Risk of Cardiovascular Events and Lipid Profile Change in Patients with Breast Cancer Taking Aromatase Inhibitor: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Cardiovascular disease (CVD) is one of the most common comorbidities in breast cancer survivors. Recently, the target population and treatment period for aromatase inhibitor (AI) treatment in breast cancer patients has been expanding. However, information on adverse CVD events from the long-term use of AI is still lacking. The aim of this study was to investigate the CVD side effects of AI treatment and to evaluate the changes in lipid profile during AI treatment. A systematic search of PubMed (Medline), EMBASE, and Cochrane Library databases reporting on cardiovascular outcomes or lipid profiles change in adult female breast cancer patients (>19 years old) with AI was performed. The pooled analysis of 25 studies showed that the prevalence rate of any type of cardiovascular disease was 6.08 per 100 persons (95% CI 2.91-10.31). Angina was the most common type of heart-related cardiovascular event accounting for 3.85 per 100 persons, followed by any type of stroke (3.34) and venous thromboembolism (2.95). Ischemic stroke (OR 1.39, 95% CI 1.07-1.81) and myocardial infarction (OR 1.30, 95% CI 0.88-1.93) were more common in AI compared with tamoxifen, whereas the prevalence of venous thromboembolism (OR 0.61, 95% CI 0.37-1) was significantly lower in the AI group. In addition, treatment with AI for 6-12 months showed a decrease in HDL-cholesterol and an increase in LDL-cholesterol and total cholesterol. Various CVDs can occur when using AI, and in particular, the risk of MI and ischemic stroke increases in comparison with the adverse effect of tamoxifen. The occurrence of CVD might be related to the deterioration of the lipid profile after AI treatment. Therefore, a customized individualization strategy considering each patient's CV risk factors is needed during AI treatment.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.035 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".