Statin Use May Be Associated With a Lower Risk of Invasive Aspergillosis in Lung Transplant Recipients
Bibliographic record
Abstract
BACKGROUND: Statins are competitive inhibitors of 3-hydroxy-3methylglutaryl coenzyme A reductase (HMG-CoA reductase) that catalyses HMG-CoA conversion to mevalonate, a process involved in synthesizing cholesterol in humans and ergosterol in fungi. The effect of statin use on the risk of development of invasive aspergillosis (IA) in lung transplant recipients (LTRs) is not well documented. METHODS: This retrospective study included LTRs from 2010 to 2017 who were followed for one-year post-transplant. Proven or probable IA was diagnosed as per ISHLT criteria. We performed a multivariable Cox proportional hazards model of the association between IA and statin use (minimum of 2 weeks duration prior to IA), adjusting for other known IA risk factors. RESULTS: We identified 785 LTRs, 44% female, mean age 53 years old, the most common underlying disease being pulmonary fibrosis (23.8%). In total, 451 LTRs (57%) received statins post-transplant, atorvastatin was the most commonly used statin (68%). The mean duration of statins post-transplant was 347 days (interquartile range [IQR]: 305 to 346). And 55 (7%) LTRs developed IA in the first-year post-transplant. Out of these 55 LTRs, 9 (16.3%) had received statin before developing IA. In multivariable analysis, statin use was independently associated with a lower risk of IA (P = .002, SHR 0.30, 95% confidence interval [CI] 95% .14-.64). Statin use was also associated with a lower incidence of post-transplant Aspergillus colonization, 114 (34%) in the no statin group vs 123 (27%) in the statin group (P = .038). CONCLUSIONS: The use of statin for a minimum of two weeks during the first-year post-transplant was associated with a 70% risk reduction of IA in LTRs.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".