Improving Latent Tuberculosis Screening Amongst Hospitalized Patients Undergoing Initiation of Immunosuppression: A Cross-Sectional Analysis
Bibliographic record
Abstract
Background: Latent tuberculosis testing prior to immunosuppression is recommended by national and international guidelines. However, no structured process currently exists for testing adult patients at tertiary care hospitals in Calgary, Alberta, Canada. Additionally, there is limited data describing interferon-γ release assay, QuantiFERON® (QFT; QIAGEN, Hilden, Germany) among inpatients. In 2021, a QFT order set was implemented for hospitalized patients with the aim to facilitate rapid testing prior to immunosuppression. This study aimed to compare the proportion of in-hospital immunosuppression started prior to QFT collection before and after QFT order set implementation, and to assess variables associated with indeterminate test results. Methods: The authors performed a retrospective chart review of adult inpatients who underwent QFT at four acute care hospitals from 2020–2022. The Z-test was used to compare the proportion of pre-immunosuppression QFT testing pre- and post- order set implementation. Associations were analyzed using logistic regression. Results: A total of 639 inpatients had QFT testing. The most common indication for QFT testing was immunosuppression. The proportion of patients who began immunosuppression prior to QFT decreased following order set implementation (54% versus 45%; p=0.0388). Indeterminate QFT results were associated with immunosuppression initiation before QFT (p<0.005). In multivariable analysis, the odds of an indeterminate QFT increased for patients with low lymphocyte counts (adjusted odds ratio [OR]: 3.45; 95% CI: 1.49–7.69) or those who received prednisone ≥50 mg (adjusted OR: 1.84; 95% CI: 1.0–3.39). Conclusion: QFT should be performed before immunosuppression and, if possible, prior to hospitalization. Despite implementation of a QFT order set, immunosuppression was still frequently started before QFT collection. Further work is needed to identify barriers to early testing and evaluate strategies to optimize screening.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".