Gamma Interferon Release Assays for Detection of Mycobacterium tuberculosis Infection

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Published in	Clinical Microbiology Reviews Vol. 27; no. 1; pp. 3 - 20
Main Authors	Pai, Madhukar, Denkinger, Claudia M., Kik, Sandra V., Rangaka, Molebogeng X., Zwerling, Alice, Oxlade, Olivia, Metcalfe, John Z., Cattamanchi, Adithya, Dowdy, David W., Dheda, Keertan, Banaei, Niaz
Format	Journal Article
Language	English
Published	Washington, DC American Society for Microbiology 01.01.2014
Subjects	Bacteriology Biological and medical sciences Fundamental and applied biological sciences. Psychology Humans Immunologic Tests - methods Immunologic Tests - standards Infectious diseases Interferon-gamma - immunology Latent Tuberculosis - diagnosis Medical sciences Microbiology Miscellaneous Mycobacterium tuberculosis - immunology Predictive Value of Tests Reproducibility of Results Reviews Sensitivity and Specificity Tuberculosis - diagnosis Cytokine Review Mycobacterial infection Infection Mycobacterium tuberculosis Mycobacteriales Bacteriosis Mycobacteriaceae Bacteria Actinomycetes Detection Release Gamma interferon
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Abstract	Classifications Services CMR Citing Articles Google Scholar PubMed Related Content Social Bookmarking CiteULike Delicious Digg Facebook Google+ Mendeley Reddit StumbleUpon Twitter current issue CMR About CMR Subscribers Authors Reviewers Advertisers Inquiries from the Press Permissions & Commercial Reprints ASM Journals Public Access Policy CMR RSS Feeds 1752 N Street N.W. • Washington DC 20036 202.737.3600 • 202.942.9355 fax • journals@asmusa.org Print ISSN: 0893-8512 Online ISSN: 1098-6618 Copyright © 2014 by the American Society for Microbiology. For an alternate route to CMR .asm.org, visit: CMR
AbstractList	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-γ) 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. Classifications Services CMR Citing Articles Google Scholar PubMed Related Content Social Bookmarking CiteULike Delicious Digg Facebook Google+ Mendeley Reddit StumbleUpon Twitter current issue CMR About CMR Subscribers Authors Reviewers Advertisers Inquiries from the Press Permissions & Commercial Reprints ASM Journals Public Access Policy CMR RSS Feeds 1752 N Street N.W. • Washington DC 20036 202.737.3600 • 202.942.9355 fax • journals@asmusa.org Print ISSN: 0893-8512 Online ISSN: 1098-6618 Copyright © 2014 by the American Society for Microbiology. For an alternate route to CMR .asm.org, visit: CMR 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-γ) 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.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-γ) 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. 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-γ) 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.
Author	Claudia M. Denkinger Olivia Oxlade Madhukar Pai Sandra V. Kik Adithya Cattamanchi John Z. Metcalfe Molebogeng X. Rangaka Keertan Dheda David W. Dowdy Alice Zwerling Niaz Banaei
Author_xml	– sequence: 1 givenname: Madhukar surname: Pai fullname: Pai, Madhukar organization: McGill International TB Centre and Department of Epidemiology & Biostatistics, McGill University, Montreal, Canada – sequence: 2 givenname: Claudia M. surname: Denkinger fullname: Denkinger, Claudia M. organization: McGill International TB Centre and Department of Epidemiology & Biostatistics, McGill University, Montreal, Canada, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA – sequence: 3 givenname: Sandra V. surname: Kik fullname: Kik, Sandra V. organization: McGill International TB Centre and Department of Epidemiology & Biostatistics, McGill University, Montreal, Canada – sequence: 4 givenname: Molebogeng X. surname: Rangaka fullname: Rangaka, Molebogeng X. organization: Institute of Infectious Diseases and Molecular Medicine and Centre for Infectious Diseases and Epidemiology Research, University of Cape Town, Cape Town, South Africa – sequence: 5 givenname: Alice surname: Zwerling fullname: Zwerling, Alice organization: Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA – sequence: 6 givenname: Olivia surname: Oxlade fullname: Oxlade, Olivia organization: Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA – sequence: 7 givenname: John Z. surname: Metcalfe fullname: Metcalfe, John Z. organization: Division of Pulmonary and Critical Care Medicine, San Francisco General Hospital, University of California, San Francisco, San Francisco, California, USA – sequence: 8 givenname: Adithya surname: Cattamanchi fullname: Cattamanchi, Adithya organization: Division of Pulmonary and Critical Care Medicine, San Francisco General Hospital, University of California, San Francisco, San Francisco, California, USA – sequence: 9 givenname: David W. surname: Dowdy fullname: Dowdy, David W. organization: Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA – sequence: 10 givenname: Keertan surname: Dheda fullname: Dheda, Keertan organization: Department of Medicine and UCT Lung Institute, University of Cape Town, Cape Town, South Africa – sequence: 11 givenname: Niaz surname: Banaei fullname: Banaei, Niaz organization: Department of Pathology and Medicine, Stanford University School of Medicine, Palo Alto, California, USA
BackLink	http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=28293366$$DView record in Pascal Francis https://www.ncbi.nlm.nih.gov/pubmed/24396134$$D View this record in MEDLINE/PubMed
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Keywords	Cytokine Review Mycobacterial infection Infection Mycobacterium tuberculosis Mycobacteriales Bacteriosis Mycobacteriaceae Bacteria Actinomycetes Detection Release Gamma interferon
Language	English
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PublicationTitle	Clinical Microbiology Reviews
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PublicationYear	2014
Publisher	American Society for Microbiology
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Snippet	Classifications Services CMR Citing Articles Google Scholar PubMed Related Content Social Bookmarking CiteULike Delicious Digg Facebook Google+ Mendeley Reddit... Identification and treatment of latent tuberculosis infection (LTBI) can substantially reduce the risk of developing active disease. However, there is no...
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SubjectTerms	Bacteriology Biological and medical sciences Fundamental and applied biological sciences. Psychology Humans Immunologic Tests - methods Immunologic Tests - standards Infectious diseases Interferon-gamma - immunology Latent Tuberculosis - diagnosis Medical sciences Microbiology Miscellaneous Mycobacterium tuberculosis - immunology Predictive Value of Tests Reproducibility of Results Reviews Sensitivity and Specificity Tuberculosis - diagnosis
Title	Gamma Interferon Release Assays for Detection of Mycobacterium tuberculosis Infection
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