Cardiovascular adverse events associated with triptans for treatment of migraine: a pharmacovigilance study of the FDA adverse event reporting system (FAERS)
Bibliographic record
Abstract
The purpose of this study was to determine the relationship between triptans (sumatriptan, rizatriptan, and zolmitriptan) and cardiovascular (CV) adverse events with data from the FDA Adverse Event Reporting System (FAERS). FAERS database was used to collect data on triptans from 1997 to 2023. Disproportionality methods were utilized to quantify triptan-associated CV events and to identify the potential risk. The reporting odds ratio was used to identify the risk signals. CV outcomes related to age, sex, clinical results, and other factors were also examined for triptans; 820 reports involving the triptans were recognized as CV adverse events out of total of 12 699 reports that were gathered from on FAERS database. Women reported more CV adverse events with rizatriptan and zolmitriptan as compared to men. The CV adverse event risk was highest among individuals aged 18-64. Clinical outcome analysis showed that sumatriptan carries a higher CV risk than rizatriptan and zolmitriptan, and most deaths and serious cases have been documented for sumatriptan. The patients prescribed sumatriptan or zolmitriptan were at a higher risk of reporting CV events for chest pain and chest discomfort, compared to rizatriptan. This finding may provide support for the clinical observation and risk evaluation of triptan treatment.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".