Abstract 183: Psychosocial Risk Profiles in Patients with Acute Coronary Syndrome: First Findings from TRACE-CORE

Abstract

Background: Psychological distress variables (depression, anxiety, stress) and social support are associated with health outcomes in patients with acute coronary syndrome (ACS), as are low quality of life (QoL) and socioeconomic status (SES). Distress variables are often correlated and, together with social support, may generate categories of psychosocial vulnerability that could be useful in predicting cardiovascular outcomes. We examined the feasibility of generating psychosocial risk profiles based on these factors and, as an initial test of validity, hypothesized that high psychosocial risk would be associated with low SES and low QoL. Methods: Adults (n = 793 to date) in Worcester, MA and Macon, GA were interviewed during hospitalization for ACS in an ongoing longitudinal study within the Transitions, Risk, and Actions in Coronary Events Center for Outcomes Research and Education (TRACE-CORE). Patients completed assessments of depression (PHQ-9), perceived stress (PSS), anxiety (GAD-7), social support (MOS), social network (LSNS), and generic (SF-36), and condition-specific (SAQ), QoL measures. K-means cluster analysis identified groups with different risk profiles using standardized summary scores of distress (PHQ-9, PSS and GAD-7) and social support (LSNS and MOS). We tested relationships of these clusters with demographic characteristics and QoL. Results: Average patient age was 61 years, 35% were women, 76% non-Hispanic White, 13% African American. We identified 3 clusters designated low, moderate and high risk (Table 1). The low-risk cluster had low distress and high social support scores; moderate-risk group had average distress and low social support; high-risk group had high distress and average social support. Moving from low to high cluster risk, we found increasing proportions of women and patients with lower SES. In contrast age and QoL decreased as cluster risk increased. Conclusions: Data-driven psychosocial risk stratification correlates as predicted with QoL and socioeconomic indicators If these clusters remain stable over time and are related to short- and long-term ACS outcomes as well (a testable hypothesis as TRACE-CORE progresses) they may facilitate developing tailored interventions to improve ACS patient outcomes.