Abstract 12728: The Addition of Social Determinants of Health Improves the Predictive Accuracy of the Pooled Cohort Equations for 10-year Ascvd Events in African Americans

Abstract

Introduction: The gold standard risk prediction tool for atherosclerotic cardiovascular disease (ASCVD), the Pooled Cohort Equations (PCE) underestimates risk among the socially disadvantaged, yet social determinants of health are absent from the PCE. This may be contributing to racial inequities in CVD since due to systemic and structural racism, socioeconomic disadvantage is disproportionately concentrated in African American (AA) communities. This analysis aimed to determine whether SDOH variables were important for the prediction of 10-year ASCVD risk in a large cohort of AA and to test whether the addition of these variables improved PCE model performance. Methods: Retrospective observational study of 3,470 Jackson Heart Study (JHS) participants aged 40-79 years old without prior ASCVD at baseline study visit. 10-year ASCVD event was defined as stroke, MI, or death from MI. First, we used competing risk random survival forests to determine variable importance of risk factors, then we used cox-proportional hazards to identify which variables among those found to be important, were independently associated with our outcome. We used time dependent AUC to compare the performance of PCE alone to models with the PCE plus SDOH to assess concordance with the true event rates for prediction of 10-year ASCVD. Results: Our sample was 35% male, 100% AA and 46% of participants had an income of <$35K/year. Of 30 SDOH factors tested, the following 6: BMI, depression, weekly stress, insurance status, family income, and neighborhood violence were determined most important for prediction and were independently associated with 10-year ASCVD risk. In this sample, the PCE only model had an AUC of 0.822; adding a limited panel of SDOH factors - income, insurance status and occupation, increased the AUC to 0.859, but this improvement was only statistically significant at the 0.10 level (p=0.093). The model adding the full 6 SDOH risk panel to the PCE, saw the highest AUC at 0.863 (p=0.029). Conclusions: Adding SDOH risk factors to the PCE improved risk prediction. These findings should be explored in a larger database to determine if adding SDOH to the PCE can improve risk prediction for ASCVD events among vulnerable populations and guide more optimal use of prevention strategies.