Real-world disproportionality analysis of cardiac failure associated with novel antineoplastic agents in breast cancer: a pharmacovigilance study
Bibliographic record
Abstract
Background: Some antineoplastic agents have been implicated in cardiac failure (CF), but large-scale real-world data remain limited. This study aimed to assess disproportional reporting signals for CF associated with novel antineoplastic agents used in breast cancer treatment. Methods: A disproportionality analysis was conducted using individual case safety reports from the FDA Adverse Event Reporting System (FAERS, Jan 2004-Mar 2025), Canada Vigilance Adverse Reaction Database (CANADA, Jan 2004-Dec 2024), and Japanese Adverse Drug Event Report (JADER, Jan 2004-Oct 2024). Reports involving breast cancer patients treated with FDA-approved targeted agents were included. Reporting odds ratios (RORs), time-to-onset (TTO), and death proportion were evaluated by drug and class. Results: A total of 8,565 CF cases were identified across the three databases. Trastuzumab exhibited consistently strong signals (FAERS: ROR 2.94; CANADA: 6.15; JADER: 7.05), with pertuzumab and everolimus showing variable significance. CDK4/6 inhibitors and immune checkpoint inhibitors (ICIs) demonstrated low or inverse RORs. Median TTO was longest for monoclonal antibodies, and shorter for ADCs and ICIs. Everolimus (20.1%), sacituzumab govitecan (22.6%), and atezolizumab (23.5%) showed the highest death proportions, while trastuzumab had a lower proportion (8.75%). HER2-targeted agents had higher reporting signal of CF than traditional chemotherapies. Conclusion: This study underscores drug-specific differences in CF risk among breast cancer therapies and highlights the importance of individualized cardiovascular risk assessment during treatment planning and monitoring.
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".