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Decision Science Can Inform Clinical Trade-Offs Regarding Cardiotoxic Cancer Treatments
Author(s) -
Arielle S. Gillman,
Jacqueline B. Vo,
Anju Nohria,
Rebecca A. Ferrer
Publication year - 2021
Publication title -
jnci cancer spectrum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.345
H-Index - 10
ISSN - 2515-5091
DOI - 10.1093/jncics/pkab053
Subject(s) - cardiotoxicity , medicine , intensive care medicine , heuristics , disease , coronary artery disease , cancer , chemotherapy , computer science , operating system
Cancer treatment-related cardiotoxicity (ie, heart failure, coronary artery disease, vascular diseases, arrhythmia) is a growing cancer survivorship concern within oncology practice; heart disease is the leading cause of noncancer death in cancer survivors and surpasses cancer as the leading cause of death for some cancers with higher survival rates. The issue of cardiotoxicity introduces a critical tradeoff that must be acknowledged and reconciled in clinical oncology practice: treating cancer aggressively and effectively in the present vs preventing future cardiotoxicity. Although many cancers must be treated as aggressively as possible, for others, multiple treatment options are available. Yet even when effective and less cardiotoxic treatments are available, they are not always chosen. Wariness to choose equally effective but less cardiotoxic treatment options may result in part from providers' and patients' reliance on "cognitive heuristics," or mental shortcuts that people (including, research shows, medical professionals) use to simplify complex judgments. These heuristics include delay discounting, availability and affect heuristics, and default bias. In the current commentary, we describe relevant research that illuminates how use of heuristics leads to biased medical decision making and translate how this research may apply when the tradeoff between aggressive cancer treatment and preventing future cardiotoxicity is considered. We discuss the implications of these biases in oncology practice, offer potential solutions to reduce bias, and call for future research in this area.

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