No abstract

INTRODUCTION: Many studies have demonstrated that reduced high-density lipoproteins (HDL) levels and elevated triglyceride (TG)/HDL ratio correlate with the development of chronic ischemic heart disease. The role of HDL and the development of cardiac rhythm disturbances in the non-ST segment elevation acute coronary syndrome (ACS) is unclear. HYPOTHESIS: We assessed the hypothesis that HDL might be protective against development of cardiac rhythm disturbances. Additionally, any protective effect was compared with TG/HDL and LDL/HDL in this setting. METHODS: A total of 6881 patients who presented during 2000–2003 with non-ST segment elevation ACS had fasting lipid panels collected within the first 24 hours of admission. Patients were followed for the development of rhythm disturbances of up to 6 years after initial presentation, with a mean of 1269 days. These patients were further separated into diabetic and nondiabetic groups. RESULTS: After adjustment for ischemic heart disease, congestive heart failure, stroke, peripheral vascular disease, hypertension, low density lipoprotein cholesterol, triglycerides, age, and body mass index, higher HDL levels were found to be independently protective against developing atrial fibrillation and other cardiac rhythm disturbances. TG/HDL and LDL/HDL were also protective of cardiac arrhythmias but not with the same power as low HDL (HDL > 31mg/dl, HR = 3.69, 95% CI=3.01– 4.53, P < 0.05). CONCLUSIONS: Based on the above results, patients with lower HDL levels during hospitalizations with non-ST segment elevation ACS have a greater chance of developing cardiac rhythm disturbances independent of other risk factors. Although higher TG/HDL and LDL/HDL are also predictive, lower HDL is associated with highest OR for the development of cardiac arrhythmia among diabetic and non-diabetic patients with non-ST elevation with ACS.

Blood samples from 20 patients with widely varying conicentrations of erythrocytes and leukocyte subtypes were inoculated into BACTEC 6B bottles (Johnston Laboratories, Inc., Towsohf, Md.). There was no relationship between growth index value and hemogram. Although sterile blood is capa,ble of generating small amounts of 14CO2, the mechanism for this phenomenon is not related to the concentration of a specific type of blood cell.

Background: Electronic Health Record (EHR) data provides a wealth of patient data valuable to providers, organizations, registries, and researchers. The Guideline Advantage™ (TGA) has been developed by the American Heart Association, American Cancer Society, and American Diabetes Association as a national ambulatory registry to cover primary and secondary prevention across multiple disease groups. Methods: The program leverages EHR data to populate a clinical registry and provide population health measurement and analytic tools to drive improvements in population health and support registry-based research. TGA uses a data extraction model without the restrictions and resource demands of traditional data abstraction or interface mechanisms typical of clinical registries. This model supports customization and flexibility in data alignment which allows for more accurate measurement and representation of the care being delivered. The web-based population health tool delivers a clear lens into clinical data from a population level to a patient level, simplifying data stewardship and improvement tracking. Through these methods, the program has grown to include 25,091,500 patient observations for 797,079 unique patients. 408 providers and their clinical teams have access to population health analytic tools, quality improvement support, and resources to drive improvements in the care they are delivering. Discussion: While there are several disease-specific registries in existence, there have not yet been integrated solutions that can expand across key chronic and acute conditions facing ambulatory patients. To obtain data across multiple conditions without a high resource burden, TGA is maintaining a data extract model that foregoes traditional barriers and resource burdens such as extensive query development, abstraction, or maintaining costly interfaces. The data extraction model affords the ability to obtain exponentially more data than abstraction models allowing the registry to cover multiple disease groups. This breadth of data improves the application of the registry in both the practice setting and research. With this data, practices are able to look across their entire population to identify high-risk or at-risk populations with a single condition or comorbid conditions and create cohorts of patients to target with quality improvement strategies. Providing clinicians and researchers a clear look into complete and longitudinal clinical data is critical to supporting quality improvement activities pivotal to success in value-based care models and the advancement of novel research in primary and secondary prevention.