Aim: To assess the temporal trends in the usage pattern of non-invasive testing before invasive coronary angiography (ICA) and its diagnostic yield in patients with suspected coronary artery disease (CAD).
Methods: Cross-sectional observational multicenter study of 4805 consecutive patients (60% male, mean age 66 ± 10 years) with suspected CAD undergoing elective ICA due to angina pectoris in two centers, from 2008 to 2017. The use of noninvasive testing and the proportion of patients with obstructive CAD (defined as the presence of at least one ≥50% stenosis on ICA) were assessed.
Results: There were 4038 (84%) patients referred for ICA with positive noninvasive test, mainly SPECT (38%, n = 1828) and exercise ECG (36%, n = 1731). Obstructive CAD was found in 54.5% (n = 2621) of the patients and 37.9% (n = 1822) underwent revascularization. The prevalence of obstructive CAD was higher in patients with vs. without previous noninvasive testing (55.8% vs. 48.1%, respectively, P < 0.001) and tended to decrease during the study period (P for trend <0.001). Both the presence of obstructive CAD and revascularization rate were higher in patients who underwent anatomical evaluation with CCTA compared with noninvasive functional tests (P = 0.001 and P = 0.018, respectively). The number of patients referred after exercise testing and SPECT decreased (p for trend 0.005 and 0.006, respectively) and after CCTA and stress CMR increased (both P for trend <0.001). The proportion of patients referred without previous testing remained stable.
Conclusion: Nearly half of the patients undergoing ICA for suspected CAD did not have obstructive coronary lesions. This proportion tended to increase over the 10-year span of this study. Better clinical assessment tools and diagnostic pathways for stable CAD are warranted.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1097/MCA.0000000000000951 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
CAD-Q (COPD-Asthma Differentiation Questionnaire): Performance of a new diagnostic score to differentiate between COPD and asthma in adults.
BMC Pulm Med
January 2025
School of Medicine, Universidad de La Sabana, Chía, Colombia.
Background: Chronic obstructive pulmonary disease (COPD) and asthma are the two most prevalent chronic respiratory diseases, significantly impacting public health. Utilizing clinical questionnaires to identify and differentiate patients with COPD and asthma for further diagnostic procedures has emerged as an effective strategy to address this issue. We developed a new diagnostic tool, the COPD-Asthma Differentiation Questionnaire (CAD-Q), to differentiate between COPD and asthma in adults.
View Article and Find Full Text PDFA Machine Learning Model Using Cardiac CT and MRI Data Predicts Cardiovascular Events in Obstructive Coronary Artery Disease.
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 PDFSystemic Lupus Erythematosus as a Risk Factor for Coronary Artery Disease.
Cureus
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 PDFDiagnostic accuracy in coronary CT angiography analysis: artificial intelligence versus human assessment.
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.
Automated classification of coronary LEsions fRom coronary computed Tomography angiography scans with an updated deep learning model: ALERT study.
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 PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!