期刊
CLINICAL MICROBIOLOGY REVIEWS
卷 27, 期 1, 页码 3-20出版社
AMER SOC MICROBIOLOGY
DOI: 10.1128/CMR.00034-13
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资金
- Canadian Institutes of Health Research (CIHR)
- European and Developing Countries Clinical Trials Partnership (EDCTP) (TB-NEAT grant)
- CIHR
- Fonds de Recherche du Quebec-Sante
- Richard Tomlinson Fellowship at McGill University
- Burroughs-Wellcome Fund from the American Society of Tropical Medicine and Hygiene
- Hasso-Plattner Fellowship at the University of Cape Town
- U.S. National Institutes of Health [K23 HL094141, K23 AI094251]
- EDCTP (TB-NEAT grant)
- Cellestis
- Oxford Immunotec
- NATIONAL HEART, LUNG, AND BLOOD INSTITUTE [K23HL094141] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES [K23AI094251] Funding Source: NIH RePORTER
Identification and treatment of latent tuberculosis infection (LTBI) can substantially reduce the risk of developing active disease. However, there is no diagnostic gold standard for LTBI. Two tests are available for identification of LTBI: the tuberculin skin test (TST) and the gamma interferon (IFN-gamma) release assay (IGRA). Evidence suggests that both TST and IGRA are acceptable but imperfect tests. They represent indirect markers of Mycobacterium tuberculosis exposure and indicate a cellular immune response to M. tuberculosis. Neither test can accurately differentiate between LTBI and active TB, distinguish reactivation from reinfection, or resolve the various stages within the spectrum of M. tuberculosis infection. Both TST and IGRA have reduced sensitivity in immunocompromised patients and have low predictive value for progression to active TB. To maximize the positive predictive value of existing tests, LTBI screening should be reserved for those who are at sufficiently high risk of progressing to disease. Such high-risk individuals may be identifiable by using multivariable risk prediction models that incorporate test results with risk factors and using serial testing to resolve underlying phenotypes. In the longer term, basic research is necessary to identify highly predictive biomarkers.
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