Abstract 14008: Socioeconomic Status and Prediction of Incident Heart Failure in the Atherosclerosis Risk in Communities (ARIC) Study

Abstract

Introduction: Socioeconomic status (SES) is a potent risk factor for heart failure (HF), but current HF prediction models do not incorporate data on SES. It is not known if the performance of HF risk prediction algorithms differs by SES, and how including SES in risk models impacts risk prediction. Methods: We performed an analysis of participants without HF at Visit 1 (1987-89) of the ARIC study. SES was assessed by household income, educational attainment, Area Deprivation Index, and as a combined cumulative SES score that included all three measures. Incident hospitalizations and deaths related to HF were assessed through 12/31/2019. We compared risk discrimination (c-statistic) of the established ARIC HF risk score across SES categories, and assessed calibration within SES strata by comparing observed versus predicted risk. We examined changes in risk discrimination, net reclassification and calibration (calculating the Hosmer-Lemeshow statistic for the latter) with adding SES to the ARIC HF risk score. Results: We studied 14,468 individuals; 54% were women, 26% were Black adults and 22% had low cumulative SES. There were 3,568 HF events over 33 years of follow-up. The c-statistic of the ARIC HF risk score was lower in those with low versus high cumulative SES (0.726 vs 0.765). There were differences in the ARIC HF risk score’s calibration by SES, with lower predicted than observed HF risk in those with low SES in all quintiles of predicted risk (Figure). Calibration improved with adding SES to the risk model (change in Hosmer-Lemeshow 127.6 to 98.3), especially in those with low SES (Figure). Adding SES to the model modestly improved risk discrimination (change in c-statistic 0.767 to 0.771; p<0.001) and changed net reclassification (0.05 [95% CI 0.02-0.08]). Conclusions: HF prediction algorithms underestimate HF risk in those with low SES. Systematic assessment and incorporation of SES data may enhance HF risk prediction in socioeconomically diverse populations.