Higher Performance of QuantiFERON TB Compared to Tuberculin Skin Test in Latent Tuberculosis Infection Prospective Diagnosis
Bibliographic record
Abstract
Background: The Tuberculin skin test (TST) has been used for years in the latent tuberculosis infection (LTBI) diagnosis, but it has, well-documented, low sensitivity and specificity. Interferon-γ release assays (IGRA) has been reported to be more sensitive and specific than TST. Therefore, this study aimed to evaluate the performance of a commercial IGRA, QuantiFERON®-TB Gold In-Tube (QFT-GIT), comparatively to TST in LTBI diagnosis. Patients and Methods: This study included 238 patients who were candidate for an anti-TNF therapy. The screening for LTBI was performed by both TST and QFT-GIT test for all patients. In order to evaluate the strength of associations, the odds ratios (OR) together with 95% confidence intervals (CI) were calculated. The correlation between QFT-GIT and TST was evaluated using κ statistics. Results: Sixty-three (26.4%) sera were positive for QFT-GIT with a mean level of IFN-γ of about 1.18 IU/ml, while 81 (34%) patients were positive for TST. Agreement between QFT-GIT and TST was poor (37 QFT-GIT+/TST- and 55 QFT-GIT-/TST+), κ=0.09 (SD=0.065). The positivity of QFT-GIT was not influenced by BCG vaccination or by immunosuppression. Nevertheless, it was significantly associated to both history of an earlier tuberculosis disease (HETD) and its radiological sequel (RS), p=6E-7 and p=1E-8, respectively. Inversely, the TST results were not correlated to either HETD or RS, but the TST positivity was less frequent in immunosuppressed patients (45.5% vs. 73.9%), p=1E-5, OR (95% CI) = 0.29 [0.17-0.52]. Moreover, the extent of both the immunosuppression period and the time elapsed from the last BCG injection was significantly correlated to a lesser TST positivity, p=3E-12 and p=5E-7, respectively. Among the QFT-GIT-/TST+ patients (n=55) whom received an anti-TNF agent without any prophylactic treatment of LTBI, no tuberculosis was detected with a median follow-up of 78 weeks [56-109]. Conclusion: Our study suggests that the QFT-GIT has a higher performance comparatively to TST in the LTBI screening that is unaffected by either BCG vaccination or immunosuppression. Therefore, IGRAs has to replace TST especially in patients who are under consideration for an anti-TNF therapy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".