QuantiFERON-TB Gold In-Tube as a Confirmatory Test for Tuberculin Skin Test in Tuberculosis Contact Tracing: A Noninferiority Clinical Trial.

Background: Screening strategies based on interferon-γ release assays in tuberculosis contact tracing may reduce the need for preventive therapy without increasing subsequent active disease. Methods: We conducted an open-label, randomized trial to test the noninferiority of a 2-step strategy with the tuberculin skin test (TST) followed by QuantiFERON-TB Gold In-Tube (QFT-GIT) as a confirmatory test (the TST/QFT arm) to the standard TST-alone strategy (TST arm) for targeting preventive therapy in household contacts of patients with tuberculosis. Participants were followed for 24 months after randomization. The primary endpoint was the development of tuberculosis, with a noninferiority margin of 1.5 percentage points. Results: A total of 871 contacts were randomized. Four contacts in the TST arm and 2 in the TST/QFT arm developed tuberculosis. In the modified intention-to-treat analysis, this accounted for 0.99% in the TST arm and 0.51% in the TST/QFT arm (-0.48% difference; 97.5% confidence interval [CI], -1.86% to 0.90%); in the per-protocol analysis, the corresponding rates were 1.67% and 0.82% in the TST and TST/QFT arms, respectively (-0.85% difference; 97.5% CI, -3.14% to 1.43%). Of the 792 contacts analyzed, 65.3% in the TST arm and 42.2% in the TST/QFT arm were diagnosed with tuberculosis infection (23.1% difference; 95% CI, 16.4% to 30.0%). Conclusions: In low-incidence settings, screening household contacts with the TST and using QFT-GIT as a confirmatory test is not inferior to TST-alone for preventing active tuberculosis, allowing a safe reduction of preventive treatments. Clinical Trials Registration: NCT01223534.

An RNA-seq Based Machine Learning Approach Identifies Latent Tuberculosis Patients With an Active Tuberculosis Profile.

A better understanding of the response against Tuberculosis (TB) infection is required to accurately identify the individuals with an active or a latent TB infection (LTBI) and also those LTBI patients at higher risk of developing active TB. In this work, we have used the information obtained from studying the gene expression profile of active TB patients and their infected -LTBI- or uninfected -NoTBI- contacts, recruited in Spain and Mozambique, to build a class-prediction model that identifies individuals with a TB infection profile. Following this approach, we have identified several genes and metabolic pathways that provide important information of the immune mechanisms triggered against TB infection. As a novelty of our work, a combination of this class-prediction model and the direct measurement of different immunological parameters, was used to identify a subset of LTBI contacts (called TB-like) whose transcriptional and immunological profiles are suggestive of infection with a higher probability of developing active TB. Validation of this novel approach to identifying LTBI individuals with the highest risk of active TB disease merits further longitudinal studies on larger cohorts in TB endemic areas.