Patient and physician preferences for attributes of coronary revascularization

BACKGROUND: Patients with a diagnosis of coronary artery disease (CAD) may face important decisions regarding treatment options, with the “right choice” depending on the relative weights of risks and benefits. Studies performed as discrete choice experiments are used to estimate these weights, and attribute selection is an essential step in the design of these studies. Attributes not included in the design cannot be analyzed. In this study, we aimed to elicit, rank, and rate attributes that may be considered important to patients and physicians who must choose between angioplasty and surgery for coronary revascularization. METHODS: The elicitation process involved performing a systematic review to search for attributes cited in declared preference studies in addition to face-to-face interviews with cardiologists and experts. The interviews were audio-recorded in digital format, and the collected data were transcribed and searched to identify new attributes. The criterion used to finish the data collection process was sampling saturation. RESULTS: A systematic review resulted in the selection of the following 14 attributes: atrial fibrillation, heart failure, incision scar, length of stay, long-term survival, myocardial infarction, periprocedural death, postoperative infection, postprocedural angina, pseudoaneurysm, renal failure, repeat coronary artery bypass grafting, repeat percutaneous coronary intervention, and stroke. The interviews added no new attributes. After rating, we identified significant differences in the values that patients and cardiologists placed on renal insufficiency (p<0.001), periprocedural death (p<0.001), and long-term survival (p<0.001). CONCLUSION: Decisions regarding the best treatment option for patients with CAD should be made based on differences in risk and the patient’s preference regarding the most relevant endpoints. We elicited, ranked, and rated 14 attributes related to CAD treatment options. This list of attributes may help researchers who seek to perform future preference studies of CAD treatment options.