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). Odds ratios with 95% confidence intervals were calculated using a fixed or random effects model. Heterogeneity was assessed using the I(2) index. RESULTS: We included thirty-two studies comprising 41,960 patients with 363 all-cause deaths (15.0%), 114 cardiac deaths (4.7%), 342 MI (14.2%), 69 unstable angina (2.8%), and 1527 late revascularizations (63.2%) over 1.96 (SD 0.77) years of follow-up. Cardiac death or MI occurred in 0.04% without, 1.29% with non-obstructive, and 6.53% with obstructive CAD. OR for cardiac death or MI was: 14.92 (95% CI, 6.78 to 32.85) for obstructive CAD, 6.41 (95% CI, 2.44 to 16.84) for non-obstructive CAD versus no CAD, and 3.19 (95% CI, 2.29 to 4.45) for non-obstructive versus obstructive CAD and 6.56 (95% CI, 3.07 to 14.02) for no versus any CAD. Similar trends were noted for all-cause mortality and composite major adverse cardiovascular events. CONCLUSIONS: Increasing CAD severity detected by CCTA is associated with cardiac death or MI, all-cause mortality, and composite major adverse cardiovascular events. Absence of CAD is associated with very low odds of major adverse events, but non-obstructive disease significantly increases odds of cardiac adverse events in this follow-up period.

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. ZI performance was also compared to ECG interpretations by 2 experienced readers The CT was then retrospectively applied to determine the incremental benefit above the ZI alone. RESULTS: ST-elevation myocardial infarction was confirmed in 23 (7.5%) of 305 patients. ZI was positive in 37 (12.1%): sensitivity: 95.6% and specificity: 94.6%, positive predictive value (PPV), 59.5%, negative predictive value (NPV), 99.6%, and accuracy of 93.8%. Moderate agreement was observed among the readers and ZI. CT criteria for CL activation were met in 24 (7.8%): 20 (83.3%) were confirmed STEMIs: sensitivity: 86.9%, specificity: 98.5%, a PPV: 83.3%, and NPV: 98.6%, accuracy of 97.7%. CT + ZI increased PPV (P<0.05) and specificity (P<0.003) by reducing false positive STEMI identifications from 15 (4.9%) to 4 (1.3%). CONCLUSIONS: In an urban cohort of all EMS transmitted ECGs, ZI has high sensitivity and specificity for STEMI identification. Whereas the PPV was low, reflecting both low STEMI prevalence and presence of STEMI-mimics, the NPV was very high. These findings suggest that a simplified CT combined with computer STEMI interpretation can identify patients for pre-hospital CL activation. Confirmation of these results could improve the design of STEMI care systems.

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. METHODOLOGY: Part I of the study consisted of a retrospective review of 603 emergency department patients who had a concurrent ethanol, basic metabolic panel and a serum osmolality results available. Estimated osmolarity (excluding ethanol) was calculated using a standard formula. The osmolar gap was determined by subtracting estimated osmolarity from the actual osmolality measured by freezing point depression. The relationship between the osmolar gap and the measured ethanol concentration was assessed by linear regression analysis. In Part II of this study, predetermined amounts of ethanol were added to aliquots of plasma and the estimated and calculated osmolarities were subjected to linear regression analysis. RESULTS: In the cases of 603 patients included in Part I of the study, the median ethanol concentration in these patients was 166 mg/dL (Q1: 90, Q3: 254) and the range ethanol concentrations was 10-644 mg/dL. The mean serum osmolality was 338 mOsm/kg (SD: 30) and a range of 244-450 mOsm/kg. The mean osmolar gap was 47 (SD: 29) and a range of - 15 to 55. There was a significant proportional relationship between ethanol concentration and osmolar gap (r(2) = 0.9882). The slope of the linear regression line was 0.2498 (95% CI: 0.2472-0.2524). The slope of the linear regression line derived from the data in Part II of the study was 0.2445 (95% CI: 0.2410-0.2480). CONCLUSIONS: The results of our study are in fairly close agreement with previous studies that used smaller samples and suggest that an accurate conversion factor for estimating the contribution of ethanol to the osmolar gap is [Ethanol (mg/dL)]/4.0.

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. Diabetes death rates during the past 15 years in Duval County, Florida, have been disproportionately high compared with the rest of the state. METHODS: We analyzed multiple sources of secondary data related to diabetes illness and death in Duval County, including data on hospital discharge, emergency department (ED) use, and vital statistics. We accessed diabetes and diabetes-related ED use and hospitalization and death data by using codes from the International Classification of Diseases versions 9 and 10. We analyzed data from the Behavioral Risk Factor Surveillance System survey for Duval County and adapted Centers for Disease Control and Prevention weighting formulas for subcounty analysis. We used relative risk-type disease ratios and geographic information systems mapping to analyze data. RESULTS: The urban, mostly minority, low-socioeconomic area of Duval County had twice the rate of diabetes-related illness and death as other areas of the county, and the inner-city, poor area of the county had almost 3 times the rate of hospitalization and ED use for diabetes and diabetes-related conditions compared with the other areas of the county. CONCLUSION: Our analyses show that diabetes-related disparities affect not only people and their families but also the community that absorbs the costs associated with the disproportionate health care use that results from these disparities. Analyzing data at the subcounty level has implications for health care planning and public health policy development at the local level.