Association of statin use on survival outcomes of patients with early-stage HER2-positive breast cancer in the APHINITY trial
Bibliographic record
Abstract
PURPOSE: There is evidence that statins might improve the outcome of patients with breast cancer. The role of statins in patients with early HER2-positive breast cancer is unknown. Therefore, we explored the association between statin use and survival outcomes in early HER2-positive breast cancer patients in the phase III APHINITY trial (adjuvant pertuzumab/trastuzumab). METHODS: All patients (intent-to-treat population, n = 4804) were included (6.2 years median follow-up database). The primary objective was to investigate the association of statin use on invasive disease-free survival (IDFS), distant relapse-free interval (DRFI), and overall survival (OS). Patients who received statins at baseline, or started statins within 1 year from randomization were considered statin users. Survival curves were estimated using the Kaplan-Meier method. We used a Cox proportional hazards model for multivariate analysis. RESULTS: Overall, 423 (8.8%) patients were classified as statin users. They were older, more often postmenopausal, had a higher body mass index, more often diabetes, hypertension, coronary heart disease and hyperlipidemia, had smaller sized tumors, were treated more often with breast conserving surgery, and less often with anthracycline-containing regimens. Overall, 508 IDFS events (12.8% among statin users and 10.4% among non-statin users) and 272 deaths (8.5% and 5.4%, respectively) occurred. In multivariate analysis, statin use was not associated with IDFS (HR, 1.11; 95% CI, 0.80-1.52), DRFI (HR, 1.21; 95% CI, 0.81-1.81) nor OS (HR, 1.16; 95% CI, 0.78-1.73). CONCLUSION: In APHINITY, statin use was not associated with improved survival outcomes. These results must be interpreted with caution due to the exploratory nature of the analysis and the associated limitations.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".