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Interpretation of the tuberculin skin test
D N Rose1, C B Schechter, J J Adler
1Department of Community Medicine, Mount Sinai School of Medicine, New York, New York, USA.
The tuberculin skin test (TST) demonstrates high accuracy in identifying tuberculosis infection. Optimal interpretation of TST results requires considering population prevalence and testing purpose, leading to varied cutpoints for positive results.
Area of Science:
- Epidemiology
- Infectious Disease Diagnostics
- Public Health
Background:
- The tuberculin skin test (TST), using purified protein derivative, is a key tool for tuberculosis screening.
- Traditional interpretation of TST results lacks nuance regarding population-specific characteristics.
- Modern approaches necessitate re-evaluating TST performance using contemporary statistical methods.
Purpose of the Study:
- To re-evaluate epidemiologic data of the TST using modern test characteristic analysis.
- To determine appropriate cutpoints for positive TST results across diverse populations.
- To calculate TST operating characteristics, including sensitivity, specificity, predictive values, and ROC curve analysis.
Main Methods:
- Utilized standard epidemiologic assumptions to differentiate Mycobacterium tuberculosis-infected from uninfected individuals.
- Employed data from U.S. Navy recruit and World Health Organization tuberculosis surveys.
- Applied Bayesian analysis for robust statistical interpretation.
Main Results:
- TST sensitivity ranged from 0.59 to 1.0, specificity from 0.95 to 1.0, and positive predictive value from 0.44 to 1.0, varying by cutpoint in the general U.S. population.
- The area under the receiver operating characteristic (ROC) curve was 0.997, indicating excellent discriminatory power.
- Optimal cutpoints for positivity varied significantly (2 mm to 16 mm) based on prevalence and testing objectives.
Conclusions:
- The TST exhibits superior operating characteristics compared to most screening and diagnostic tests for tuberculosis.
- Effective interpretation of TST requires considering disease prevalence and the specific goals of testing.
- Using variable cutpoints for different populations and interpreting induration size as a probability of infection enhances diagnostic accuracy.
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