ST-segment elevation: Distinguishing ST elevation myocardial infarction from ST elevation secondary to nonischemic etiologies.

The benefits of early perfusion in ST elevation myocardial infarctions (STEMI) are established; however, early perfusion of non-ST elevation myocardial infarctions has not been shown to be beneficial. In addition, ST elevation (STE) caused by conditions other than acute ischemia is common. Non-ischemic STE may be confused as STEMI, but can also mask STEMI on electrocardiogram (ECG). As a result, activating the primary percutaneous coronary intervention (pPCI) protocol often depends on determining which ST elevation patterns reflect transmural infarction due to acute coronary artery thrombosis. Coordination of interpreting the ECG in its clinical context and appropriately activating the pPCI protocol has proved a difficult task in borderline cases. But its importance cannot be ignored, as reflected in the 2013 American College of Cardiology Foundation/American Heart Association guidelines concerning the treatment of ST elevation myocardial infarction. Multiples strategies have been tested and studied, and are currently being further perfected. No matter the strategy, at the heart of delivering the best care lies rapid and accurate interpretation of the ECG. Here, we present the different patterns of non-ischemic STE and methods of distinguishing between them. In writing this paper, we hope for quicker and better stratification of patients with STE on ECG, which will lead to be better outcomes.

Characterizing Functional Connectivity Differences in Aging Adults using Machine Learning on Resting State fMRI Data.

The brain at rest consists of spatially distributed but functionally connected regions, called intrinsic connectivity networks (ICNs). Resting state functional magnetic resonance imaging (rs-fMRI) has emerged as a way to characterize brain networks without confounds associated with task fMRI such as task difficulty and performance. Here we applied a Support Vector Machine (SVM) linear classifier as well as a support vector machine regressor to rs-fMRI data in order to compare age-related differences in four of the major functional brain networks: the default, cingulo-opercular, fronto-parietal, and sensorimotor. A linear SVM classifier discriminated between young and old subjects with 84% accuracy (p-value < 1 × 10(-7)). A linear SVR age predictor performed reasonably well in continuous age prediction (R (2) = 0.419, p-value < 1 × 10(-8)). These findings reveal that differences in intrinsic connectivity as measured with rs-fMRI exist between subjects, and that SVM methods are capable of detecting and utilizing these differences for classification and prediction.

Stand alone surgical ablation for atrial fibrillation.

BACKGROUND: Trials to maintain sinus rhythm in patients with atrial fibrillation (AF) and refractory symptoms have been complicated by lack of success or intolerance of medications. Experience with minimally invasive AF surgery is relatively new, and early results have been promising. However, the study populations and techniques were heterogeneous, and the follow-up periods were short in many series. METHODS: We present a single center experience through a retrospective review of medical records of patients who had minimally invasive AF surgery. RESULTS: The surgical techniques addressed several possible mechanisms of AF and causes of recurrence, including pulmonary vein isolation, underlying substrates modification, ligament of Marshall interruption, ganglion plexus ablation, and left atrial appendage exclusion. Thirty-three cases were identified. The mean duration of follow-up was 23.2 months, and 58.6% were maintained in a sinus rhythm and were off antiarrhythmic drugs at the end of the follow-up period. Cases with persistent AF had a lower success rate. CONCLUSION: Results with minimally invasive surgery are suboptimal at two years of follow-up, particularly for patients with persistent AF.