The Temporal Risk of Heart Failure Associated With Adjuvant Trastuzumab in Breast Cancer Patients: A Population Study
Bibliographic record
Abstract
BACKGROUND: The late cardiac effect of adjuvant trastuzumab and its potential interaction with anthracycline have not been well-studied on a population level. METHODS: In this retrospective population-based cohort study, female breast cancer patients in Ontario, diagnosed between 2003 and 2009, were identified by the Ontario Cancer Registry and linked to administrative databases to ascertain demographics, cardiac risk factors, comorbidities, and use of adjuvant trastuzumab and other chemotherapy. Patients with pre-existing heart failure (HF) were excluded. The main endpoint was new diagnosis of HF. Analyses included Kaplan-Meier (KM) survival analysis, multivariable piecewise Cox regression, and competing risk and propensity score analyses. All statistical tests were two-sided. RESULTS: Nineteen thousand seventy-four women with breast cancer treated with adjuvant chemotherapy were identified, of whom 3371 (17.7%) also received adjuvant trastuzumab. Anthracycline use was 84.9% overall. After a median follow-up of 5.9 years, patients treated with trastuzumab and chemotherapy were more likely to develop HF than patients on chemotherapy alone (5-year cumulative incidences of 5.2% vs 2.5%; log-rank P < .001). After adjusting for confounders, adjuvant trastuzumab remained independently associated with incident HF in the first 1.5 years (HR = 5.77, 95% CI = 4.38 to 7.62, P < .001), but not thereafter (HR = 0.87, 95% CI = 0.57 to 1.33, P = .53). Anthracycline use did not increase the risk of HF with trastuzumab synergistically, neither within (P interaction = .92) nor beyond 1.5 years (P interaction = .23). CONCLUSION: Adjuvant trastuzumab was associated with increased risk of new incidence of HF in breast cancer survivors during the period of adjuvant treatment but not thereafter. Routine intensive monitoring may not be necessary after completing adjuvant therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".