A Prior Myocardial Infarction: How Does it Affect Management and Outcomes in Recurrent Acute Coronary Syndromes?

Background Despite improved secondary prevention efforts, acute coronary syndrome (ACS) recurrence among patients with prior history of coronary events remains high. The differences in presentation, management, and subsequent clinical outcomes in patients with and without a prior myocardial infarction (MI) and presenting with another episode of ACS remain unexplored. Methods A total of 3,624 consecutive patients admitted to the University of Michigan with ACS from January 1999 to June 2006 were studied retrospectively. In-hospital management, outcomes, and postdischarge outcomes such as death, stroke, and reinfarction in patients with and without a prior MI were compared. Results Patients with a prior MI were more likely to be older and have a higher incidence of diabetes mellitus, hypertension, hyperlipidemia, and peripheral vascular disease. In-hospital outcomes were not significantly different in the 2 groups, except for a higher incidence of cardiac arrest (4.3% versus 2.5%, p < 0.01) and cardiogenic shock (5.7% versus 3.9%, p = 0.01) among patients without a prior MI. However, at 6 mo postdischarge, the incidences of death (8.0% versus 4.5%, p < 0.0001) and recurrent MI (10.0% versus 5.1%, p < 0.0001) were significantly higher in patients with a prior history of MI compared with those without. Conclusion Patients with prior MI with recurrent ACS remain at a higher risk of major adverse events on follow-up. This may be partly explained by the patients not being on optimal medications at presentation, as well as disease progression. Increased efforts must be directed at prevention of recurrent ACS, as well as further risk stratification of these patients to improve their overall outcomes. Copyright © 2008 Wiley Periodicals, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/61452/1/20356_ftp.pd

Heart Rate Variability Dynamics for the Prognosis of Cardiovascular Risk

Statistical, spectral, multi-resolution and non-linear methods were applied to heart rate variability (HRV) series linked with classification schemes for the prognosis of cardiovascular risk. A total of 90 HRV records were analyzed: 45 from healthy subjects and 45 from cardiovascular risk patients. A total of 52 features from all the analysis methods were evaluated using standard two-sample Kolmogorov-Smirnov test (KS-test). The results of the statistical procedure provided input to multi-layer perceptron (MLP) neural networks, radial basis function (RBF) neural networks and support vector machines (SVM) for data classification. These schemes showed high performances with both training and test sets and many combinations of features (with a maximum accuracy of 96.67%). Additionally, there was a strong consideration for breathing frequency as a relevant feature in the HRV analysis

Heart Rate Variability Dynamics for the Prognosis of Cardiovascular Risk

Statistical, spectral, multi-resolution and non-linear methods were applied to heart rate variability (HRV) series linked with classification schemes for the prognosis of cardiovascular risk. A total of 90 HRV records were analyzed: 45 from healthy subjects and 45 from cardiovascular risk patients. A total of 52 features from all the analysis methods were evaluated using standard two-sample Kolmogorov-Smirnov test (KS-test). The results of the statistical procedure provided input to multi-layer perceptron (MLP) neural networks, radial basis function (RBF) neural networks and support vector machines (SVM) for data classification. These schemes showed high performances with both training and test sets and many combinations of features (with a maximum accuracy of 96.67%). Additionally, there was a strong consideration for breathing frequency as a relevant feature in the HRV analysis

Pairwise Measures of Causal Direction in the Epidemiology of Sleep Problems and Depression

Peer reviewe