A randomized trial of deferred stenting versus immediate stenting to prevent no- or slow-reflow in acute ST-segment elevation myocardial infarction (DEFER-STEMI)

Objectives: The aim of this study was to assess whether deferred stenting might reduce no-reflow and salvage myocardium in primary percutaneous coronary intervention (PCI) for ST-segment elevation myocardial infarction (STEMI). Background: No-reflow is associated with adverse outcomes in STEMI. Methods: This was a prospective, single-center, randomized, controlled, proof-of-concept trial in reperfused STEMI patients with ≥1 risk factors for no-reflow. Randomization was to deferred stenting with an intention-to-stent 4 to 16 h later or conventional treatment with immediate stenting. The primary outcome was the incidence of no-/slow-reflow (Thrombolysis In Myocardial Infarction ≤2). Cardiac magnetic resonance imaging was performed 2 days and 6 months after myocardial infarction. Myocardial salvage was the final infarct size indexed to the initial area at risk. Results: Of 411 STEMI patients (March 11, 2012 to November 21, 2012), 101 patients (mean age, 60 years; 69% male) were randomized (52 to the deferred stenting group, 49 to the immediate stenting). The median (interquartile range [IQR]) time to the second procedure in the deferred stenting group was 9 h (IQR: 6 to 12 h). Fewer patients in the deferred stenting group had no-/slow-reflow (14 [29%] vs. 3 [6%]; p = 0.006), no reflow (7 [14%] vs. 1 [2%]; p = 0.052) and intraprocedural thrombotic events (16 [33%] vs. 5 [10%]; p = 0.010). Thrombolysis In Myocardial Infarction coronary flow grades at the end of PCI were higher in the deferred stenting group (p = 0.018). Recurrent STEMI occurred in 2 patients in the deferred stenting group before the second procedure. Myocardial salvage index at 6 months was greater in the deferred stenting group (68 [IQR: 54% to 82%] vs. 56 [IQR: 31% to 72%]; p = 0.031]. Conclusions: In high-risk STEMI patients, deferred stenting in primary PCI reduced no-reflow and increased myocardial salvage

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 190, February 1979

This bibliography lists 235 reports, articles, and other documents introduced into the NASA scientific and technical information system in January 1979

Ezetimibe therapy: mechanism of action and clinical update.

The lowering of low-density lipoprotein cholesterol (LDL-C) is the primary target of therapy in the primary and secondary prevention of cardiovascular events. Although statin therapy is the mainstay for LDL-C lowering, a significant percentage of patients prescribed these agents either do not achieve targets with statin therapy alone or have partial or complete intolerance to them. For such patients, the use of adjuvant therapy capable of providing incremental LDL-C reduction is advised. One such agent is ezetimibe, a cholesterol absorption inhibitor that targets uptake at the jejunal enterocyte brush border. Its primary target of action is the cholesterol transport protein Nieman Pick C1 like 1 protein. Ezetimibe is an effective LDL-C lowering agent and is safe and well tolerated. In response to significant controversy surrounding the use and therapeutic effectiveness of this drug, we provide an update on the biochemical mechanism of action for ezetimibe, its safety and efficacy, as well as the results of recent randomized studies that support its use in a variety of clinical scenarios

Coronary Artery Centerline Extraction in Cardiac CT Angiography Using a CNN-Based Orientation Classifier

Coronary artery centerline extraction in cardiac CT angiography (CCTA) images is a prerequisite for evaluation of stenoses and atherosclerotic plaque. We propose an algorithm that extracts coronary artery centerlines in CCTA using a convolutional neural network (CNN). A 3D dilated CNN is trained to predict the most likely direction and radius of an artery at any given point in a CCTA image based on a local image patch. Starting from a single seed point placed manually or automatically anywhere in a coronary artery, a tracker follows the vessel centerline in two directions using the predictions of the CNN. Tracking is terminated when no direction can be identified with high certainty. The CNN was trained using 32 manually annotated centerlines in a training set consisting of 8 CCTA images provided in the MICCAI 2008 Coronary Artery Tracking Challenge (CAT08). Evaluation using 24 test images of the CAT08 challenge showed that extracted centerlines had an average overlap of 93.7% with 96 manually annotated reference centerlines. Extracted centerline points were highly accurate, with an average distance of 0.21 mm to reference centerline points. In a second test set consisting of 50 CCTA scans, 5,448 markers in the coronary arteries were used as seed points to extract single centerlines. This showed strong correspondence between extracted centerlines and manually placed markers. In a third test set containing 36 CCTA scans, fully automatic seeding and centerline extraction led to extraction of on average 92% of clinically relevant coronary artery segments. The proposed method is able to accurately and efficiently determine the direction and radius of coronary arteries. The method can be trained with limited training data, and once trained allows fast automatic or interactive extraction of coronary artery trees from CCTA images.Comment: Accepted in Medical Image Analysi

NASA contributions to - Cardiovascular monitoring

NASA contributions to cardiovasular monitorin

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 156)

This bibliography lists 170 reports, articles, and other documents introduced into the NASA scientific and technical information system in June 1976

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Modelling the impact of atherosclerosis on drug release and distribution from coronary stents

Although drug-eluting stents (DES) are now widely used for the treatment of coronary heart disease, there remains considerable scope for the development of enhanced designs which address some of the limitations of existing devices. The drug release profile is a key element governing the overall performance of DES. The use of in vitro, in vivo, ex vivo, in silico and mathematical models has enhanced understanding of the factors which govern drug uptake and distribution from DES. Such work has identified the physical phenomena determining the transport of drug from the stent and through tissue, and has highlighted the importance of stent coatings and drug physical properties to this process. However, there is limited information regarding the precise role that the atherosclerotic lesion has in determining the uptake and distribution of drug. In this review, we start by discussing the various models that have been used in this research area, highlighting the different types of information they can provide. We then go on to describe more recent methods that incorporate the impact of atherosclerotic lesions

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 290)

This bibliography lists 125 reports, articles and other documents introduced into the NASA scientific and technical information system in October 1986