Aromatase inhibitors and cardiac outcomes in women undergoing cardiac angiography after early breast cancer.
Bibliographic record
Abstract
558 Background: Data show that in post-menopausal women with early breast cancer, longer use of aromatase inhibitor (AI) is associated with increased odds of ischemic heart disease. Here we explore the association between adjuvant AI use and cardiac disease in women undergoing cardiac angiography after a diagnosis of early breast cancer. Methods: We linked a database of 7,681 women who underwent cardiac angiography at the University Clinical Center of Ljubljana between December 2004 and November 2010 with the Cancer Registry for Slovenia. Women with early breast cancer that subsequently underwent cardiac angiography were identified. Information on cardiovascular risk factors was retrieved from the patients’ charts and from discharge letters after cardiac angiography. The endpoint of interest was a diagnosis of ischemic heart disease or left ventricular dysfunction (IHD-LVD) without evidence of valvular heart disease at the time of angiography. Conditional, logistic regression was used to test for associations between variables. Results: Among 117 eligible women 75% (n=88) were postmenopausal and 62% (n=73) had hormonal receptor positive (HR+) disease. Of these 42% (n=31) were treated with AI. Overall, 48% (n=56) of women were found to have IHD-LVD. In patients with HR+ breast cancer, use of AIs was significantly associated with IHD-LVD as compared to tamoxifen alone (HR 2.50, 95% CI 1.01-6.29, p=0.046). For each year of AI therapy, there was a trend for higher odds of IHD-LVD (OR: 1.25, 95% CI 0.95-1.67, p=0.116). This effect appeared independent of age, body mass index, baseline hypertension, hypercholesterolemia, diabetes and heart disease or prior anthracyclines exposure. Among all patients, use of anthracyclines and left-sided irradiation was associated with non-significant increases in IHD-LVD (HR 2.37, 95% CI 0.89-6.09, p=0.45 and HR 1.28, 95% CI 0.69-2.40, p=0.44 respectively). Conclusions: Compared to tamoxifen, AIs are associated with a time dependent increase in IHD-LVD. This risk appears independent of other risk factors for heart disease. Anthracycline exposure and left breast or chest wall radiation showed non-significant associations with IHD-LVD in this small cohort.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".