The aim of the present study was to determine whether coronary stenosis and computed tomography-derived fractional flow reserve (CT-FFR), detected by coronary computed tomography angiography (CCTA), can potentially contribute to distinguish acute myocardial infarction (AMI) from unstable angina (UA). The study retrospectively collected data from consecutive patients who were admitted with obstructive coronary artery disease (CAD) and who received CCTA and invasive coronary angiography (ICA) as part of their clinical workup. According to the inclusion criteria, the patients were divided into the AMI group and UA group, and the basic clinical data, CCTA stenosis degree and CT-FFR values were compared between the two groups. Univariate and multivariate logistic regression methods were used to analyze the association between ≥70% CCTA stenosis, ≤0.80 CT-FFR and AMI. A diagnostic model of AMI was established (model 1, ≤0.80 CT-FFR; model 2, ≥70% CCTA stenosis; and model 3, ≤0.80 CT-FFR combined with ≥70% CCTA stenosis), and the diagnostic efficacy of the three models for AMI was compared. The significance level was set at P<0.05. A total of 116 participants were finally enrolled in this study. There were 37 patients in the AMI group, with an average age of 62.06±7.74 years, and 79 patients in the UA group, with an average age of 58.11±10.0 years; there was no significant difference in age (P>0.05). The multivariate regression analysis revealed that ≤0.80 CT-FFR (HR=28.074; 95% CI: 5.712-137.973; P<0.001), and ≥70% CCTA stenosis (HR=10.796; 95% CI: 2.566-45.425; P=0.001) were independent risk factors for AMI. The diagnostic model of ≤0.80 CT-FFR combined with ≥70% CCTA stenosis (AUC=0.914; 95% CI: 0.847-0.958) exhibited increased diagnosis performance than the ≤0.80 CT-FFR model (AUC=0.865; 95% CI: 0.790-0.922; P=0.0060) and the ≥70% CCTA stenosis model (AUC=0.827; 95% CI: 0.745-0.891; P=0.0008). Collectively, it was demonstrated that ≤0.80 CT-FFR and ≥70% CCTA stenosis were independent risk factors for the diagnosis of AMI, and the combination of CT-FFR and CCTA stenosis further improved AMI diagnosis performance.
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http://dx.doi.org/10.3892/etm.2023.12258 | DOI Listing |
J Cardiovasc Comput Tomogr
January 2025
Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA; Department of Diagnostic and Interventional Radiology, University Medical Center of Johannes Gutenberg-University, Mainz, Germany; German Centre for Cardiovascular Research, Mainz, Germany.
Background: This study aimed to determine whether artificial intelligence (AI)-based automated assessment of left atrioventricular coupling index (LACI) can provide incremental value above other traditional risk factors for predicting mortality among patients with severe aortic stenosis (AS) undergoing coronary CT angiography (CCTA) before transcatheter aortic valve replacement (TAVR).
Methods: This retrospective study evaluated patients with severe AS who underwent CCTA examination before TAVR between September 2014 and December 2020. An AI-prototype software fully automatically calculated left atrial and left ventricular end-diastolic volumes and LACI was defined by the ratio between them.
Clin Radiol
December 2024
Department of Radiology, Division of General Radiology, Medical University of Graz, Auenbruggerplatz 9, 8036 Graz, Austria; Department of Radiology and Nuclear Medicine, University Hospital Wiener Neustadt, Corvinusring 3-5, 2700 Wiener Neustadt, Austria.
Aim: To assess the diagnostic potential of a noncoronary-dedicated pre-TAVR CT angiography (CTA) conducted as a prospective ECG-gated scan without premedication and standard cardiac reconstructions in evaluating bystander coronary artery disease (CAD) against invasive coronary angiography (ICA) as the gold standard.
Materials And Methods: This retrospective study included 232 patients who underwent both CTA and ICA as part of their pre-TAVR evaluation. Exclusion criteria included prior stent, pacemaker, coronary artery bypass, or valve surgery.
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 PDFAim: To evaluate characteristics of atherosclerotic plaques (ASP) remaining after percutaneous coronary intervention (PCI) in patients with acute coronary syndrome (ACS) by coronary computed tomography angiography (CCTA).
Material And Methods: Among 249 patients (193 men) with ACS aged 58±10 years, 183 (73.5%) had myocardial infarction, 66 (26.
Circ Cardiovasc Imaging
January 2025
Cardiovascular Center Aalst, Onze-Lieve-Vrouwziekenhuis (OLV) Clinic, Aalst, Belgium (M. Belmonte, P.P., M.M.V., M. Beles, H.O., R.S., G.E., M.S., R.D., W.H., J.V.K., J.B., M.V.).
Background: Coronary computed tomography angiography (CCTA) is emerging as a valuable tool for noninvasive surveillance of cardiac allograft vasculopathy (CAV) in patients with heart transplant (HTx). We assessed the diagnostic performance of a comprehensive CCTA-based approach compared with the invasive reference, which includes invasive coronary angiography, intravascular ultrasound, and fractional flow reserve, for detecting CAV.
Methods: This was a multicenter prospective study including 37 patients with HTx who underwent CCTA, invasive coronary angiography, intravascular ultrasound, and fractional flow reserve.
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