Evaluating interferon-gamma release assays for routine screening of health care workers for tuberculosis infection
Bibliographic record
Abstract
Tuberculosis (TB) continues to pose a global health threat, and was responsible for 1.4 million deaths worldwide in 2010.1 Health care workers (HCWs) are at risk of TB exposure, infection and disease. Effective screening of HCWs for latent TB infection (LTBI) is a critical element of TB control programs in Canada and elsewhere, and requires accurate and reliable tests to diagnose and predict progression to active disease. Recently, novel blood-based assays called interferon-gamma release assays (IGRAs) have been introduced for the diagnosis of LTBI, as an alternative to the tuberculin skin test (TST). IGRAs offer many advantages over the TST, but in the absence of a gold standard for LTBI, their evaluation is problematic. The use of IGRAs for the screening of HCWs remains controversial, and it is unclear how these novel tests can be incorporated into TB infection control programs. The overall objective of this doctoral thesis was to evaluate whether an IGRA (QuantiFERON-TB Gold In-Tube test [QFT]) could be used to identify LTBI in HCWs undergoing routine occupational TB screening in high (India) and low (Canada) TB incidence countries. This manuscript-based thesis includes 4 manuscripts: 1. Interferon-gamma release assays for tuberculosis screening of healthcare workers: a systematic review (published in Thorax 2012) 2. TB screening in Canadian health care workers using interferon-gamma release assays (published in PLoS One 2012) 3. Repeat TB screening with interferon-gamma release assays in Canadian health care workers: conversions or unexplained variability? 4. Trajectories of tuberculosis-specific interferon-gamma release assay responses among medical and nursing students in rural IndiaIn the systematic review (manuscript 1), we found a total of 50 published studies that evaluated IGRAs in HCWs. We found large variations in rates of conversions and reversions among serial testing studies in HCWs and little information on their association with TB exposure. Among the cohort of 388 Canadian HCWs (manuscript 2), we found low prevalence of positivity for both TST and QFT, but high rates of unexplained test discordance between TST and QFT. QFT test positivity was not associated with occupational TB exposure in the cross-sectional analysis. Upon repeat, annual screening in Canadian HCWs (manuscript 3), we found high rates of QFT conversions and reversions, which could not be explained by recent TB exposure or treatment. Finally, among the Indian HCW cohort (manuscript 4), we saw high within-person variability in interferon-gamma response over time, that could not be explained by TB exposure, and a high rate of QFT reversions in the absence of treatment. Alternative QFT conversion definitions were evaluated in both the Canadian and Indian cohorts. Alternative definitions estimated reduced rates of QFT conversions, but showed no greater association with TB exposure than the conventional definition. Indian HCWs were classified into patterns of change over time based on interferon-gamma responses, and while 'stable converters' were associated with exposure to TB in the hospital prior to enrolment, the prognosis of HCWs with these 'patterns' remains unclear.Overall, because of the dynamic nature of IGRAs, high rates of conversions and reversions, and the lack of any strong association with recent TB exposure, our data suggest that IGRAs may not be well suited for routine serial testing of HCWs. Their implementation in existing HCW screening programs should be done, if at all, with caution, particularly with respect to the interpretation of conversions and reversions.
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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 | 0.017 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".