Sex and Racial Differences in High-Density Lipoprotein Levels in Acute Coronary Syndromes.

BACKGROUND: This study aimed to assess sex and racial differences related to high-density lipoprotein cholesterol (HDL-C) levels in those presenting with acute coronary syndromes (ACS). METHODS: Records from patients with ACS presenting to the Emergency Department of University of Florida Hospital Jacksonville from 2009 to 2012, were reviewed. Detailed medical history was obtained. HDL-C levels were measured within 72 h of presentation. Pearson chi-square and Wilcoxon rank sum tests were used to compare groups in univariate analysis. Analysis of variance was performed to determine independent predictors of higher HDL-C levels using variable selection. RESULTS: Of 2400 patients screened, 614 (382 men and 232 women) met inclusion criteria. Hypertension, chronic kidney disease or prior CAD history was similar between sexes and races. Women were more likely to be older (62.4 vs 58.4 years), diabetic (56.5 vs 36.5%) and have higher body mass index (31.2 vs 30.1 kg/m2). Blacks were more likely to be diabetic (50.3 vs 41.3%). After adjusting for all clinical markers, women and blacks along with absence of CAD or diabetes, were significantly associated with higher HDL-C levels. CONCLUSIONS: High HDL-C levels (> 40 mg/dL), considered cardio-protective, were seen in women and blacks with ACS more often than in men and whites. Significant differences in HDL-C levels between sexes were seen in whites but not in blacks. Relevance and quality of HDL-C levels in racial groups need further study as this may have important implications in the interpretation of current guidelines.

Racial and Socioeconomic Disparities in Out-Of-Hospital Cardiac Arrest Outcomes: Artificial Intelligence-Augmented Propensity Score and Geospatial Cohort Analysis of 3,952 Patients.

INTRODUCTION: Social disparities in out-of-hospital cardiac arrest (OHCA) outcomes are preventable, costly, and unjust. We sought to perform the first large artificial intelligence- (AI-) guided statistical and geographic information system (GIS) analysis of a multiyear and multisite cohort for OHCA outcomes (incidence and poor neurological disposition). METHOD: We conducted a retrospective cohort analysis of a prospectively collected multicenter dataset of adult patients who sequentially presented to Houston metro area hospitals from 01/01/07-01/01/16. Then AI-based machine learning (backward propagation neural network) augmented multivariable regression and GIS heat mapping were performed. RESULTS: Of 3,952 OHCA patients across 38 hospitals, African Americans were the most likely to suffer OHCA despite representing a significantly lower percentage of the population (42.6 versus 22.8%; p < 0.001). Compared to Caucasians, they were significantly more likely to have poor neurological disposition (OR 2.21, 95%CI 1.25-3.92; p=0.006) and be discharged to a facility instead of home (OR 1.39, 95%CI 1.05-1.85; p=0.023). Compared to the safety net hospital system primarily serving poorer African Americans, the university hospital serving primarily higher income commercially and Medicare insured patients had the lowest odds of death (OR 0.45, p < 0.001). Each additional $10,000 above median household income was associated with a decrease in the total number of cardiac arrests per zip code by 2.86 (95%CI -4.26- -1.46; p < 0.001); zip codes with a median income above $54,600 versus the federal poverty level had 14.62 fewer arrests (p < 0.001). GIS maps showed convergence of the greater density of poor neurologic outcome cases and greater density of poorer African American residences. CONCLUSION: This large, longitudinal AI-guided analysis statistically and geographically identifies racial and socioeconomic disparities in OHCA outcomes in a way that may allow targeted medical and public health coordinated efforts to improve clinical, cost, and social equity outcomes.