Association of cardiac events with coronary artery disease detected by 64-slice or greater coronary CT angiography: a systematic review and meta-analysis.

Background: The value of ≥64-slice coronary CT angiography (CCTA) to determine odds of cardiac death or non-fatal myocardial infarction (MI) needs further clarification.

Methods: We performed a systematic review and meta-analysis using publications reporting events/severity of coronary artery disease (CAD) in patients with suspected CAD undergoing CCTA. Patients were divided into: no CAD, non-obstructive CAD (maximal stenosis <50%), and obstructive CAD (≥50% stenosis).

An algorithm for identification of ST-elevation myocardial infarction patients by emergency medicine services.

Objective: ST-elevation myocardial infarction (STEMI) identification by emergency medicine services (EMS) leading to pre-hospital catheterization laboratory (CL) activation shortens ischemic time and improves outcomes. We examined the incremental value of addition of a screening clinical tool (CT), containing clinical information and a Zoll electrocardiogram (ECG)-resident STEMI identification program (ZI) to ZI alone.

Methods: All EMS-performed and ZI-analyzed ECGs transmitted to a percutaneous coronary intervention hospital from October 2009 to January 2011 were reviewed for diagnostic accuracy.

Validation of a pre-existing formula to calculate the contribution of ethanol to the osmolar gap.

Purpose: The aim of this study was to validate the formula derived by Purssell et al. that relates blood ethanol concentration to the osmolar gap and determine the best coefficient for use in the formula. The osmolar gap is often used to help diagnose toxic alcohol poisoning when direct measurements are not available.

Using multiple sources of data to assess the prevalence of diabetes at the subcounty level, Duval County, Florida, 2007.

Introduction: Diabetes rates continue to grow in the United States. Effectively addressing the epidemic requires better understanding of the distribution of disease and the geographic clustering of factors that influence it. Variations in the prevalence of diabetes at the local level are largely unreported, making understanding the disparities associated with the disease more difficult.