Optimizing Cardiovascular Health in Patients With Cancer: A Practical Review of Risk Assessment, Monitoring, and Prevention of Cancer Treatment–Related Cardiovascular Toxicity
Bibliographic record
Abstract
Advances in cancer screening and improved treatment approaches have led to an increase in survivorship and, consequently, recognition of an association between cancer treatments and the development of cardiovascular complications. In addition, as the population becomes proportionally older, comorbid cardiovascular risk factors are more prevalent in the population and compound the risk of developing cancer treatment-related cardiovascular toxicity. Cardio-oncology has emerged as a new subspecialty of medicine that provides a multidisciplinary approach, bringing together oncologists, cardiologists, and allied health care providers who are tasked with optimizing the cardiovascular health of patients exposed to potentially cardiotoxic cancer therapy. Using a case-based approach, practical advice on how to identify, monitor, and treat patients with cancer who are at risk for developing cancer treatment-related cardiovascular dysfunction is discussed. Cardiovascular risk factors (e.g., age, hypertension, diabetes) and cancer therapies (chemotherapy, targeted therapy, radiation) associated with cardiovascular toxicity are presented. Current cardiac monitoring strategies such as two- and three-dimensional echocardiography, cardiac MRI, and biomarkers (troponin and brain natriuretic peptide [BNP]) are discussed. Last, the current literature on pharmacologic (e.g., angiotensin-converting enzyme inhibitors, β-blockers, statins) and lifestyle (diet and exercise) strategies to mitigate cardiovascular toxicity during and following completion of cancer therapy are reviewed.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".