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An anomalous origin of the left main coronary artery arising from the left ventricular outflow tract is an exceedingly rare congenital coronary anomaly, typically associated with impaired myocardial perfusion. Here, we report the case of a 67-year-old asymptomatic woman in whom an anomalous origin of the left main coronary artery, arising from the left ventricular outflow tract below the aortic valve, was incidentally identified during routine preoperative cardiac evaluation. Despite the anatomical abnormality, the patient exhibited no clinical or imaging evidence of myocardial ischemia. This finding is likely explained by a marked dilation of the right coronary artery and the presence of well-developed collateral circulation supplying the left coronary system. With no evidence of ischemia and maintained ventricular function, a non-interventional approach was deemed appropriate. This case highlights the importance of comprehensive anatomical and functional assessment in detecting clinically silent coronary anomalies and underscores the value of advanced cardiac imaging in the preoperative evaluation of elderly patients undergoing non-cardiac procedures.
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http://dx.doi.org/10.3389/fcvm.2025.1640534 | DOI Listing |
JMIR Med Inform
September 2025
Departments of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, 600 Tianhe Road, Guangzhou, Guangdong, 510630, China, 86 18922109279, 86 20852523108.
Background: Despite the Coronary Artery Reporting and Data System (CAD-RADS) providing a standardized approach, radiologists continue to favor free-text reports. This preference creates significant challenges for data extraction and analysis in longitudinal studies, potentially limiting large-scale research and quality assessment initiatives.
Objective: To evaluate the ability of the generative pre-trained transformer (GPT)-4o model to convert real-world coronary computed tomography angiography (CCTA) free-text reports into structured data and automatically identify CAD-RADS categories and P categories.
Eur J Cardiothorac Surg
September 2025
Department of Cardiothoracic Surgery, Weill Cornell Medicine, New York, NY, USA.
Objectives: The no-touch (NT) technique for saphenous vein (SV) harvesting in coronary artery bypass surgery preserves perivascular tissue and has been proposed to improve vein graft patency compared to conventional (CON) harvesting. However, recent large randomized clinical trials (RCTs) have reported conflicting results. We performed a meta-analysis of all available RCTs comparing graft patency and clinical outcomes between NT-SV and CON-SV harvesting techniques.
View Article and Find Full Text PDFJ Invasive Cardiol
September 2025
Department of Cardiology, Centre Hospitalier La Rochelle Ré Aunis, La Rochelle, France.
Objectives: The management of patients with calcified de novo lesions remains a major clinical challenge even in the era of drug-eluting stents (DES). Drug-coated balloon (DCB) therapy has emerged as an alternative to DES to treat de novo lesions. Nevertheless, the management of calcified lesions using intravascular lithotripsy (IVL) combined with DCB to treat de novo lesions has not been investigated.
View Article and Find Full Text PDFJ Invasive Cardiol
September 2025
Newark Beth Israel Medical Center, Newark, New Jersey.
Objectives: The authors hypothesized that the origin of the right coronary artery (RCA) is a direct continuation of the major aortic arch branches (MAAB) takeoff plane, which may have implications for brachiocephalic interventions and next generation transcatheter aortic valve intervention (TAVI) embolic protection devices (EPDs).
Methods: In this single-center, retrospective, cross-sectional study, the authors analyzed computed tomographic angiography (CTA) images from 92 patients undergoing TAVI evaluation to determine the spatial relationship between the origin of the RCA and the MAAB takeoff plane. Patients with prior cardiothoracic or aortic interventions and those with anomalous RCA origin were excluded.
PLoS One
September 2025
Department of Medicine, The Red Rogers Centre for Heart Research, Peter Munk Cardiac Centre, University Health Network, University of Toronto, Toronto, Ontario, Canada.
Background: In order to seriously impact the global burden of heart failure (HF) and coronary artery disease (CAD), identifying at-risk individuals as early as possible is vital. Risk calculator tools in wide clinical use today are informed by traditional statistical methods that have historically yielded only modest prediction accuracy.
Methods: This study uses machine learning algorithms to generate predictions models for the development and progression of severe HF and CAD.