Introduction: Anomalous aortic origin of a coronary artery (AAOCA) is a potential etiology of sudden cardiac death (SCD) in physically active individuals. Identification of coronary artery origins is an essential part of comprehensive pre-participation athletic screening. Although echocardiography is an established method for identifying AAOCA, current imaging protocols are time intensive and readers frequently have low confidence in coronary artery identification.
Methods: Echocardiographic images from a sample of 110 patients from a database of competitive athletes ages 13-22 years from the Kansas City metropolitan area were reviewed by six echocardiographers of varying experience. Coronary artery images were provided to the readers in the conventional single plane for all the patients; then biplane images of the same patients were presented to the readers. While reviewing the images, readers recorded perceived confidence level of identifying the coronary artery from 1 (least confident) to 5 (most confident). Ratings and differences between ratings were summarized descriptively by means and standard deviations across all readings as well as by individual reader.
Results: The mean confidence level of echocardiogram readers in identifying coronary artery origins increased by 0.4 points (P = .05) on a five-point confidence scale when using biplane imaging rather than single plane imaging. When assessing the variability of confidence of readers on the same patient, the between-reader variability improved from 25.9% to 10.3%.
Conclusions: Biplane echocardiographic imaging increases the confidence of readers in identifying coronary artery origins.
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http://dx.doi.org/10.1111/echo.15082 | DOI Listing |
J Mater Sci Mater Med
January 2025
Department of Neurology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, PR China.
In-stent restenosis (ISR) following interventional therapy is a fatal clinical complication. Current evidence indicates that neointimal hyperplasia driven by uncontrolled proliferation of vascular smooth muscle cells (VSMC) is a major cause of restenosis. This implies that inhibiting VSMC proliferation may be an attractive approach for preventing in-stent restenosis.
View Article and Find Full Text PDFPharmacoepidemiol Drug Saf
January 2025
Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina, USA.
Purpose: Increases in adult stimulant prescribing pose a potential risk due to the higher prevalence of contraindicated conditions among this population. We sought to identify patient, provider, and visit characteristics predictive of potentially inappropriate adult stimulant prescriptions.
Methods: We conducted a repeated cross-sectional study using the National Ambulatory Medical Care Survey, a nationally representative weighted sample of 5 453 702 723 ambulatory care visits from 2012 to 2019.
Eur J Cardiothorac Surg
December 2024
Coronary Center, Department of Thoracic and Cardiovascular Surgery, Miller Family Heart, Vascular, & Thoracic Institute, Cleveland Clinic, Cleveland, Ohio, USA.
Open Heart
January 2025
Department of Cardiology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
Background: Visual assessment of coronary CT angiography (CCTA) is time-consuming, influenced by reader experience and prone to interobserver variability. This study evaluated a novel algorithm for coronary stenosis quantification (atherosclerosis imaging quantitative CT, AI-QCT).
Methods: The study included 208 patients with suspected coronary artery disease (CAD) undergoing CCTA in Perfusion Imaging and CT Coronary Angiography With Invasive Coronary Angiography-1.
Open Heart
January 2025
Department of Molecular and Clinical Medicine, University of Gothenburg Institute of Medicine, Gothenburg, Sweden.
Purpose: We examined whether end-to-end deep-learning models could detect moderate (≥50%) or severe (≥70%) stenosis in the left anterior descending artery (LAD), right coronary artery (RCA) or left circumflex artery (LCX) in iodine contrast-enhanced ECG-gated coronary CT angiography (CCTA) scans.
Methods: From a database of 6293 CCTA scans, we used pre-existing curved multiplanar reformations (CMR) images of the LAD, RCA and LCX arteries to create end-to-end deep-learning models for the detection of moderate or severe stenoses. We preprocessed the images by exploiting domain knowledge and employed a transfer learning approach using EfficientNet, ResNet, DenseNet and Inception-ResNet, with a class-weighted strategy optimised through cross-validation.
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