Clinical Management and Outcomes of Nontuberculous Mycobacterial Infections in Solid Organ Transplant Recipients: A Multinational Case-control Study
Bibliographic record
Abstract
BACKGROUND: The management and outcomes of nontuberculous mycobacterial (NTM) infections in solid organ transplant (SOT) recipients are poorly characterized. We aimed to describe the management and 1-y mortality of these patients. METHODS: Retrospective, multinational, 1:2 matched case-control study included SOT recipients aged 12 y old or older diagnosed with NTM infection between January 1, 2008, and December 31, 2018. Controls were matched on transplanted organs, NTM treatment center, and posttransplant survival at least equal to the time to NTM diagnosis. The primary aim was 1-y mortality after NTM diagnosis. Differences between cases and controls were compared using the log-rank test, and Cox regression models were used to identify factors associated with mortality at 12 mo among cases. RESULTS: In 85 patients and 169 controls, the median age at the time of SOT was 54 y (interquartile range, 40-62 y), 59% were men, and the lungs were the most common site of infection after SOT (57.6%). One-year mortality was significantly higher in cases than in controls (20% versus 3%; P < 0.001), and higher mortality was associated with lung transplantation (hazard ratio 3.27; 95% confidence interval [1.1-9.77]; P = 0.034). Median time (interquartile range) from diagnosis to treatment initiation (20 [4-42] versus 11 [3-21] d) or the reduction of net immunosuppression (36% versus 45%, hazard ratio 1.35 [95% CI, 0.41-4.43], P = 0.618) did not differ between survivors and those who died. CONCLUSIONS: NTM disease in SOT recipients is associated with a higher mortality risk, especially among lung transplant recipients. Time to NTM treatment and reduction in net immunosuppression were not associated with mortality.
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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".