Quantitative Myocardial Contrast Echocardiography during Pharmacological Stress for Diagnosis of Coronary Artery Disease: A Systematic Review and Meta-analysis of Diagnostic Accuracy Studies

AIMS: We conducted a meta-analysis to evaluate the accuracy of quantitative stress myocardial contrast echocardiography (MCE) in coronary artery disease (CAD). METHODS AND RESULTS: Database search was performed through January 2008. We included studies evaluating accuracy of quantitative stress MCE for detection of CAD compared with coronary angiography or single-photon emission computed tomography (SPECT) and measuring reserve parameters of A, beta, and Abeta. Data from studies were verified and supplemented by the authors of each study. Using random effects meta-analysis, we estimated weighted mean difference (WMD), likelihood ratios (LRs), diagnostic odds ratios (DORs), and summary area under curve (AUC), all with 95% confidence interval (CI). Of 1443 studies, 13 including 627 patients (age range, 38-75 years) and comparing MCE with angiography (n = 10), SPECT (n = 1), or both (n = 2) were eligible. WMD (95% CI) were significantly less in CAD group than no-CAD group: 0.12 (0.06-0.18) (P < 0.001), 1.38 (1.28-1.52) (P < 0.001), and 1.47 (1.18-1.76) (P < 0.001) for A, beta, and Abeta reserves, respectively. Pooled LRs for positive test were 1.33 (1.13-1.57), 3.76 (2.43-5.80), and 3.64 (2.87-4.78) and LRs for negative test were 0.68 (0.55-0.83), 0.30 (0.24-0.38), and 0.27 (0.22-0.34) for A, beta, and Abeta reserves, respectively. Pooled DORs were 2.09 (1.42-3.07), 15.11 (7.90-28.91), and 14.73 (9.61-22.57) and AUCs were 0.637 (0.594-0.677), 0.851 (0.828-0.872), and 0.859 (0.842-0.750) for A, beta, and Abeta reserves, respectively. CONCLUSION: Evidence supports the use of quantitative MCE as a non-invasive test for detection of CAD. Standardizing MCE quantification analysis and adherence to reporting standards for diagnostic tests could enhance the quality of evidence in this field.

Climate and soil attributes determine plant species turnover in global drylands

Aim Geographical, climatic and soil factors are major drivers of plant beta diversity, but their importance for dryland plant communities is poorly known. The aim of this study was to: (1) characterize patterns of beta diversity in global drylands; (2) detect common environmental drivers of beta diversity; and (3) test for thresholds in environmental conditions driving potential shifts in plant species composition. Location Global. Methods Beta diversity was quantified in 224 dryland plant communities from 22 geographical regions on all continents except Antarctica using four complementary measures: the percentage of singletons (species occurring at only one site); Whittaker's beta diversity, β(W); a directional beta diversity metric based on the correlation in species occurrences among spatially contiguous sites, β(R2); and a multivariate abundance-based metric, β(MV). We used linear modelling to quantify the relationships between these metrics of beta diversity and geographical, climatic and soil variables. Results Soil fertility and variability in temperature and rainfall, and to a lesser extent latitude, were the most important environmental predictors of beta diversity. Metrics related to species identity percentage of singletons and β(W) were most sensitive to soil fertility, whereas those metrics related to environmental gradients and abundance (β(R2) and β(MV) were more associated with climate variability. Interactions among soil variables, climatic factors and plant cover were not important determinants of beta diversity. Sites receiving less than 178 mm of annual rainfall differed sharply in species composition from more mesic sites (> 200 mm). Main conclusions Soil fertility and variability in temperature and rainfall are the most important environmental predictors of variation in plant beta diversity in global drylands. Our results suggest that those sites annually receiving c. 178 mm of rainfall will be especially sensitive to future climate changes. These findings may help to define appropriate conservation strategies for mitigating effects of climate change on dryland vegetation.