Effects of Adjuvant Sorafenib and Sunitinib on Cardiac Function in Renal Cell Carcinoma Patients without Overt Metastases: Results from ASSURE, ECOG 2805
Bibliographic record
Abstract
PURPOSE: Sunitinib and sorafenib are used widely in the treatment of renal cell carcinoma (RCC). These agents are associated with a significant incidence of cardiovascular (CV) dysfunction and left ventricular ejection fraction (LVEF) declines, observed largely in the metastatic setting. However, in the adjuvant population, the CV effects of these agents remain unknown. We prospectively defined the incidence of cardiotoxicity among resected, high-risk RCC patients treated with these agents. EXPERIMENTAL DESIGN: Sunitinib, sorafenib, or placebo was administered for up to 12 months in patients with high-risk, resected RCC. LVEF was measured by multigated acquisition (MUGA) scans at standard intervals. Additional CV adverse events were reported according to NCI Common Terminology Criteria for Adverse Events (CTCAE). RESULTS: Among 1,943 patients randomized, 1,599 had at least 1 post-baseline MUGA. Within 6 months, 21 patients (1.3%) experienced a cardiac event, defined as an LVEF decline from baseline that was >15% and below the institutional lower limit of normal. Nine of 513 patients (1.8%) were on sunitinib, 7 of 508 (1.4%) on sorafenib, and 5 of 578 (0.9%) on placebo (P = 0.28 and 0.56 comparing sunitinib and sorafenib to placebo, respectively). With dose interruption or adjustment, 16 of the 21 recovered their LVEF to >50%. The incidence of symptomatic heart failure, arrhythmia, or myocardial ischemia did not differ among groups. CONCLUSIONS: In the adjuvant setting, we prospectively define low incidence of cardiotoxicity with sunitinib and sorafenib. These findings may be related to close CV monitoring, or potentially to fewer CV comorbidities in our nonmetastatic population.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".