Cardiovascular outcomes in breast cancer survivors: a systematic review and meta-analysis
Bibliographic record
Abstract
AIMS: It is unclear whether the future risk of cardiovascular events in breast cancer (Bc) survivors is greater than in the general population. This meta-analysis quantifies the risk of cardiovascular disease development in Bc patients, compared to the risk in a general matched cancer-free population, and reports the incidence of cardiovascular events in patients with Bc. METHODS AND RESULTS: We searched PubMed, Scopus, and Web of Science databases (up to 23 March 2022) for observational studies and post hoc analyses of randomized controlled trials. Cardiovascular death, heart failure (HF), atrial fibrillation (AF), coronary artery disease (CAD), myocardial infarction (MI), and stroke were the individual endpoints for our meta-analysis. We pooled incidence rates (IRs) and risk in hazard ratios (HRs), using random-effects meta-analyses. Heterogeneity was reported through the I2 statistic, and publication bias was examined using funnel plots and Egger's test in the meta-analysis of risk. One hundred and forty-two studies were identified in total, 26 (836 301 patients) relevant to the relative risk and 116 (2 111 882 patients) relevant to IRs. Compared to matched cancer-free controls, Bc patients had higher risk for cardiovascular death within 5 years of cancer diagnosis [HR = 1.09; 95% confidence interval (CI): 1.07, 1.11], HF within 10 years (HR = 1.21; 95% CI: 1.1, 1.33), and AF within 3 years (HR = 1.13; 95% CI: 1.05, 1.21). The pooled IR for cardiovascular death was 1.73 (95% CI 1.18, 2.53), 4.44 (95% CI 3.33, 5.92) for HF, 4.29 (95% CI 3.09, 5.94) for CAD, 1.98 (95% CI 1.24, 3.16) for MI, 4.33 (95% CI 2.97, 6.30) for stroke of any type, and 2.64 (95% CI 2.97, 6.30) for ischaemic stroke. CONCLUSION: Breast cancer exposure was associated with the increased risk for cardiovascular death, HF, and AF. The pooled incidence for cardiovascular endpoints varied depending on population characteristics and endpoint studied. REGISTRATION: CRD42022298741.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.035 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".