Safety of Permissive Cardiotoxicity of Trastuzumab in Patients with Breast Cancer: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Cardiotoxicity is a recognized adverse effect associated with anti-HER2 therapies. The Trastuzumab-mediated cardiac dysfunction is not dose-dependent and lacks ultrastructural changes, allowing potential recovery after a few months. There is no consensus on the management of patients who develop cardiotoxicity. This meta-analysis aims to assess the safety of the permissive cardiotoxicity of Trastuzumab in patients with breast cancer. We searched PubMed, Cochrane Library, and Embase up to October 2023 for retrospective or prospective studies that investigated the safety of Trastuzumab in patients with breast cancer who continued Trastuzumab therapy after asymptomatic ejection fraction (EF) decline. We conducted a pooled meta-analysis for the subsequent cardiac events, cardiac mortality, and all-cause mortality. We assessed the quality of included studies using the Newcastle-Ottawa Scale and ROBIN-1 tool. A total of eight cohort studies (six retrospective and two prospective), comprising 222 patients, were found eligible and were included in our analysis. The pooled incidence of cardiac events, cardiac-related mortality, and all-cause mortality was 18% (95% CI 13% to 24%), 5% (95% CI 2% to 10%), and 8% (95% CI 2% to 28%), respectively. The incidence of symptomatic or severe cardiac events was lower in those who received cardioprotective medications concomitant with Trastuzumab. Most patients received either angiotensin-converting-enzyme inhibitors, beta-blockers, or a combination of both. Continuation of planned Trastuzumab therapy with concomitant use of cardioprotective medications can be a safe and effective approach for breast cancer control in patients with asymptomatic EF decline.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.036 |
| Bibliometrics | 0.005 | 0.006 |
| 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) | 0.003 | 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".