A real-world pharmacovigilance analysis of the risk of retinal artery occlusion from medication use
Why this work is in the frame
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Bibliographic record
Abstract
Purpose The risk of retinal artery occlusion (RAO) as related to specific drug use is unclear. Using the Food and Drug Administration Adverse Event Reporting System (FAERS), we aimed to comprehensively elicit a list of FDA-approved drugs overreported for RAO. Design Retrospective, population-based pharmacovigilance study. Methods Pharmacovigilance data were sourced from the FAERS database between October 2003 and March 2024 using Open Vigil 2.1 (Kiel, Germany) software. FDA-approved pharmacological agents which were recorded as the primary suspect drug for at least 10 reports of RAO were included. Disproportionality analyses were performed to identify positive adverse drug reaction signals by comparing drug-specific reports of RAO to the background rate of RAO reports across all other drugs in the database. Results Out of 12,345,128 adverse events reported to the FAERS database during the study period, 1,461 (0.01%) were identified as cases of RAO. Most primary suspect drugs were indicated for eye disorders (20.7%, n=303/1,461), neoplasms (11.4%, n=166/1,461), or musculoskeletal and connective tissue disorders (7.2%, n=105/1,461). Notably, brolucizumab and tranexamic acid were significantly overreported for RAO events. These were followed by melphalan, triamcinolone, aflibercept, ranibizumab, lidocaine, sildenafil, epinephrine, bupivacaine, and rofecoxib. Conclusion Several primary suspect drugs showed disproportionately high reports of RAO in the FAERS database; however, some of these medications are indicated for conditions associated with a hypercoagulable state, a significant risk factor for RAO. These findings underscore the need for continued pharmacovigilance efforts to distinguish potential drug-related effects from the influence of underlying disease.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it