To investigate the patterns and diagnostic implications of coronary arterial lesion calcification by CT angiography (CTA) using a novel, cross-sectional grading method, we studied 371 patients enrolled in the CorE-64 study who underwent CTA and invasive angiography for detecting coronary artery stenoses by quantitative coronary angiography (QCA). The number of quadrants involving calcium on a cross-sectional view for ≥ 30 and ≥ 50 % lesions in 4,511 arterial segments was assessed by CTA according to: noncalcified, mild (one-quadrant), moderate (two-quadrant), severe (three-quadrant) and very severe (four-quadrant calcium). Area under the receiver operating characteristic curve (AUC) were used to evaluate CTA diagnostic accuracy and agreement versus. QCA for plaque types. Only 4 % of ≥ 50 % stenoses by QCA were very severely calcified while 43 % were noncalcified. AUC for CTA to detect ≥ 50 % stenoses by QCA for non-calcified, mildly, moderately, severely, and very severely calcified plaques were 0.90, 0.88, 0.83, 0.76 and 0.89, respectively (P < 0.05). In 198 lesions with severe calcification, the presence or absence of a visible residual lumen by CTA was associated with ≥ 50 % stenosis by QCA in 20.3 and 76.9 %, respectively. Kappa was 0.93 for interobserver variability in evaluating plaque calcification. We conclude that calcification of individual coronary artery lesions can be reliably graded using CTA. Most ≥ 50 % coronary artery stenoses are not or only mildly calcified. If no residual lumen is seen on CTA, calcified lesions are predictive of ≥ 50 % stenoses and vice versa. CTA diagnostic accuracy for detecting ≥ 50 % stenoses is reduced in lesions with more than mild calcification due to lower specificity.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3796157 | PMC |
http://dx.doi.org/10.1007/s10554-013-0240-8 | DOI Listing |
Biotech Histochem
May 2024
Department of Applied Biological Chemistry, Graduate School of Agriculture, Kindai University, Nara City, Japan.
Abdominal aortic aneurysm (AAA) is a vascular disease that involves aortic wall dilation. Cigarette smoking is an established risk factor and rupture, and nicotine may be a major contributor to the onset of AAA. In humans the condition is associated with stenosis of the vasa vasorum (VV), which may be caused by nicotine.
View Article and Find Full Text PDFExpert Rev Med Devices
September 2023
Operative Unit of Vascular Surgery, IRCCS Policlinico San Donato, San Donato Milanese, Milan, Italy.
Comput Methods Biomech Biomed Engin
May 2023
Department of Cardiology, Institute of Medical Science, BHU, Varanasi, Uttar Pradesh, India.
The study of patient-specific human arterial flow dynamics is well known to face challenges like a) apt geometric modelling, b) bifurcation zone meshing, and c) capturing the hemodynamic prone to variations with multiple disease complications. Due to aneurysms and stenosis in the same arterial network, the blood flow dynamics get affected, which needs to be explored. This study develops a new protocol for accurate geometric modelling, bifurcation zone meshing and numerically investigates the arterial network with abdominal aortic aneurysms (AAA) and right internal iliac stenosis (RIIAS).
View Article and Find Full Text PDFAnn Vasc Surg
March 2022
Vascular Surgery, Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Sant'Orsola-Malpighi Hospital, Bologna, Italy.
Introduction: Preserving pelvic circulation is crucial to minimize the risk of spinal cord and colonic ischemia, especially during the endovascular treatment of extended thoraco-abdominal aneurysm (TAAA) after previous open repair (OR).
Case Report: A 78-years-old patient, previously treated for AAA with OR and reimplantation of inferior mesenteric artery (IMA), has presented with 9 cm type-III TAAA and underwent to a multi-stage endovascular procedure. Two thoracic endografts, t-Branch and a straight endograft by Cook Zenith platform were deployed.
Biomech Model Mechanobiol
December 2021
Faculty of Science and Engineering, Swansea University, Swansea, UK.
This study presents an application of machine learning (ML) methods for detecting the presence of stenoses and aneurysms in the human arterial system. Four major forms of arterial disease-carotid artery stenosis (CAS), subclavian artery stenosis (SAS), peripheral arterial disease (PAD), and abdominal aortic aneurysms (AAA)-are considered. The ML methods are trained and tested on a physiologically realistic virtual patient database (VPD) containing 28,868 healthy subjects, adapted from the authors previous work and augmented to include disease.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!