Risk Factors for Voriconazole Hepatotoxicity at 12 Weeks in Lung Transplant Recipients
Bibliographic record
Abstract
Voriconazole is commonly used for prophylaxis and treatment of invasive aspergillosis in lung transplant recipients. However, the use of voriconazole may at times be limited by the development of hepatotoxicity. Our goal is to determine predictors of voriconazole-associated hepatotoxicity in lung transplant recipients. We conducted a single center retrospective cohort study of lung transplant recipients from 2006 to 2010 who received voriconazole therapy. We compared characteristics of patients who developed hepatotoxicity and those who did not. One hundred five lung transplant recipients received voriconazole. Hepatotoxicity occurred in 51% (54/105) of patients and lead to discontinuation in 34% (36/105). In univariate analysis, age less than 40 years, cystic fibrosis, use of azathioprine, history of liver disease and early initiation of voriconazole were associated with hepatotoxicity. In multivariable logistic regression analysis, perioperative initiation of voriconazole (within 30 days of transplantation) was independently associated with hepatotoxicity (OR 4.37, 95% CI: 1.53-12.43, p = 0.006). The five risk factors identified in the univariate analysis were used to build a K-nearest neighbor algorithm predictive model for hepatotoxicity. This model predicted hepatotoxicity with an accuracy of 70%. Voriconazole therapy initiated within the first 30 days of transplantation is associated with a greater risk of developing hepatotoxicity.
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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 | 0.001 | 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 | 0.000 | 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) | 0.001 | 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".