Socioclinical Phenotyping in Patients With Atherosclerotic Cardiovascular Disease: A Latent Class Analysis - Kundi, Harun; Hagan, Kobina; Yahya, Tamer; Sharma, Garima; Al-Kindi, Sadeer; Javed, Zulqarnain; Nasir, Khurram (The American journal of cardiology, 2024)
In a common disease population, such as atherosclerotic cardiovascular disease (ASCVD), latent classes may uncover subgroups of patients that can be distinguished by combinations of several factors instead of a single factor. In this study, we sought to identify the clinical, demographic, and social subphenotypes of ASCVD using latent class analysis (LCA) and assess the risk of all-cause and cardiovascular mortality across the identified socioclinical classes. LCA is a statistical technique used to uncover hidden class divisions within a set of subjects using a mix of categorical and/or continuous observed variables. Using the National Health Interview Survey between 2013 and 2018, a nationwide self-reported survey, linked to the National Mortality Index, we included participants aged 18 years and older who reported a history of ASCVD in the United States. The main outcome of this study is all-cause and cardiovascular mortality. There were 17,807 patients with a mean (SD) age of 66.9 (13.5) years. In summary, the 3 classes derived from LCA can be described as follows: class 1 is characterized by non-Hispanic White subjects with a low co-morbidity burden, class 2 consists of older subjects with a higher proportion of men, and class 3 includes younger subjects, predominantly non-Hispanic Black and Hispanic, with a greater burden of co-morbidities. In the multivariable models, the adjusted hazard ratio with 95% confidence intervals were 1.678 (1.458 to 1.930) in class 2 and 2.255 (1.931 to 2.633) in class 3 (p <0.001) for the all-cause long-term mortality. The ASCVD subphenotype (latent class) of younger, female, non-Hispanic Black or Hispanic subjects with a high burden of co-morbidities and unfavorable social determinants of health was associated with the highest risk of mortality compared with other classes. Our approach may inform future work to understand the heterogeneity among the demographic, clinical, and social risk factors in the ASCVD population and classify the mortality risk based on these key population characteristics.