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Updated: Jan 2, 2026

Application of Long-term cultured Interferon-γ Enzyme-linked Immunospot Assay for Assessing Effector and Memory T Cell Responses in Cattle
Published on: July 11, 2015
Quantitative IFN-γ Release Assay and Tuberculin Skin Test Results to Predict Incident Tuberculosis. A Prospective
Rishi K Gupta1, Marc Lipman2,3,4, Charlotte Jackson1
1Institute for Global Health.
Abstract:
Rationale: Development of diagnostic tools with improved predictive value for tuberculosis (TB) is a global research priority.Objectives: We evaluated whether implementing higher diagnostic thresholds than currently recommended for QuantiFERON Gold-in-Tube (QFT-GIT), T-SPOT.TB, and the tuberculin skin test (TST) might improve prediction of incident TB.Methods: Follow-up of a UK cohort of 9,610 adult TB contacts and recent migrants was extended by relinkage to national TB surveillance records (median follow-up 4.7 yr). Incidence rates and rate ratios, sensitivities, specificities, and predictive values for incident TB were calculated according to ordinal strata for quantitative results of QFT-GIT, T-SPOT.TB, and TST (with adjustment for prior bacillus Calmette-Guérin [BCG] vaccination).Measurements and Main Results: For all tests, incidence rates and rate ratios increased with the magnitude of the test result (P < 0.0001). Over 3 years' follow-up, there was a modest increase in positive predictive value with the higher thresholds (3.0% for QFT-GIT ≥0.35 IU/ml vs. 3.6% for ≥4.00 IU/ml; 3.4% for T-SPOT.TB ≥5 spots vs. 5.0% for ≥50 spots; and 3.1% for BCG-adjusted TST ≥5 mm vs. 4.3% for ≥15 mm). As thresholds increased, sensitivity to detect incident TB waned for all tests (61.0% for QFT-GIT ≥0.35 IU/ml vs. 23.2% for ≥4.00 IU/ml; 65.4% for T-SPOT.TB ≥5 spots vs. 27.2% for ≥50 spots; 69.7% for BCG-adjusted TST ≥5 mm vs. 28.1% for ≥15 mm).Conclusions: Implementation of higher thresholds for QFT-GIT, T-SPOT.TB, and TST modestly increases positive predictive value for incident TB, but markedly reduces sensitivity. Novel biomarkers or validated multivariable risk algorithms are required to improve prediction of incident TB.
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