Serial testing for latent tuberculosis using QuantiFERON-TB Gold In-Tube: A Markov model

Sci Rep. 2016 Jul 29:6:30781. doi: 10.1038/srep30781.

Abstract

Healthcare workers (HCWs) in low-incidence settings are often serially tested for latent TB infection (LTBI) with the QuantiFERON-TB Gold In-Tube (QFT) assay, which exhibits frequent conversions and reversions. The clinical impact of such variability on serial testing remains unknown. We used a microsimulation Markov model that accounts for major sources of variability to project diagnostic outcomes in a simulated North American HCW cohort. Serial testing using a single QFT with the recommended conversion cutoff (IFN-g > 0.35 IU/mL) resulted in 24.6% (95% uncertainty range, UR: 23.8-25.5) of the entire population testing false-positive over ten years. Raising the cutoff to >1.0 IU/mL or confirming initial positive results with a (presumed independent) second test reduced this false-positive percentage to 2.3% (95%UR: 2.0-2.6%) or 4.1% (95%UR: 3.7-4.5%), but also reduced the proportion of true incident infections detected within the first year of infection from 76.5% (95%UR: 66.3-84.6%) to 54.8% (95%UR: 44.6-64.5%) or 61.5% (95%UR: 51.6-70.9%), respectively. Serial QFT testing of HCWs in North America may result in tremendous over-diagnosis and over-treatment of LTBI, with nearly thirty false-positives for every true infection diagnosed. Using higher cutoffs for conversion or confirmatory tests (for initial positives) can mitigate these effects, but will also diagnose fewer true infections.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Cohort Studies
  • False Positive Reactions
  • Female
  • Health Personnel*
  • Humans
  • Incidence
  • Interferon-gamma Release Tests / methods*
  • Latent Tuberculosis / diagnosis*
  • Latent Tuberculosis / epidemiology
  • Male
  • Markov Chains
  • Mass Screening
  • Medical Overuse / statistics & numerical data
  • Models, Statistical*
  • North America / epidemiology
  • Sensitivity and Specificity

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