Cardiovascular Adverse Events Associated with Prostate Cancer Treatment: A Disproportionality Analysis from the Food and Drug Administration Adverse Event Reporting System Database
Bibliographic record
Abstract
Background/Objectives: Several drugs used to treat prostate cancer have been reported to cause cardiovascular adverse events, and this study sought to identify the real-world risk. Methods: This study utilized real-world data from the FAERS to analyze the association between prostate cancer treatment and cardiovascular adverse events. It evaluated men treated with LHRH agonists and antagonists, antiandrogens, androgen synthesis inhibitors, and PARP inhibitors from 2003 to 2023. This study included patients treated with leuprolide, goserelin, triptorelin, degarelix, relugolix, bicalutamide, flutamide, apalutamide, nilutamide, abiraterone, enzalutamide, olaparib, rucaparib, talazoparib, and niraparib. The main outcome measure was the reported odds ratio (ROR) of adverse cardiovascular event associated with these treatments. Results: Among the 4,049,329 unique adverse event reports, 4391 cardiovascular events were identified. Leuprolide (ROR 0.481, 95% CI: 0.423–0.547), triptorelin (ROR 0.527, 95% CI: 0.305–0.909), enzalutamide (ROR 0.393, 95% CI: 0.341–0.452), and olaparib (ROR 0.145, 95% CI: 0.054–0.386) reduced the risk of myocardial infarction. Goserelin increased the risk of myocardial infarction (ROR 2.235, 95% CI: 1.367–3.654). Degarelix and relugolix both increased the risk of heart failure (ROR 3.136, 95% CI: 2.186–4.497), and enzalutamide was associated with an increased risk of heart failure (ROR 1.305, 95% CI: 1.135–1.501). Bicalutamide increased the risk of unstable angina (ROR 3.019, 95% CI: 1.621–5.622) and heart failure (ROR 3.730, 95% CI: 3.085–4.510). Niraparib increased the risk of hypertension (ROR 4.154, 95% CI: 1.709–10.092). Conclusions: These findings underscore the need for clinicians to monitor cardiac complications in patients undergoing these therapies.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".