PT  - JOURNAL ARTICLE
AU  - Petruccioli, Elisa
AU  - Petrone, Linda
AU  - Chiacchio, Teresa
AU  - Vanini, Valentina
AU  - Palmieri, Fabrizio
AU  - Gualano, Gina
AU  - Cuzzi, Gilda
AU  - Navarra, Assunta
AU  - Girardi, Enrico
AU  - Goletti, Delia
TI  - Evaluation of the accuracy of several immune experimental diagnostic tools to distinguish active tuberculosis from latent infection
AID  - 10.1183/13993003.congress-2015.OA3486
DP  - 2015 Sep 01
TA  - European Respiratory Journal
PG  - OA3486
VI  - 46
IP  - suppl 59
4099  - http://erj.ersjournals.com/content/46/suppl_59/OA3486.short
4100  - http://erj.ersjournals.com/content/46/suppl_59/OA3486.full
SO  - Eur Respir J2015 Sep 01; 46
AB  - Background: There is still not reliable tests to distinguish active tuberculosis (TB) from latent TB infection (LTBI) by an immune diagnostic test. QuantiFERON TB Gold (QFT) is used for LTBI diagnosis; however this test does not distinguish between LTBI and active TB. Recently, assays based on the response to antigens of latency as HBHA or Rv2628, or evaluation on RD1-specific T cells by cytometry of the modulation of the effector memory/central memory status has been evaluated.Aim: To evaluate in a low TB endemic country as Italy, the accuracy to discriminate active TB from LTBI using several immune diagnostic tests based on different TB antigens and on the modulation of the T cell effector memory status.Methods: Evaluation of the accuracy to distinguish active TB from LTBI in several cohorts of QFT-IT-positive subjects either HIV-uninfected (107) or HIV-infected subjects (27) with active TB, LTBI or enrolled as cured TB. Whole blood or peripheral blood cells were stimulated with RD1 and latency antigens. Interferon (IFN)-γ response was evaluated by ELISA and mono-polyfunctional cytokine expression (IFN-γ, IL-2, TNF-α) and differentiation markers by cytometry.Results: To predict active TB we used the combination of the different experimental-immune-based approaches by logistic regression followed by ROC analysis (AUC 0.96; 95% CI, 0.87-0.99, p<0.0001). We then identified a cut-off which led to an accuracy of 92.3% sensitivity and 95.2% specificity.Conclusions: This study proposes that a combination of several experimental approaches may help to distinguish TB disease from LTBI in a low TB endemic country.