago, we had just one screening tool, the tuberculin skin test (TST), and screening algorithms for such patients were presumably straightforward. With the recent introduction and fairly widespread adoption of interferon γ release assays (IGRAs), clinicians have a new tool that theoretically improves their ability to screen. However, since the introduction of IGRAs, the studies conducted within the biologic therapy setting that compare IGRAs and TST have been fraught with a lack of statistical power and heterogeneity (eg, differing underlying inflammatory disease states, variable immunosuppressive therapies or underlying BCG status),1 ,2 and firm conclusions regarding the relative sensitivity of these two screening tests (Quantiferon-Gold TB In-tube (QFT-IT) or the T-SPOT.TB assay) is only now becoming more clear. Kleinert et al and Mariette et al have published two studies to overcome some of these limitations,3 ,4 and along with other data, we believe the time has arrived to offer a screening algorithm that is practical in its approach. While we believe IGRAs have become the preferred screening tool in this setting, unfortunately these recent data argue that an IGRA alone is insufficient to identify all patients at risk.
TB is rumoured to infect nearly a third of the world's population. The risk of LTBI progression to active TB is clearly heightened by antitumour necrosis factor α (anti-TNF) therapy, and the absolute risk of this complication is driven by the background prevalence of LTBI in a particular region and the probability of infection in an individual. Within low prevalence countries, rates of anti-TNF-associated TB are typically 5–10-fold higher than background general populations, …