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.

Diagnostic accuracy of adenosine deaminase for pleural tuberculosis in a low prevalence setting: A machine learning approach within a 7-year prospective multi-center study.

OBJECTIVE: To analyze the performance of adenosine deaminase in pleural fluid combined with other parameters routinely measured in clinical practice and assisted by machine learning algorithms for the diagnosis of pleural tuberculosis in a low prevalence setting, and secondly, to identify effusions that are non-tuberculous and most likely malignant. PATIENTS AND METHODS: We prospectively analyzed 230 consecutive patients diagnosed with lymphocytic exudative pleural effusion from March 2013 to June 2020. Diagnosis according to the composite reference standard was achieved in all cases. Pre-test probability of pleural tuberculosis was 3.8% throughout the study period. Parameters included were: levels of adenosine deaminase, pH, glucose, proteins, and lactate dehydrogenase, red and white cell counts and lymphocyte percentage in pleural fluid, as well as age. We tested six different machine learning-based classifiers to categorize the patients. Two different classifications were performed: a) tuberculous/non-tuberculous and b) tuberculous/malignant/other. RESULTS: Out of a total of 230 patients with pleural effusion included in the study, 124 were diagnosed with malignant effusion and 44 with pleural tuberculosis, while 62 were given other diagnoses. In the tuberculous/non-tuberculous classification, and taking into account the validation predictions, the support vector machine yielded the best result: an AUC of 0.98, accuracy of 97%, sensitivity of 91%, and specificity of 98%, whilst in the tuberculous/malignant/other classification, this type of classifier yielded an overall accuracy of 80%. With this three-class classifier, the same sensitivity and specificity was achieved in the tuberculous/other classification, but it also allowed the correct classification of 90% of malignant cases. CONCLUSION: The level of adenosine deaminase in pleural fluid together with cell count, other routine biochemical parameters and age, combined with a machine-learning approach, is suitable for the diagnosis of pleural tuberculosis in a low prevalence scenario. Secondly, non-tuberculous effusions that are suspected to be malignant may also be identified with adequate accuracy.