Background: Whether reproductive factors are associated with coronary artery disease (CAD) has been debated. The aim of this study was to investigate etiologic associations of a wide range of reproductive factors of women with the presence of angiographic obstructive CAD.
Materials And Methods: Study data were obtained from a nationwide registry that enrolled 687 Korean women (59.9 ± 11.4 years) with chest pain undergoing invasive coronary angiography (ICA). Obstructive CAD was defined as ≥50% luminal stenosis of one or more epicardial coronary arteries in ICA. Information on reproductive history, including ages at menarche and menopause, duration of reproductive capacity, number of pregnancies, hormonal replacement therapy, and history of twin pregnancy, was obtained using a standardized questionnaire.
Results: A total of 178 women (25.9%) had obstructive CAD. Multivariable logistic regression analysis identified that later age at menarche (odds ratio [OR] = 1.265, 95% confidence interval [CI] = 1.064-1.504, p = 0.008, per year) and increased number of pregnancies (OR = 1.223, 95% CI = 1.026-1.457, p = 0.025, per pregnancy) were the independent predictors of obstructive CAD even after controlling for potential confounders, including age, diabetes mellitus, hypertension, dyslipidemia, renal function, high-density lipoprotein level, white blood cell count, hemoglobin, and E/e'.
Conclusions: Later age at menarche and increased number of pregnancies may be reproductive risk factors for angiographic obstructive CAD, suggesting the important role of hormonal status in the development of CAD.
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http://dx.doi.org/10.1089/jwh.2015.5381 | DOI Listing |
Radiology
January 2025
From the Department of Cardiology (T.P., K.H., T.G., A.L., E.G., A.U., J.G.D., P.H.), MIRACL.ai (Multimodality Imaging for Research and Analysis Core Laboratory: and Artificial Intelligence) (T.P., S.T., K.H., T.G., A.L., E.G., A.U., J.G.D., P.H.), Inserm MASCOT-UMRS 942 (T.P., K.H., T.A.S., T.G., A.L., E.G., A.U., J.G.D., P.H.), and Department of Radiology (T.P., V.B., L.H., T.G.), Université Paris Cité, University Hospital of Lariboisière, Assistance Publique-Hôpitaux de Paris, Paris, France; Cardiovascular Magnetic Resonance Laboratory (T.P., T.H., T.U., F.S., S.C., P.G., J.G.) and Cardiac Computed Tomography Laboratory (T.P., T.H., T.L., B.C., T.U., F.S., S.C., H.B., A.N., M.A., P.G., J.G.), Hôpital Privé Jacques Cartier, Institut Cardiovasculaire Paris Sud, Ramsay Santé, 6 Avenue du Noyer Lambert, 91300 Massy, France; Scientific Partnerships, Siemens Healthcare France, Saint-Denis, France (S.T.); Department of Cardiology, Hôpital Universitaire de Bruxelles-Hôpital Erasme, Brussels, Belgium (A.U.); and Department of Cardiovascular Imaging, American Hospital of Paris, Neuilly, France (O.V., M.S.).
Background Multimodality imaging is essential for personalized prognostic stratification in suspected coronary artery disease (CAD). Machine learning (ML) methods can help address this complexity by incorporating a broader spectrum of variables. Purpose To investigate the performance of an ML model that uses both stress cardiac MRI and coronary CT angiography (CCTA) data to predict major adverse cardiovascular events (MACE) in patients with newly diagnosed CAD.
View Article and Find Full Text PDFCureus
December 2024
Adult Cardiology, Uganda Heart Institute, Kampala, UGA.
Acute coronary syndrome is the leading cause of death worldwide, with the highest rates occurring in low-income global regions. This is possibly due to increasing levels of urbanization, which are accompanied by changes in diet and lifestyle, the most common risk factors for coronary artery disease (CAD). Risk factors for CAD are divided into traditional and non-traditional risk factors.
View Article and Find Full Text PDFOpen 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.
Eur Radiol
January 2025
Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, University of Amsterdam, Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands.
Objectives: The use of deep learning models for quantitative measurements on coronary computed tomography angiography (CCTA) may reduce inter-reader variability and increase efficiency in clinical reporting. This study aimed to investigate the diagnostic performance of a recently updated deep learning model (CorEx-2.0) for quantifying coronary stenosis, compared separately with two expert CCTA readers as references.
View Article and Find Full Text PDFCureus
December 2024
Department of Invasive Cardiology, University Hospital "St. Marina", Varna, BGR.
Background Cardiovascular diseases (CVDs) are the leading cause of mortality worldwide, with coronary artery disease (CAD) being the primary contributor. Periodontitis, a common non-communicable disease, has been associated with an increased risk of CVD. Previous studies have suggested a link between the severity of periodontitis and the degree of coronary artery obstruction.
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