Cardiotoxicity of anticancer treatments: Epidemiology, detection, and management
Bibliographic record
Abstract
Answer questions and earn CME/CNE Cancer and heart disease are the leading causes of morbidity and mortality in the industrialized world. Modern treatment strategies have led to an improvement in the chances of surviving a diagnosis of cancer; however, these gains can come at a cost. Patients may experience adverse cardiovascular events related to their cancer treatment or as a result of an exacerbation of underlying cardiovascular disease. With longer periods of survival, late effects of cancer treatment may become clinically evident years or decades after completion of therapy. Current cancer therapy incorporates multiple agents whose deleterious cardiac effects may be additive or synergistic. Cardiac dysfunction may result from agents that can result in myocyte destruction, such as with anthracycline use, or from agents that appear to transiently affect left ventricular contractility. In addition, cancer treatment may be associated with other cardiac events, such as severe treatment-induced hypertension and vasospastic and thromboembolic ischemia, as well as rhythm disturbances, including QTc prolongation, that may be rarely life-threatening. Early and late effects of chest radiation can lead to radiation-induced heart disease, including pericardial disease, myocardial fibrosis, cardiomyopathy, coronary artery disease, valvular disease, and arrhythmias, in the setting of myocardial fibrosis. The discipline of cardio-oncology has developed in response to the combined decision making necessary to optimize the care of cancer patients, whether they are receiving active treatment or are long-term survivors. Strategies to prevent or mitigate cardiovascular damage from cancer treatment are needed to provide the best cancer care. This review will focus on the common cardiovascular issues that may arise during or after cancer therapy, the detection and monitoring of cardiovascular injury, and the best management principles to protect against or minimize cardiotoxicity during the spectrum of cancer treatment strategies. CA Cancer J Clin 2016;66:309-325. © 2016 American Cancer Society.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".