Outcome Preferences Related To Cardiovascular Preventive Therapies In Older Adults: An Online Survey

Background: Personalized cardiovascular disease (CVD) preventive therapy for older adults is important and emphasized in current clinical guidelines. Older adults often have multiple chronic conditions, are at higher risk for medication side effects, and may have different preferences when weighing multiple health outcomes. However, little is known about preferences for outcomes related to CVD preventive therapies in older adults. Methods: In May 2021, using an online panel, we surveyed US older adults (age 65-84 years) on outcome preferences related to statin (benefit-related outcomes: heart attack and stroke; side effect-related outcomes: diabetes, abnormal liver test, and muscle pain) or aspirin (benefit-related outcomes: heart attack and stroke; side effects: brain bleed, bowel bleed, and stomach ulcer). We used best-worst scaling, a novel stated-preference research method, to quantify relative outcome priorities. A standardized score was calculated for each outcome to indicate the strength of preference, ranging from -1.0 (least worrisome) to +1.0 (most worrisome). Results: 304 participants (median age 75, 45% male) completed the statin survey and 303 participants (median age 74, 46% male) completed the aspirin survey. In both surveys, stroke and heart attack, the two benefit outcomes from CVD prevention, were considered significantly more worrisome than outcomes related to side effects of the preventive therapies ( Table ). The general trend was similar for those age 65-74 vs. 75-84, although those age 75-84 tended to worry more about stroke than heart attack. Conclusions: Older adults considered the outcomes related to the benefits of CVD preventive therapies more important than outcomes related to the therapies’ side effects. These results suggest that older adults tend to prioritize the benefits of CVD preventive therapies over their side effects. Integrating individualized patient preferences with risk prediction to guide personalized shared decision-making is an important next step.