Safety assessment of tafamidis: a real-world adverse event analysis from the FAERS database
Bibliographic record
Abstract
BACKGROUND: Tafamidis emerged as the first FDA-approved drug for treating termed amyloid fibrils. This study aims to analyze adverse events (AEs) related to tafamidis from the second quarter (Q2) of 2019 to the fourth quarter (Q4) of 2023 from the FDA adverse event reporting system (FAERS) database. RESEARCH DESIGN AND METHODS: The AE data related to tafamidis from 2019 Q2 to 2023 Q4 were collected and standardized. Various signal quantification techniques, including reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network, and multi-item gamma Poisson shrinker, were employed for analysis. RESULTS: Among the 8742 AE reports with tafamidis as the primary suspected drug, 180 preferred terms of AEs spanning 27 different system organ classes were identified. Unique adverse events to tafamidis included renal and urinary disorders and ear and labyrinth disorders, with not mentioned in the official drug label. Additionally, uncommon but significantly strong AE signals, such as blood culture positive and acquired hemophilia, were observed. Common AEs with relatively high occurrence rates included death, off-label use, fall, hypoacusis, and dysphagia. CONCLUSION: These findings offer valuable insights for optimizing the use of tafamidis and reducing potential side effects, thereby facilitating its safe use in clinical settings.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".