ArticleBMC infectious diseases2026
Evaluation of TST/C-TST combination for latent tuberculosis infection screening: diagnostic performance and cost-effectiveness compared to TST/IGRAs.
Article in BMC infectious diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
backgroundThe interferon-gamma release assays (IGRAs) are characterized by their complexity and reliance on specific instrumentation. The recombinant Mycobacterium tuberculosis (MTB) fusion protein skin test (C-TST) utilizes the same MTB fusion protein as IGRAs. If the C-TST demonstrates consistent results and greater cost-effectiveness, it could potentially replace IGRAs and be used alongside the tuberculin skin test (TST) for the screening of latent tuberculosis infection (LTBI). This study aims to assess whether the TST/C-TST screening strategy offers superior diagnostic efficacy and cost-effectiveness compared to the conventional TST/IGRAs strategy, thereby evaluating the potential application of the combined C-TST and TST approach for LTBI screening.
methodsThe study population consisted of young males, aged 18 to 40 years, who attended the outpatient clinic of a hospital in 2023. These participants underwent TST and C-TST, followed by IGRAs between October and December 2023. The sensitivity, specificity, and reliability of the screening results were assessed. Furthermore, a hybrid decision tree and Markov model were utilized to evaluate the costs and health outcomes associated with five active screening strategies-TST, C-TST, IGRAs, TST combined with C-TST, and TST combined with IGRAs-from a societal perspective over a 20-year period.
resultsIn this study, valid results were obtained from 1412 participants: 202 individuals completed three tests, while 1210 completed two tests. The sensitivity for the TST, the C-TST, and IGRA (QFT-GIT) was 92.7%, 52.9%, and 58.8%, respectively, with corresponding specificities of 56.0%, 83.6%, and 83.6%. Notably, the C-TST and IGRA (QFT-GIT) exhibited high concordance, as indicated by a Kappa value of 0.869. However, through a simulation-based analysis of this single-center cohort, which consisted exclusively of male participants and included a small subgroup subjected to all three tests, and under predefined model assumptions, the combination of TST and C-TST emerged as a more effective screening strategy. This combination achieved an AUC of 0.821, achieved the optimal balance between accuracy and cost-effectiveness.
conclusionWithin the context of this study, the novel screening strategy that integrates TST and C-TST demonstrates promising efficacy and economic advantages when compared to the traditional TST/IGRAs approach for the detection of LTBI.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.