Drug-induced interstitial lung disease: a real-world pharmacovigilance study of the FDA Adverse Event Reporting System from 2004 to 2021
Bibliographic record
Abstract
Background: Drug-induced interstitial lung disease (DILD) is an increasingly common cause of morbidity and mortality. However, due to the lack of specificity, DILD detection remains an unsolved public health challenge. Objectives: For the first time, we aimed to examine DILD reports submitted to the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) to identify demographic characteristics and top drugs associated with DILD at a group level (including age, sex, drug class, and country stratification) and individual drug level. Design: A retrospective analysis of the FAERS database was examined by disproportionality analysis. Methods: We reviewed the FAERS database from 2004 to 2021, using search terms 'interstitial lung disease' and sorting cases by generic drug name. The reporting odds ratio, proportional reporting ratio, and Bayesian confidence propagation neural network were calculated as the measure of strength of association. Results: There were 32,821 DILD reports in the FAERS. After excluding reports without age, sex, or country data according to the specific measurement, the median age of patients was 68 (interquartile range: 59), 54.77% were male, and 46.00% of reports came from Japan. The top drug classes related to DILD in the FAERS were antineoplastic, followed by cardiovascular and antirheumatic agents, in varying order in different sexes. Fam-trastuzumab deruxtecan-nxki, ramucirumab, and eribulin were the top three drugs with the highest strength of association. We also found some drugs without DILD in the labels, such as amiodarone, temsirolimus, and ursodiol. There are significant differences in DILD reports in various countries. For example, the United States and France reported more cardiovascular agents, whereas Canada reported more antirheumatic agents. Conclusion: We found the top drugs and drug classes that were associated with DILD in the FAERS, which provides a real-world window for different ages, sexes, and countries to formulate precise pharmacovigilance policies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 teacher head, 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".