What Tool for Diagnosis of Latent Tuberculosis Infection in Developing Country with Tuberculosis High Burden: Interferon Gamma Release Assays <i>versus</i> Tuberculin Skin Test in Burkina Faso
Bibliographic record
Abstract
Background: The diagnosis and treatment of active tuberculosis and the detection/management of latent tuberculosis infection (LTBI) cases are the two main strategies for the TB control, particularly in endemic countries. Tuberculin skin test (TST) and Interferon Gamma Release Assays (IGRAs) are tools for detection of LTBI. The objective of this study was to evaluate the performance of the TST and QuantiFERON-TB Gold Plus® (QTF-Plus) and to identify a threshold for TST in best agreement with QTF-Plus for LTBI detection in a high TB burden setting. Methods: In July 2020, a cross-sectional analytical study was performed for QFT-Plus using blood samples and TST in 101 individuals with a high risk of TB living in Bobo-Dioulasso, Burkina Faso. A crude comparison between both tests was done and receiver operating characteristic curve was generated to determine TST’s threshold. TST sensitivity, specificity, predictive values and accuracy were calculated. Adjusted agreement between TST and QFT-Plus was evaluated. Results: With the minimum threshold of positivity set at 5 mm for TST, the overall agreement between the latter and QFT-Plus was poor with a Kappa coefficient (κ) rated at 0.319 (95% CI: 0.131 - 0.508). This cut-off yielded a sensitivity of 94.12% (95% CI: 88.53 - 99.71), and very poor specificity of 36.4% (95% IC: 25.0 - 47.80). However, an adjusted cut-off set at 11 mm gave a better specificity of 72.73% (95% CI: 62.1 - 83.30) of TST and improved the PPV (86%). Moreover, concordance between both tests was improved with κ at 0.56 (95% CI: 0.385 - 0.728) and 80.20% of accuracy. Factors associated with discordance between TST (11 mm) and QFT-Plus results were BCG vaccination, OR = 7.53 (95% CI: 1.43 - 139.25), p = 0.05 and chronic cough, OR = 5.07 (95% CI: 1.27 - 20.43), p = 0.01. Conclusions: This study showed that using a minimal cut-off of 11mm for TST significantly improved the concordance between QTF-Plus (IGRA) and TST. Using the cut-off TST of 11 mm would be ideal in low-income countries with a high TB burden, taking into account factors that could contribute to the discrepancy of results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".