Can allocation of risk be used to guide management in patients undergoing stress echocardiography?

Background This study evaluated the ability of intravenous myocardial contrast echocardiography (MCE) performed in the setting of acute myocardial infarction for prediction of left ventricular (LV) remodeling. Methods. Intravenous MCE was performed immediately before, 1 hour, and 24 hours after primary percutaneous transluminal coronary angioplasty (PTCA) in 35 patients with a first myocardial infarction. The MCE was used to define the relative perfusion defect size (in %; relMCD). Two-dimensional echocardiography was performed directly after angioplasty and after 4 weeks to determine LV end-diastolic volumes (LVEDV). The increase in LVEDV at 4 weeks defined a remodeling (>15% increase) and a nonremodeling group (less than or equal to15% increase). Results: Patients with remodeling had larger relMCD before (22.0+/-16.1 vs 8.0+/-11.9, P=.015), 1 hour (20.0+/-13.0 vs 4.9+/-11.6, P=.001), and 24 hours after PTCA (22.9+/-14.1 vs 1.2+/-2.8, P<.001). There was a significant correlation between relMCD 24 hours after PTCA and the increase in LVEDV at 4 weeks (r=0.648; P<.001). Receiver operating characteristic (ROC) curve analysis revealed a relMCD at 24 hours of 5.1% or more to predict remodeling with a sensitivity of 94% and a specificity of 87% (area under ROC curve=0.917; SE=0.054). Multivariate analysis demonstrated relMCD at 24 hours to be the only predictor of remodeling (odds ratio=173.4; P=.022). Conclusion: The size of the persistent MCE perfusion defect after revascularization for acute myocardial infarction has a high predictive value for LV remodeling during a 4-week follow-up period

The association between lower educational attainment and depression owing to shared genetic effects? Results in ∼25 000 subjects: Results in ~25,000 subjects

An association between lower educational attainment (EA) and an increased risk for depression has been confirmed in various western countries. This study examines whether pleiotropic genetic effects contribute to this association. Therefore, data were analyzed from a total of 9662 major depressive disorder (MDD) cases and 14 949 controls (with no lifetime MDD diagnosis) from the Psychiatric Genomics Consortium with additional Dutch and Estonian data. The association of EA and MDD was assessed with logistic regression in 15 138 individuals indicating a significantly negative association in our sample with an odds ratio for MDD 0.78 (0.75-0.82) per standard deviation increase in EA. With data of 884 105 autosomal common single-nucleotide polymorphisms (SNPs), three methods were applied to test for pleiotropy between MDD and EA: (i) genetic profile risk scores (GPRS) derived from training data for EA (independent meta-analysis on ∼120 000 subjects) and MDD (using a 10-fold leave-one-out procedure in the current sample), (ii) bivariate genomic-relationship-matrix restricted maximum likelihood (GREML) and (iii) SNP effect concordance analysis (SECA). With these methods, we found (i) that the EA-GPRS did not predict MDD status, and MDD-GPRS did not predict EA, (ii) a weak negative genetic correlation with bivariate GREML analyses, but this correlation was not consistently significant, (iii) no evidence for concordance of MDD and EA SNP effects with SECA analysis. To conclude, our study confirms an association of lower EA and MDD risk, but this association was not because of measurable pleiotropic genetic effects, which suggests that environmental factors could be involved, for example, socioeconomic status