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Related Concept Videos

Variability: Analysis01:11

Variability: Analysis

191
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
191

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Effect of Variability on Interferon-Gamma Release Assay Performance: A Quantitative Analysis.

Won-Ki Min1

  • 1Department of Laboratory Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea; Future Strategy Division, SD Biosensor, Seoul, Korea.

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|July 23, 2025
PubMed
Summary

Interferon-gamma release assays (IGRAs) for latent tuberculosis infection (LTBI) show high variability. Expanding borderline zones in IGRA interpretation can reduce misclassification, but reducing manufacturing and analytical variability is crucial for reliable LTBI diagnosis.

Keywords:
False-negative reactionsFalse-positive reactionsInterferon-gamma release testsIntra-individual variabilityLatent tuberculosisPredictive value of testsReproducibility of resultsTuberculosis

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Area of Science:

  • Medical Diagnostics
  • Immunology
  • Infectious Diseases

Background:

  • Interferon-gamma release assays (IGRAs) are standard for latent tuberculosis infection (LTBI) detection.
  • Inherent variability in IGRAs challenges accurate diagnostic interpretation.

Purpose of the Study:

  • To evaluate the impact of coefficient of variation (CV) on IGRA false-positive, false-negative, conversion, and reversion rates.
  • To assess the effectiveness of expanding borderline zones in IGRA interpretation.

Main Methods:

  • Statistical modeling was used to simulate IGRA results across CVs of 20% to 100%.
  • False-positive, false-negative, conversion, and reversion rates were analyzed at various diagnostic cutoffs and borderline zones.

Main Results:

  • False-negative rates increased significantly with higher CVs (1.61% to 33.41% at 0.35 IU/mL).
  • Expanding the borderline zone from 0.20-0.70 IU/mL to 0.20-1.00 IU/mL reduced false-positive rates (max 3.16%) without impacting false-negative rates.
  • High rates of correct reversion and false conversion were observed within borderline zones, dependent on the cutoff used.

Conclusions:

  • Implementing borderline zones in IGRA interpretation can mitigate misclassification due to test variability.
  • Reducing variability in manufacturing, pre-analytical, and analytical procedures is essential for improving IGRA diagnostic reliability for LTBI.