Cardiovascular adverse events associated with BRAF versus BRAF/MEK inhibitor: Cross‐sectional and longitudinal analysis using two large national registries
Bibliographic record
Abstract
BACKGROUND: Cardiovascular adverse events (CVAEs) associated with BRAF inhibitors alone versus combination BRAF/MEK inhibitors are not fully understood. METHODS: This study included all adult patients who received BRAF inhibitors (vemurafenib, dabrafenib, encorafenib) or combinations BRAF/MEK inhibitors (vemurafenib/cobimetinib; dabrafenib/trametinib; encorafenib/binimetinib). We utilized the cross-sectional FDA's Adverse Events Reporting System (FAERS) and longitudinal Truven Health Analytics/IBM MarketScan database from 2011 to 2018. Various CVAEs, including arterial hypertension, heart failure (HF), and venous thromboembolism (VTE), were studied using adjusted regression techniques. RESULTS: In FAERS, 7752 AEs were reported (40% BRAF and 60% BRAF/MEK). Median age was 60 (IQR 49-69) years with 45% females and 97% with melanoma. Among these, 567 (7.4%) were cardiovascular adverse events (mortality rate 19%). Compared with monotherapy, combination therapy was associated with increased risk for HF (reporting odds ratio [ROR] = 1.62 (CI = 1.14-2.30); p = 0.007), arterial hypertension (ROR = 1.75 (CI = 1.12-2.89); p = 0.02) and VTE (ROR = 1.80 (CI = 1.12-2.89); p = 0.02). Marketscan had 657 patients with median age of 53 years (IQR 46-60), 39.3% female, and 88.7% with melanoma. There were 26.2% CVAEs (CI: 14.8%-36%) within 6 months of medication start in those receiving combination therapy versus 16.7% CVAEs (CI: 13.1%-20.2%) among those receiving monotherapy. Combination therapy was associated with CVAEs compared to monotherapy (adjusted HR: 1.56 (CI: 1.01-2.42); p = 0.045). CONCLUSIONS AND RELEVANCE: In two independent real-world cohorts, combination BRAF/MEK inhibitors were associated with increased CVAEs compared to monotherapy, especially HF, and hypertension.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".