Risk Factors for Nontuberculous Mycobacteria Infections in Solid Organ Transplant Recipients: A Multinational Case-Control Study
Bibliographic record
Abstract
BACKGROUND: Risk factors for nontuberculous mycobacteria (NTM) infections after solid organ transplant (SOT) are not well characterized. Here we aimed to describe these factors. METHODS: Retrospective, multinational, 1:2 matched case-control study that included SOT recipients ≥12 years old diagnosed with NTM infection from 1 January 2008 to 31 December 2018. Controls were matched on transplanted organ, NTM treatment center, and post-transplant survival greater than or equal to the time to NTM diagnosis. Logistic regression on matched pairs was used to assess associations between risk factors and NTM infections. RESULTS: Analyses included 85 cases and 169 controls (59% male, 88% White, median age at time of SOT of 54 years [interquartile range {IQR} 40-62]). NTM infection occurred in kidney (42%), lung (35%), heart and liver (11% each), and pancreas transplant recipients (1%). Median time from transplant to infection was 21.6 months (IQR 5.3-55.2). Most underlying comorbidities were evenly distributed between groups; however, cases were older at the time of NTM diagnosis, more frequently on systemic corticosteroids and had a lower lymphocyte count (all P < .05). In the multivariable model, older age at transplant (adjusted odds ratio [aOR] 1.04; 95 confidence interval [CI], 1.01-1.07), hospital admission within 90 days (aOR, 3.14; 95% CI, 1.41-6.98), receipt of antifungals (aOR, 5.35; 95% CI, 1.7-16.91), and lymphocyte-specific antibodies (aOR, 7.73; 95% CI, 1.07-56.14), were associated with NTM infection. CONCLUSIONS: Risk of NTM infection in SOT recipients was associated with older age at SOT, prior hospital admission, receipt of antifungals or lymphocyte-specific antibodies. NTM infection should be considered in SOT patients with these risk factors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".