Identifying high-risk drugs and demographic patterns in drug-induced liver injury from FAERS and CVARD analyses
Bibliographic record
Abstract
Drug-induced liver injury (DILI) is a major cause of acute liver failure, yet identifying associated drugs has been limited by the lack of large-scale analyses. This study addresses this gap by analyzing adverse event reports from the FDA Adverse Event Reporting System (FAERS) and the Canada Vigilance Adverse Reaction Database (CVARD) from 2012 to 2023. FAERS served as the primary database, while CVARD provided validation for the findings. We employed disproportionality analyses (ROR, PRR, BCPNN, MGPS) to identify drugs linked to DILI. Our analysis of 21,738 cases from FAERS identified 172 drugs with significant DILI signals, including high-risk drugs such as dapsone, isoniazid, and nitrofurantoin, which showed the strongest associations (BCPNN > 3). The most frequently implicated drug classes included antineoplastics, antibacterials, and direct-acting antivirals. Time-to-onset of DILI varied significantly across drug classes, with antibacterials exhibiting the shortest median onset (54 days) and immunosuppressants the longest (297 days). Gender and age were also found to be important risk factors, with higher DILI rates observed in females and older adults. Systemic drug administration, particularly through oral and intravenous routes, was most commonly associated with DILI, with serious outcomes reported in 55.9% of cases, including 30.8% requiring hospitalization. While these findings provide valuable insights into drug safety and clinical decision-making, the study's limitations, including potential biases from the spontaneous reporting nature of FAERS and CVARD, should be acknowledged. Overall, the findings highlight the importance of personalized monitoring and risk stratification to enhance medication safety and improve patient outcomes.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".