Screening for Latent Tuberculosis Infection in Solid Organ Transplant Recipients to Predict Active Disease: A Systematic Review and Meta-Analysis of Diagnostic Studies
Bibliographic record
Abstract
Abstract Background This is a systematic review and meta-analysis of diagnostic test accuracy studies to assess the predictive value of both tuberculin skin test (TST) and interferon-gamma release assays (IGRA) for active tuberculosis (TB) among solid organ transplantation (SOT) recipients. Methods Medline, Embase, and the CENTRAL databases were searched from 1946 until June 30, 2022. Two independent assessors extracted data from studies. Sensitivity analyses were performed to investigate the effect of studies with high or low risk of bias. Methodological quality of each publication was assessed using QUADAS-2. Results A total of 43 studies (36 403 patients) with patients who were screened for latent TB infection (LTBI) and who underwent SOT were included: 18 were comparative and 25 noncomparative (19 TST, 6 QuantiFERON-TB Gold In-Tube [QFT-GIT]). For IGRA tests taken together, positive predictive value (PPV) and negative predictive value (NPV) were 1.2% and 99.6%, respectively. For TST, PPV was 2.13% and NPV was 95.5%. Overall, PPV is higher when TB burden is higher, regardless of test type, although still low in absolute terms. Incidence of active TB was similar between studies using LTBI prophylaxis (mean incidence 1.22%; 95% confidence interval [CI], .2179–2.221) and those not using prophylaxis (mean incidence 1.045%; 95% CI, 0.2731–1.817; P = .7717). Strengths of this study include the large number of studies available from multiple different countries; limitations include absence of gold standard for diagnosis of latent TB and low incidence of active TB. Conclusions We found both TST and IGRA had a low PPV and high NPV for the development of active TB posttransplant. Further studies are needed to better understand how to prevent active TB in the SOT population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.054 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.040 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".