Abstract P059: Utilizing Electronic Health Records to Evaluate Racial Disparities in Metabolic Syndrome

Abstract

Purpose: Metabolic syndrome is defined as a clustering of clinical metabolic conditions (increased blood pressure, high blood sugar, increased body fat, abnormal cholesterol or triglycerides) and has been associated with an increased risk for several chronic diseases, such as cardiovascular disease. The aim of this project was to identify individuals presenting with metabolic syndrome using a computational patient phenotype definition derived from electronic medical records (EHR) clinical outcomes data. Secondly, this project evaluated racial disparities in metabolic syndrome across Southeast Louisiana. Methods: Data was obtained through Research Action for Health Network (REACHnet). Using the National Patient-Centered Clinical Research Network Common Data Model, REACHnet has standardized and made usable EHR data for patient-centered research across Louisiana and Texas. The computational patient phenotype definition for metabolic syndrome was developed based on the National Cholesterol Education Program Expert Panel in Adult Treatment Panel III (NCEP III) guidelines. The presence of metabolic conditions was established using ICD9 Diagnosis codes, patient vitals and lab results that are routinely available in EHR data. Logistic regression models to assess racial disparities were executed using SAS 9.4. Results: We analyzed 18,664 patient EHRs for individuals 18 years or older with complete clinical data spanning the years 2013 to 2014. The sample was 43.28% male (n=8,077) and 29.35% black (n=5,477). Based on the patient phenotype definition, the prevalence of metabolic syndrome in the sample was 39.09%. Controlling for age, the odds of metabolic syndrome were twice as high for black women than for white women (OR= 2 (1.83, 2.18)), while the odds were 15% greater for black men than for white men (OR: 1.15 (1.04, 1.28)). Conclusion: We observed significant disparities in the prevalence of clinically evident metabolic syndrome in southeast Louisiana. Racial disparities were greatest among women. It has been increasingly recognized that differential exposure to chronic social and nutritive stress from living in a disadvantaged neighborhood may be contributing to racial health disparities. Further research in this sample will link ancillary sources of neighborhood data to the successfully developed metabolic syndrome phenotype to explore potential mechanisms for racial disparities in cardiovascular disease among a clinically-rich, state-wide sample.