Abstract 4136932: Impact of Different Socioeconomic Metrics on Heart Failure-Related Admission and Short-Term Outcomes in Maryland

Abstract

Introduction: Annually, over 500,000 Americans are hospitalized due to heart failure (HF), marking it as a major contributor to morbidity and mortality. It also poses a significant financial burden and leads to considerable losses in productivity. Objective: This study investigates the predictive accuracy of different socioeconomic metrics on the risk and outcomes of HF in Maryland. Methodology: A retrospective analysis of the Maryland State Inpatient Database (2016-2020) was conducted to assess the predictive accuracy of race/ethnicity, insurance status, household median income, and neighborhood poverty level (measured by the Distressed Communities Index) on the risk of heart failure-related hospital admissions and outcomes. Multivariate logistic regression models were also used to adjust for confounders. Result: During the study period, a total of 389,220 cases of HF were reported in the Maryland SID. The majority of these patients were white (56.8%) and female (51.1%), with a median age of 73 years (interquartile range [IQR] 62-82 years). The in-hospital mortality rate was 5.1%, while rates of atrial fibrillation, cardiac arrest and prolonged hospital stay were 34.4%, 0.3%, and 48.4%, respectively. Multivariate analysis revealed a substantial area under the ROC curve (AUC) indicating good model performance: 0.88 for predicting HF, 0.64 for atrial fibrillation, 0.64 for cardiac arrest 0.57 for prolonged hospital stays, 0.63 for mortality. Subgroup analyses showed variable predictiveness by race (AUC = 0.4378), payment method (AUC = 0.5754), income quartile (AUC = 0.5202), and deprivation composite score (AUC = 0.4751). Patients with private insurance had the highest risk of stress cardiomyopathy (odds ratio [OR] = 1.98; 95% confidence interval [CI] 1.70-2.29). Socioeconomic metrics, including neighborhood distress, showed varying predictive accuracy for the HF-related admissions and selected short-term outcomes, with the highest predictive accuracy for neighborhood distress on the risk of HF (AUC = 0.50, std: 0.006), atrial fibrillation (AUC = 0.48, std: 0.0007), cardiac arrest (AUC = 0.51, std: 0.007), and prolonged hospital stays (AUC = 0.53, std: 0.0005) and mortality (AUC = 0.50, std: 0.0015). Conclusion: Neighborhood poverty level demonstrates significant predictive power for assessing the risk of HF-related hospital admissions and the short-term outcomes among Maryland residents, exceeding factors like insurance and race/ethnicity.