The Risk of Heart Failure and Other Cardiovascular Hospitalizations After Early Stage Breast Cancer: A Matched Cohort Study
Bibliographic record
Abstract
BACKGROUND: Data are limited regarding the risk of heart failure (HF) requiring hospital-based care after early stage breast cancer (EBC) and its relationship to other types of cardiovascular disease (CVD). METHODS: We conducted a population-based, retrospective cohort study of EBC patients (diagnosed April 1, 2005-March 31, 2015) matched 1:3 on birth-year to cancer-free control subjects. We identified hospitalizations and emergency department visits for CVD through March 31, 2017. We used cumulative incidence function curves to estimate CVD incidence and cause-specific regression models to compare CVD rates between cohorts. All statistical tests were two-sided. RESULTS: We identified 78 318 EBC patients and 234 954 control subjects. The 10-year incidence of CVD hospitalization was 10.8% (95% confidence interval [CI] = 10.5% to 11.1%) after EBC and 9.1% (95% CI = 8.9% to 9.2%) in control subjects. Ischemic heart disease was the most common reason for CVD hospitalization after EBC. After regression adjustment, the relative rates compared with control subjects remained statistically significantly elevated for HF (hazard ratio [HR] = 1.21, 95% CI = 1.14 to 1.29, P < .001), arrhythmias (HR = 1.31, 95% CI = 1.23 to 1.39, P < .001), and cerebrovascular disease (HR 1.10, 95% CI = 1.04 to 1.17, P = .002) hospitalizations. It was rare for HF hospital presentations (2.9% of cases) to occur in EBC patients without recognized risk factors (age >60 years, hypertension, diabetes, prior CVD). Anthracycline and/or trastuzumab were used in 28 950 EBC patients; they were younger than the overall cohort with lower absolute rates of CVD, hypertension, and diabetes. However, they had higher relative rates of CVD in comparison with age-matched control subjects. CONCLUSIONS: Atherosclerotic diagnoses, rather than HF, were the most common reasons for CVD hospitalization after EBC. HF hospital presentations were often preceded by risk factors other than chemotherapy, suggesting potential opportunities for prevention.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".