Optimization and Interpretation of Serial QuantiFERON Testing to Measure Acquisition ofMycobacterium tuberculosisInfection

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Conversion from a negative to positive QuantiFERON-TB test is indicative of Mycobacterium tuberculosis (Mtb) infection, which predisposes individuals to tuberculosis disease. Interpretation of serial tests is confounded by immunological and technical variability.


To improve the consistency of serial QuantiFERON-TB testing algorithms and provide a data-driven definition of conversion.


Sources of QuantiFERON-TB variability were assessed, and optimal procedures were identified. Distributions of IFN-γ response levels were analyzed in healthy adolescents, Mtb-unexposed control subjects, and patients with pulmonary tuberculosis.

Measurements and Main Results:

Individuals with no known Mtb exposure had IFN-γ values less than 0.2 IU/ml. Among individuals with IFN-γ values less than 0.2 IU/ml, 0.2-0.34 IU/ml, 0.35-0.7 IU/ml, and greater than 0.7 IU/ml, tuberculin skin test positivity results were 15%, 53%, 66%, and 91% (P < 0.005), respectively. Together, these findings suggest that values less than 0.2 IU/ml were true negatives. In short-term serial testing, “uncertain” conversions, with at least one value within the uncertainty zone (0.2-0.7 IU/ml), were partly explained by technical assay variability. Individuals who had a change in QuantiFERON-TB IFN-γ values from less than 0.2 to greater than 0.7 IU/ml had 10-fold higher tuberculosis incidence rates than those who maintained values less than 0.2 IU/ml over 2 years (P = 0.0003). By contrast, “uncertain” converters were not at higher risk than nonconverters (P = 0.229). Eighty-seven percent of patients with active tuberculosis had IFN-γ values greater than 0.7 IU/ml, suggesting that these values are consistent with established Mtb infection.


Implementation of optimized procedures and a more rigorous QuantiFERON-TB conversion definition (an increase from IFN-γ <0.2 to >0.7 IU/ml) would allow more definitive detection of recent Mtb infection and potentially improve identification of those more likely to develop disease.

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