Predicting Risk of Atherosclerotic Cardiovascular Disease Using Pooled Cohort Equations in Older Adults With Frailty, Multimorbidity, and Competing Risks

Abstract

Background Assessment of atherosclerotic cardiovascular disease (ASCVD) risk is crucial for prevention and management, but the performance of the pooled cohort equations in older adults with frailty and multimorbidity is unknown. We evaluated the pooled cohort equations in these subgroups and the impact of competing risks. Methods and Results In 4249 community‐dwelling adults, aged ≥65 years, from the CHS (Cardiovascular Health Study), we calculated 10‐year risk of hard ASCVD. Frailty was determined using the Fried phenotype. Latent class analysis was used to identify individuals with multimorbidity patterns using chronic conditions. We assessed discrimination using the C‐statistic and calibration by comparing predicted ASCVD risks with estimated risk using cause‐specific and cumulative incidence models, by multimorbidity patterns and frailty status. A total of 917 (21.6%) participants had an ASCVD event, and 706 (16.6%) had a competing event of death. C‐statistic was 0.68 in men and 0.69 in women; calibration was good when compared with cause‐specific and cumulative incidence estimated risks (males, −0.1% and 3.3%; females, 0.6% and 1.4%). Latent class analysis identified 4 patterns: minimal disease, cardiometabolic, low cognition, musculoskeletal‐lung depression. In the cardiometabolic pattern, ASCVD risk was overpredicted compared with cumulative incidence risk in men (7.4%) and women (6.8%). Risk was underpredicted in men (−10.7%) and women (−8.2%) with frailty compared with cause‐specific risk. Miscalibration occurred mostly at high predicted risk ranges. Conclusions ASCVD prediction was good in this cohort of adults aged ≥65 years. Although calibration varied by multimorbidity patterns, frailty, and competing risks, miscalibration was mostly present at high predicted risk ranges and thus less likely to alter decision making for primary prevention therapy.

Keywords

• atherosclerotic cardiovascular disease
• frailty
• multimorbidity
• older adults
• risk prediction