Coronary Artery Disease Reporting And Data System Research Articles

A token-mixer architecture for CAD-RADS classification of coronary stenosis on multiplanar reconstruction CT images

Background and objectiveIn patients with suspected Coronary Artery Disease (CAD), the severity of stenosis needs to be assessed for precise clinical management. An automatic deep learning-based algorithm to classify coronary stenosis lesions according to the Coronary Artery Disease Reporting and Data System (CAD-RADS) in multiplanar reconstruction images acquired with Coronary Computed Tomography Angiography (CCTA) is proposed. MethodsIn this retrospective study, 288 patients with suspected CAD who underwent CCTA scans were included. To model long-range semantic information, which is needed to identify and classify stenosis with challenging appearance, we adopted a token-mixer architecture (ConvMixer), which can learn structural relationship over the whole coronary artery. ConvMixer consists of a patch embedding layer followed by repeated convolutional blocks to enable the algorithm to learn long-range dependences between pixels. To visually assess ConvMixer performance, Gradient-Weighted Class Activation Mapping (Grad-CAM) analysis was used. ResultsExperimental results using 5-fold cross-validation showed that our ConvMixer can classify significant coronary artery stenosis (i.e., stenosis with luminal narrowing ≥50%) with accuracy and sensitivity of 87% and 90%, respectively. For CAD-RADS 0 vs. 1–2 vs. 3–4 vs. 5 classification, ConvMixer achieved accuracy and sensitivity of 72% and 75%, respectively. Additional experiments showed that ConvMixer achieved a better trade-off between performance and complexity compared to pyramid-shaped convolutional neural networks. ConclusionsOur algorithm might provide clinicians with decision support, potentially reducing the interobserver variability for coronary artery stenosis evaluation.

Comparing the Accuracy of Diagnostic Tests When Disease Is Characterized by an Ordinal Scale.

In diagnostic medicine, the true disease status of a patient is often represented on an ordinal scale-for example, cancer stage (0, I, II, III, or IV) or coronary artery disease severity measured using the Coronary Artery Disease Reporting and Data System (CAD-RADS) scale (none, minimal, mild, moderate, severe, or occluded). With advances in quantitation of diagnostic images and in artificial intelligence (AI), both supervised and unsupervised algorithms are being developed to help physicians correctly grade disease. Most of the diagnostic accuracy literature deals with binary disease status (disease present or absent); however, tests diagnosing ordinal-scaled diseases should not be reduced to a binary status just to simplify diagnostic accuracy testing. In this paper, we propose different characterizations of ordinal-scale accuracy for different clinical use scenarios, along with methods for comparing tests. In the simplest scenario, just the proportion of correct grades is considered; other scenarios address the magnitude and direction of misgrading; and at the other extreme, a weighted accuracy measure with weights based on the relative costs of different types of misgrading is presented. The various scenarios are illustrated using a coronary artery disease example where the accuracy of AI algorithms in providing patients with the correct CAD-RADS grade is assessed.

Deep learning-based noise reduction for coronary CT angiography: using four-dimensional noise-reduction images as the ground truth.

To assess low-contrast areas such as plaque and coronary artery stenosis, coronary computed tomography angiography (CCTA) needs to provide images with lower noise without increasing radiation doses. To develop a deep learning-based noise-reduction method for CCTA using four-dimensional noise reduction (4DNR) as the ground truth for supervised learning. \We retrospectively collected 100 retrospective ECG-gated CCTAs. We created 4DNR images using non-rigid registration and weighted averaging three timeline CCTA volumetric data with intervals of 50 ms in the mid-diastolic phase. Our method set the original reconstructed image as the input and the 4DNR as the target image and obtained the noise-reduced image via residual learning. We evaluated the objective image quality of the original and deep learning-based noise-reduction (DLNR) images based on the image noise of the aorta and the contrast-to-noise ratio (CNR) of the coronary arteries. Further, a board-certified radiologist evaluated the blurring of several heart structures using a 5-point Likert scale subjectively and assigned a coronary artery disease reporting and data system (CAD-RADS) category independently. DLNR CCTAs showed 64.5% lower image noise (P < 0.001) and achieved a 2.9 times higher CNR of coronary arteries than that in original images, without significant blurring in subjective comparison (P > 0.1). The intra-observer agreement of CAD-RADS in the DLNR image was excellent (0.87, 95% confidence interval = 0.77-0.99) with original CCTAs. Our DLNR method supervised by 4DNR significantly reduced the image noise of CCTAs without affecting the assessment of coronary stenosis.

Deep learning-based detection of functionally significant stenosis in coronary CT angiography.

Patients with intermediate anatomical degree of coronary artery stenosis require determination of its functional significance. Currently, the reference standard for determining the functional significance of a stenosis is invasive measurement of the fractional flow reserve (FFR), which is associated with high cost and patient burden. To address these drawbacks, FFR can be predicted non-invasively from a coronary CT angiography (CCTA) scan. Hence, we propose a deep learning method for predicting the invasively measured FFR of an artery using a CCTA scan. The study includes CCTA scans of 569 patients from three hospitals. As reference for the functional significance of stenosis, FFR was measured in 514 arteries in 369 patients, and in the remaining 200 patients, obstructive coronary artery disease was ruled out by Coronary Artery Disease-Reporting and Data System (CAD-RADS) category 0 or 1. For prediction, the coronary tree is first extracted and used to reconstruct an MPR for the artery at hand. Thereafter, the coronary artery is characterized by its lumen, its attenuation and the area of the coronary artery calcium in each artery cross-section extracted from the MPR using a CNN. Additionally, characteristics indicating the presence of bifurcations and information indicating whether the artery is a main branch or a side-branch of a main artery are derived from the coronary artery tree. All characteristics are fed to a second network that predicts the FFR value and classifies the presence of functionally significant stenosis. The final result is obtained by merging the two predictions. Performance of our method is evaluated on held out test sets from multiple centers and vendors. The method achieves an area under the receiver operating characteristics curve (AUC) of 0.78, outperforming other works that do not require manual correction of the segmentation of the artery. This demonstrates that our method may reduce the number of patients that unnecessarily undergo invasive measurements.

The association between cardiovascular risk factors and coronary CT angiography findings for predicting obstructive coronary artery disease in Korean patients with stable chest pain

Abstract Background/Introduction The 2019 European Society of Cardiology (ESC) guidelines currently recommend the use of coronary computed tomography angiography (CCTA) as the initial test for diagnosing coronary artery disease (CAD) in symptomatic patients in whom obstructive CAD cannot be excluded by clinical assessment alone. Purpose The purpose of this study is to identify the prevalence of obstructive CAD in patients with stable chest pain, and the correlation between several clinical cardiovascular disease (CVD) risk factors and CCTA findings presented by Coronary Artery Disease Reporting and Data System (CAD-RADS). Methods The present study is a single-center retrospective cross-sectional study. A total of 1,892 patients with stable chest pain who underwent CCTA were enrolled in this study. Diamond-Forrester classification, Framingham risk score (FRS), atherosclerotic CVD (ASCVD) 10-year risk score, coronary artery calcium score (CACS) and CAD-RADS category were obtained from every patient. Results Among 1,892 patients (mean age, 60.5±8.6 years; men, 59.3%), 356 (18.8%) had obstructive CAD according to CCTA. Patients with high and intermediate ASCVD 10-year risk score had 2.59 times (aOR 2.59, 95% CI; 1.58 to 4.23) and 1.66 times (aOR 1.66, 95% CI; 1.04 to 2.65) higher odds of having obstructive CAD than patients with low ASCVD 10-year risk score, respectively (adjusted for Diamond-Forrester classification and CACS group). Higher ASCVD risk scores were significantly associated with higher CAD-RADS category (p&amp;lt;0.001), and patients with CAD-RADS category 3 had ASCVD 10-year risk score of 20.1±12.7. CACS showed the highest discrimination in presence of obstructive CAD, followed by ASCVD 10-year risk score, FRS, and Diamond-Forrester classification (AUC: 0.821 [95% CI; 0.797–0.845]; 0.711 [95% CI; 0.683–0.740]; 0.675 [95% CI; 0.646–0.704]; 0.600 [95% CI; 0.569–0.632], respectively). Conclusion This is the first study of CCTA findings in stable chest pain patients in Korea. The prevalence of obstructive CAD in patients with stable chest pain was 18.8%. Higher ASCVD score is significantly associated with presence of obstructive CAD and higher CAD-RADS category. As coronary stenosis of 50%-69% had a mean ASCVD score of 20.1, we should consider CCTA for identifying obstructive CAD in patients with ASCVD score over 20 with stable chest pain. Funding Acknowledgement Type of funding sources: None.

CAD-RADS™ 2.0 - 2022 Coronary Artery Disease - Reporting and Data System An Expert Consensus Document of the Society of Cardiovascular Computed Tomography (SCCT), the American College of Cardiology (ACC), the American College of Radiology (ACR) and the North America Society of Cardiovascular Imaging (NASCI).

Coronary Artery Disease Reporting and Data System (CAD-RADS) was created to standardize reporting system for patients undergoing coronary CT angiography (CCTA) and to guide possible next steps in patient management. The goal of this updated 2022 CAD-RADS 2.0 is to improve the initial reporting system for CCTA by considering new technical developments in Cardiac CT, including data from recent clinical trials and new clinical guidelines. The updated CAD-RADS classification will follow an established framework of stenosis, plaque burden, and modifiers, which will include assessment of lesion-specific ischemia using CT fractional-flow-reserve (CT-FFR) or myocardial CT perfusion (CTP), when performed. Similar to the method used in the original CAD-RADS version, the determinant for stenosis severity classification will be the most severe coronary artery luminal stenosis on a per-patient basis, ranging from CAD-RADS 0 (zero) for absence of any plaque or stenosis to CAD-RADS 5 indicating the presence of at least one totally occluded coronary artery. Given the increasing data supporting the prognostic relevance of coronary plaque burden, this document will provide various methods to estimate and report total plaque burden. The addition of P1 to P4 descriptors are used to denote increasing categories of plaque burden. The main goal of CAD-RADS, which should always be interpreted together with the impression found in the report, remains to facilitate communication of test results with referring physicians along with suggestions for subsequent patient management. In addition, CAD-RADS will continue to provide a framework of standardization that may benefit education, research, peer-review, artificial intelligence development, clinical trial design, population health and quality assurance with the ultimate goal of improving patient care. Keywords: Coronary Artery Disease, Coronary CTA, CAD-RADS, Reporting and Data System, Stenosis Severity, Report Standardization Terminology, Plaque Burden, Ischemia Supplemental material is available for this article. This article is published synchronously in Radiology: Cardiothoracic Imaging, Journal of Cardiovascular Computed Tomography, JACC: Cardiovascular Imaging, Journal of the American College of Radiology, and International Journal for Cardiovascular Imaging. © 2022 Society of Cardiovascular Computed Tomography. Published by RSNA with permission.

Commentary on: computed tomography-derived fractional flow reserve (FFRCT) has no additional clinical impact over the anatomical Coronary Artery Disease - Reporting and Data System (CAD-RADS) in real-world elective healthcare of coronary artery disease

In the paper by Hamilton et al.,1 the authors retrospectively review their practice of computed tomography (CT) coronary angiography (CTCA) in stable chest pain, assessing the ability of Coronary Artery Disease - Reporting and Data System (CAD-RADS)2 to predict clinical outcomes, and then adding fractional flow reserve derived by CT (FFRCT), in selected patients, to see if this increased accuracy. Alongside clinical assessment, they considered CAD-RADS 0–2 as a predictor of negative outcome and CAD-RADS 3–5 as a positive predictor of conventional stress testing, invasive FFR <0.8, revascularisation, or clinical decision to treat medically for typical angina in CAD-RADS 4.

Computed tomography-derived fractional flow reserve (FFRCT) has no additional clinical impact over the anatomical Coronary Artery Disease - Reporting and Data System (CAD-RADS) in real-world elective healthcare of coronary artery disease

To evaluate the impact of computed tomography-derived fractional flow reserve (FFRCT) compared to the anatomical Coronary Artery Disease - Reporting and Data System (CAD-RADS) in the elective assessment of coronary artery disease in real-world cardiology practise. A retrospective review was undertaken of 1,239 coronary CT examinations from August 2018 to December 2019 with a minimum follow-up period of 1 year. Coronary disease was classified according to the CAD-RADS system. A non-occlusive ≥30% maximum diameter stenosis was considered eligible for FFRCT. Lesion-specific FFRCT and FFR were considered positive if≤0.80. The patients were followed up using the hospital radiology information system and the electronic patient record. A positive outcome was defined by a subsequent invasive angiogram (ICA) showing disease requiring revascularisation or FFR ≤0.80 or a positive stress test or medical therapy for angina in CAD-RADS 4. Of the 1,145 analysable studies (mean follow up 618±153 days) the incidence of a positive result was 7% with a 5.4% elective revascularisation rate. Two hundred and forty-five patients (CAD-RADS 2-4) had FFRCT. FFRCT reduced the accuracy of the CAD-RADS grade from 91% to 78.4% (p<0.001). In CAD-RADS 2, the accuracy is reduced from 99% to 90.7% (p=0.005), and in CAD-RADS 3 from 93.9% to 67.7% (p<0.001). In CAD-RADS 4, FFRCT increases accuracy from 69.4% to 75.5% (p=0.025), but 89.8% of FFRCT are positive and specificity is low (26.7%). In the present "real-world" practise, FFRCT does not improve standard radiological assessment of coronary disease graded by the CAD-RADS alone.

Added Value of CCTA-Derived Features to Predict MACEs in Stable Patients Undergoing Coronary Computed Tomography.

Clinical evidence has emphasized the importance of coronary plaques’ characteristics, rather than lumen stenosis, for the outcome of cardiovascular events. Coronary computed tomographic angiography (CCTA) has a well-established role as a non-invasive tool for assessing plaques. The aim of this study was to compare clinical characteristics and CCTA-derived information of stable patients with non-severe plaques in predicting major adverse cardiac events (MACEs) during follow-up. We retrospectively selected 371 patients (64% male) who underwent CCTA in our center from March 2016 to January 2021 with Coronary Artery Disease—Reporting and Data System (CAD-RADS) 0 to 3. Of those, 198 patients (53% male) had CAD-RADS 0 to 1. Among them, 183 (49%) had normal pericoronary fat attenuation index (pFAI), while 15 (60% male) had pFAI ≥ 70.1 Hounsfield unit (HU). The remaining 173 patients (76% male) had CAD-RADS 2 to 3 and were divided into patients with at least one low attenuation plaque (LAP) and patients without LAPs (n-LAP). Compared to n-LAP, patients with LAPs had higher pFAI (p = 0.005) and had more plaques than patients with n-LAP. Presence of LAPs was significantly higher in elderly (p < 0.001), males (p < 0.001) and patients with traditional risk factors (hypertension p = 0.0001, hyperlipemia p = 0.0003, smoking p = 0.0003, diabetes p = <0.0001, familiarity p = 0.0007). Among patients with CAD-RADS 0 to 1, the ones with pFAI ≥ 70.1 HU were more often hyperlipidemic (p = 0.05) and smokers (p = 0.007). Follow-up (25,4 months, range: 17.6–39.2 months) demonstrated that LAP and pFAI ≥ 70.1 significantly and independently (p = 0.04) predisposed to outcomes (overall mortality and interventional procedures). There is an added value of CCTA-derived features in stratifying cardiovascular risk in low- to intermediate-risk patients with non-severe, non-calcified coronary plaques. This is of utmost clinical relevance as it is possible to identify a subset of patients with increased risk who need strengthening in therapeutic management and closer follow-up even in the absence of severe CAD. Further studies are needed to evaluate the effect of medical treatments on pericoronary inflammation and plaque composition.

Predicting CT-Based Coronary Artery Disease Using Vascular Biomarkers Derived from Fundus Photographs with a Graph Convolutional Neural Network.

The study population contains 145 patients who were prospectively recruited for coronary CT angiography (CCTA) and fundoscopy. This study first examined the association between retinal vascular changes and the Coronary Artery Disease Reporting and Data System (CAD-RADS) as assessed on CCTA. Then, we developed a graph neural network (GNN) model for predicting the CAD-RADS as a proxy for coronary artery disease. The CCTA scans were stratified by CAD-RADS scores by expert readers, and the vascular biomarkers were extracted from their fundus images. Association analyses of CAD-RADS scores were performed with patient characteristics, retinal diseases, and quantitative vascular biomarkers. Finally, a GNN model was constructed for the task of predicting the CAD-RADS score compared to traditional machine learning (ML) models. The experimental results showed that a few retinal vascular biomarkers were significantly associated with adverse CAD-RADS scores, which were mainly pertaining to arterial width, arterial angle, venous angle, and fractal dimensions. Additionally, the GNN model achieved a sensitivity, specificity, accuracy and area under the curve of 0.711, 0.697, 0.704 and 0.739, respectively. This performance outperformed the same evaluation metrics obtained from the traditional ML models (p < 0.05). The data suggested that retinal vasculature could be a potential biomarker for atherosclerosis in the coronary artery and that the GNN model could be utilized for accurate prediction.

Coronary Atherosclerosis, Cardiac Troponin, and Interleukin-6 in Patients With Chest Pain: The PROMISE Trial Results

BackgroundIncreased inflammation and myocardial injury can be observed in the absence of myocardial infarction or obstructive coronary artery disease (CAD). ObjectivesThe authors determined whether biomarkers of inflammation and myocardial injury—interleukin (IL)-6 and high-sensitivity cardiac troponin (hs-cTn)—were associated with the presence and extent of CAD and were independent predictors of major adverse cardiovascular events (MACEs) in stable chest pain. MethodsUsing participants from the PROMISE trial, the authors measured hs-cTn I and IL-6 concentrations and analyzed computed tomography angiography (CTA) images in the core laboratory for CAD characteristics: significant stenosis (≥70%), high-risk plaque (HRP), Coronary Artery Disease Reporting and Data System (CAD-RADS) categories, segment involvement score (SIS), and coronary artery calcium (CAC) score. The primary endpoint was a composite MACE (death, myocardial infarction, or unstable angina). ResultsThe authors included 1,796 participants (age 60.2 ± 8.0 years; 47.5% men, median follow-up 25 months). In multivariable linear regression adjusted for atherosclerotic cardiovascular disease (ASCVD) risk, hs-cTn was associated with HRP, stenosis, CAD-RADS, and SIS. IL-6 was only associated with stenosis and CAD-RADS. hs-cTn above median (1.5 ng/L) was associated with MACEs in univariable analysis (HR: 2.1 [95% CI: 1.3-3.6]; P = 0.006), but not in multivariable analysis adjusted for ASCVD and CAD. IL-6 above median (1.8 ng/L) was associated with MACEs in multivariable analysis adjusted for ASCVD and HRP (HR: 1.9 [95% CI: 1.1-3.3]; P = 0.03), CAC (HR: 1.9 [95% CI: 1.0-3.4]; P = 0.04), and SIS (HR: 1.8 [95% CI: 1.0-3.2]; P = 0.04), but not for stenosis or CAD-RADS. In participants with nonobstructive CAD (stenosis 1%-69%), the presence of both hs-cTn and IL-6 above median was strongly associated with MACEs (HR: 2.5-2.7 after adjustment for CAD characteristics). ConclusionsConcentrations of hs-cTn and IL-6 were associated with CAD characteristics and MACEs, indicating that myocardial injury and inflammation may each contribute to pathways in CAD pathophysiology. This association was most pronounced among participants with nonobstructive CAD representing an opportunity to tailor treatment in this at-risk group. (PROspective Multicenter Imaging Study for Evaluation of Chest Pain [PROMISE]; NCT01174550)

Impact of machine-learning-based coronary computed tomography angiography-derived fractional flow reserve on decision-making in patients with severe aortic stenosis undergoing transcatheter aortic valve replacement.

To evaluate feasibility and diagnostic performance of coronary CT angiography (CCTA)-derived fractional flow reserve (CT-FFR) for detection of significant coronary artery disease (CAD) and decision-making in patients with severe aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR) to potentially avoid additional pre-TAVR invasive coronary angiography (ICA). Consecutive patients with severe AS (n = 95, 78.6 ± 8.8 years, 53% female) undergoing pre-procedural TAVR-CT followed by ICA with quantitative coronary angiography were retrospectively analyzed. CCTA datasets were evaluated using CAD Reporting and Data System (CAD-RADS) classification. CT-FFR measurements were computed using an on-site machine-learning algorithm. A combined algorithm was developed for decision-making to determine if ICA is needed based on pre-TAVR CCTA: [1] all patients with CAD-RADS ≥ 4 are referred for ICA; [2] patients with CAD-RADS 2 and 3 are evaluated utilizing CT-FFR and sent to ICA if CT-FFR ≤ 0.80; [3] patients with CAD-RADS < 2 or CAD-RADS 2-3 and normal CT-FFR are not referred for ICA. Twelve patients (13%) had significant CAD (≥ 70% stenosis) on ICA and were treated with PCI. Twenty-eight patients (30%) showed CT-FFR ≤ 0.80 and 24 (86%) of those were reported to have a maximum stenosis ≥ 50% during ICA. Using the proposed algorithm, significant CAD could be identified with a sensitivity, specificity, and positive and negative predictive value of 100%, 78%, 40%, and 100%, respectively, potentially decreasing the number of necessary ICAs by 65 (68%). Combination of CT-FFR and CAD-RADS is able to identify significant CAD pre-TAVR and bears potential to significantly reduce the number of needed ICAs. • Coronary CT angiography-derived fractional flow reserve (CT-FFR) using machine learning together with the CAD Reporting and Data System (CAD-RADS) classification safely identifies significant coronary artery disease based on quantitative coronary angiography in patients prior to transcatheter aortic valve replacement. • The combination of CT-FFR and CAD-RADS enables decision-making and bears the potential to significantly reduce the number of needed invasive coronary angiographies.

Results of Treatment of Patients with Concomitant Aortic Lesions and Coronary Heart Disease

The aim. To determine the degree of increased operative risk in the group of patients with a combination of aortic aneurysms and coronary artery disease compared with those with isolated aortic aneurysms.&#x0D; Materials and methods. In the period from January 1, 2010 to October 1, 2021 at the National Amosov Institute of Cardiovascular Surgery, 820 patients with aortic aneurysm, including 172 (20.9%) patients with concomitant aortic and coronary artery disease, underwent surgical treatment. Diagnosis was based on standard examination methods such as electrocardiography, transthoracic echocardiography, coronary angiography, and computed tomography without coronary angiography in cases of aortic dissection. In patients with aortic dissection and coronary artery disease, the extent of the lesion was examined mainly by CT diagnosis and Coronary Artery Disease Reporting and Data System (CAD-RADS) scale due to contraindications to coronary angiography.&#x0D; Results.Thetotalnumberofcomplicationswas26(15.1%)cases.Cerebrovasculardisorderswereobservedin4(2.3%) cases, 3 of which regressed in the postoperative period; in all 4 (2.3%) cases there was a history of acute cerebrovascular disorders. Spinal cord ischemia was observed in 2 (1.2%) cases. Multiple organ failure occurred in the postoperative period in 6 (3.5%) patients, renal failure in 4 (2.3%) patients. Respiratory failure was found in 3 (1.7%) patients. Septic shock occurred in 1 (0.6%) patient. Increased exudation was present in 6 (3.5%) cases requiring rethoracotomy. There were 7 (4.0%) in-hospital deaths, 3 (9.3%) in the acute dissection group and 4 (2.9%) in the aortic aneurysm group without stratification. The distribution of patients by lethal complications was as follows: 1 (14.3%) patient had acute cerebrovascular accident, 1 (14.3%) had septic shock and 1 (14.3%) had acute renal failure. In more than 50% of cases, the cause of death was multiple organ failure (4 [57%] patients).&#x0D; Conclusions. Concomitant lesions of arteries with aortic aneurysms are associated with higher rates of postoperative complications and mortality. In the group of patients with aortic dissection combined with coronary artery disease, there was longer duration of surgery, duration of artificial circulation and aortic compression due to the high initial severity of condition in such patients and greater complexity and volume of surgery. Hospital mortality in the group of aortic aneurysms combined with coronary artery lesions was almost 3 times higher than that in the group of isolated aortic aneurysms (4% and 1.5%, respectively).

Functional CAD-RADS using FFRCT on therapeutic management and prognosis in patients with coronary artery disease.

To propose a novel functional Coronary Artery Disease-Reporting and Data System (CAD-RADS) category system integrated with coronary CT angiography (CCTA)-derived fractional flow reserve (FFRCT) and to validate its effect on therapeutic decision and prognosis in patients with coronary artery disease (CAD). Firstly, we proposed a novel functional CAD-RADS and evaluated the performance of functional CAD-RADS for guiding treatment strategies with actual clinical treatment as a reference standard in a retrospective multicenter cohort with CCTA and invasive FFR performed in all patients (n = 466). Net reclassification improvement (NRI) of functional CAD-RADS over anatomical CAD-RADS was calculated. Secondly, the prognostic value of functional CAD-RADS in a prospective two-arm cohort (566 [FFRCT arm] vs. 567 [CCTA arm]) was calculated, after a 1-year follow-up, functional CAD-RADS in FFRCT arm (n = 513) and anatomical CAD-RADS in CCTA arm (n = 511) to determine patients at risk of adverse outcomes were compared with a Cox hazard proportional model. Functional CAD-RADS demonstrated superior value over anatomical CAD-RADS (AUC: 0.828 vs. 0.681, p < 0.001) and comparable performance to FFR (AUC: 0.828 vs. 0.848, p = 0.253) in guiding therapeutic decisions. Functional CAD-RADS resulted in the revision of management plan as determined by anatomical CAD-RADS in 30.0% of patients (n = 140) (NRI = 0.369, p < 0.001). Functional CAD-RADS was an independent predictor for 1-year outcomes with indexes of concordance of 0.795 and the corresponding value was 0.751 in anatomical CAD-RADS. The novel functional CAD-RADS gained incremental value in guiding therapeutic decision-making compared with anatomical CAD-RADS and comparable power in 1-year prognosis with anatomical CAD-RADS in a real-world scenario. • The novel functional CAD-RADS category system with FFRCT integrated into the anatomical CAD-RADS categories was originally proposed. • The novel functional CAD-RADS category system was validated superior value over anatomical CAD-RADS (AUC: 0.828 vs. 0.681, p < 0.001) in guiding therapeutic decisions and revised management plan in 30.0% of patients as determined by anatomical CAD-RADS (net reclassification improvement index = 0.369, p < 0.001). • Functional CAD-RADS was an independent predictor with an index of concordance of 0.795 and 0.751 in anatomical CAD-RADS for 1-year prognosis of adverse outcomes.

Monocyte-to-High-Density Lipoprotein Ratio may be Related to the Coronary Artery Disease Reporting and Data System Score and Increased Left Ventricular Mass

Background: Activation of monocytes and lower high-density lipoprotein (HDL) levels accelerate atherosclerosis. The monocyte-to-HDL ratio (MHR) and the coronary artery disease-reporting and data system (CAD-RADS) score are recognized as proper indicators of the atherosclerotic process. Objectives: This study aimed to investigate the relationship between the CAD-RADS score and the left ventricular mass (LVM) and MHR and to evaluate the role of MHR as a predictive marker for the CAD-RADS score. Patients and Methods: In this study, the data of 160 patients, who underwent coronary computed tomography angiography (CCTA) for cardiac assessment in our radiology unit between January 2019 and January 2021, were collected and evaluated retrospectively. The CAD-RADS score, LVM, and MHR were calculated for the patients. Results: The multivariate analysis showed independent relationships between CAD-RADS scores and high-sensitive C-reactive protein (hs-CRP) (β = 407, P &lt; 0.001), MHR (β = 351, P &lt; 0.001), age (β = 0.254, P &lt; 0.001), male sex (β = 0.179, P &lt; 0.001), and diabetes mellitus (β = 0.122, P = 0.008). The cutoff values of MHR (9.4 for CAD-RADS-1, 11.5 for CAD-RADS-2, 12.8 for CAD-RADS-3, and 14.0 for CAD-RADS-4) showed high sensitivity and specificity for the CAD-RADS scores. Conclusion: Based on the results, MHR may be associated with the CAD-RADS score and increased LVM. As an inexpensive and reliable test, MHR may be useful for the early diagnosis and follow-up of atherosclerotic heart diseases.

An Egyptian study to assess the accuracy and reliability of CAD-RADS CT coronary angiography algorithm in the evaluation of coronary artery disease

BackgroundMultidetector computed tomography angiography (MDCT) is a non-invasive examination for coronary artery disease. Coronary artery disease reporting and data system (CAD-RADS) is a structured reporting system that successfully facilitated communication with clinicians. Our study aimed to assess the accuracy as well as the agreement of the CAD-RADS system with the conventional angiography results.Results48 patients were enrolled in this prospective study, all patients underwent MDCT coronary angiography and conventional coronary artery angiography. An excellent inter method agreement between coronary CT angiography (CCTA) and conventional coronary angiography was noted for the left main trunk (LMT) with k = 1 (p < 0.001). An excellent inter method agreement was found for the proximal, mid- and distal segments of the left anterior descending artery (LAD) and the second diagonal segment, with k = 1, 0.842 0.886 and 0.886, respectively (p < 0.001). A good agreement was noted at the first diagonal segment with k = 0.765 (p < 0.001). An excellent inter-method agreement was found for the proximal, mid- and distal segments of the left circumflex artery (LCx) and the obtuse marginal branch, with k = 0.838, 0.846, 1 and 0.846, respectively (p < 0.001). An excellent agreement was found for the proximal and mid-segments of the right coronary artery (RCA) and the posterior descending artery, with k = 1 for all segments (p < 0.001), yet a good agreement was noted at its distal segment with k = 0.782 (p < 0.001). The overall per-patient sensitivity, specificity and accuracy of the CT coronary angiography were 92.9%, 90% and 91.7% respectively.ConclusionsThe CAD-RADS algorithm and invasive coronary angiography agreed perfectly; thus, CT coronary angiography can be used as the first screening test and the invasive coronary angiography can be spared for cases requiring intervention.

CT multivessel aggregate stenosis score: A novel point-of-care tool for predicting major adverse cardiac events

BackgroundWe evaluated the utility of a novel 15-point multivessel aggregate stenosis (MVAS) score for predicting major adverse cardiac events (MACE) in low-risk patients with suspected ischaemic symptoms undergoing CTCA. Prognostic performance was compared with the Coronary Artery Disease Reporting and Data System (CAD-RADS) classification and the 16-point Segment Involvement Score (SIS). Methods772 consecutive patients underwent CTCA and coronary artery calcification scoring (CACS) from 2010 to 2015. Coronary artery disease severity was calculated according to CAD-RADS class (0–5 ​± ​vulnerability modifier), the SIS (0–16), and an MVAS score (0–15) based on the aggregate stenosis severity in all 4 coronary vessels (maximum 12 points) plus the presence of any high-risk plaque features (additional 3 points). 52 patients were referred directly for coronary angiography based on CTCA findings and were excluded; the remainder were followed-up for 64.6 ​± ​19.1 months. Results54 ​MACE were observed in 720 patients (7.5%); MACE patients had higher CAD-RADS class (3.92 ​± ​0.7 vs 0.91 ​± ​1.2, p ​< ​0.0001), SIS (4.59 ​± ​2.7 vs 0.79 ​± ​1.2, p ​< ​0.0001), and MVAS scores (10.1 ​± ​1.7 vs 1.7 ​± ​2.1, p ​< ​0.0001). Adjusted Cox proportional hazards analysis identified CAD-RADS class (HR 2.96 (2.2–4), p ​< ​0.0001), SIS (HR 1.29 (1.2–1.4, p ​< ​0.0001), and MVAS score (HR 1.82 (1.6–2.1), p ​< ​0.0001) as predictors of MACE. Adjusted receiver operating characteristic (ROC) analysis found MVAS a more powerful predictor of MACE than CAD-RADS and SIS (AUC: 0.92 vs 0.84 vs 0.83, p ​= ​0.018). ConclusionsCAD-RADS and SIS are reliable predictors of MACE, and the MVAS score provided incremental prognostic data. MVAS may potentiate risk stratification, particularly in institutions without advanced plaque analysis software.

Evaluation of a deep learning model on coronary CT angiography for automatic stenosis detection

PurposeThe purpose of this study was to evaluate a deep-learning model (DLM) for classifying coronary arteries on coronary computed tomography -angiography (CCTA) using the Coronary Artery Disease-Reporting and Data System (CAD-RADS). Materials and methodsThe DLM was trained with 10,800 curved multiplanar reformatted (cMPR) CCTA images classified by an expert radiologist using the CAD-RADS. For each of the three main coronary arteries, nine cMPR images 40° apart acquired around each arterial circumference were then classified by the DLM using the highest probability. For the validation set composed of 159 arteries from 53 consecutive patients, the images were read by two senior and two junior readers; consensus of the two seniors was the reference standard. With the DLM, the majority vote for the nine images was used to classify each artery. Three groups (CAD-RADS 0, 1–2, or 3–4–5) and 2 groups CAD-RADS 0–1–2 or 3–4–5 (<50% vs. ≥50% stenosis) were used for comparisons with readers and consensus. Performance of the model and readers was compared to the consensus reading using the intraclass coefficient (ICC) and Cohen's kappa coefficient at the artery and patient levels. ResultsWith the three groups at the artery level, the ICC of the DLM was 0.82 (95% CI: 0.75–0.88) and not significantly different from those of 3/4 readers; accuracy was 81%. With the binary classification, Cohen kappa coefficient of the DLM was 0.85 (95% CI: 0.69–0.94) and not significantly different from that of 3/4 readers; accuracy was 96%. At the patient level, sensitivity and specificity were 93% and 97% respectively, and the negative predictive value was 97%. ConclusionThe DLM detected ≥50% stenoses with performances similar to those achieved by senior radiologists.

Long-term prognostic value of the serial changes of CT-derived fractional flow reserve and perivascular fat attenuation index.

To investigate the serial changes of computed tomography (CT) fractional flow reserve (CT-FFR) and fat attenuation index (FAI), and explore their relationships with long-term clinical outcomes. Consecutive symptomatic patients with an intermediate pretest probability of coronary artery disease 1-4 were prospectively enrolled if coronary CT angiography (CCTA) revealed at least 1 lesion with 30-70% stenosis on major epicardial arteries. Follow-up CCTA was performed at 1 to 1.5-year intervals. All patients were further followed up after the second CCTA until September 2019. The Coronary Artery Disease - Reporting and Data System (CAD-RADS) grade, high-risk plaque features, lesion-specific CT-FFR, and FAI were measured for prognosis analysis. A total of 263 patients were included in the analysis, and 38 major adverse cardiac events (MACEs) occurred. In the MACE group, the lesion-specific CT-FFR decreased significantly at the follow-up CCTA [0.80 (0.74-0.90) versus 0.85 (0.76-0.93); P=0.01], whereas the FAI did not notably increase (-70.4±8.9 versus -71.3±7.1 HU; P=0.436). In the non-MACE group, lesion-specific CT-FFR increased markedly [0.91 (0.84-0.95) versus 0.90 (0.82-0.94); P<0.001], while the FAI decreased substantially (-74.0±10.8 versus -72.4±11.5 HU; P=0.004). Decreased CT-FFR (adjusted overall hazard ratio =2.455; P=0.023) and increased FAI (adjusted hazard ratio =2.956; P=0.002) were the strongest independent predictors of MACEs. Serial changes of CT-FFR and FAI provided incremental prognostic value (Concordance statistic =0.716; P=0.003; over conventional clinical and imaging parameters (Concordance statistic =0.762; P=0.004). Decreased CT-FFR and increased FAI at follow-up CCTA were the 2 strongest predictors of MACEs. Serial changes of CT-FFR and FAI provided incremental prognostic value over conventional clinical and imaging parameters for risk stratification. In addition, decreased CT-FFR provided incremental predictive value for MACEs from 15 months after second CCTA, while increased FAI added prognostic value from the second CCTA onwards.

Validation of Coronary Artery Disease Reporting and Data System (CAD-RADS) and Application of Coronary Artery Calcium Data and Reporting System (CAC-DRS) as New Standardized Tools in the Management of Coronary Artery Disease Patients.

Background and ObjectivesThe coronary artery disease reporting and data system (CAD-RADS) is intended to standardize the reporting of CCTA and the subsequent management guidelines of CAD. The present study was conducted to investigate the validation of CAD-RADS and the application of coronary calcium grading in CAD management.Patients and MethodsThe current study is a single-center prospective study that involved 177 participants with chest pain who were submitted to coronary CT angiography (CCTA). Two reviewers independently assessed CCTA results and gave each patient a CAD-RADS category. The reference standard for determining the clinical utility of CAD-RADS was invasive coronary angiography (ICA). The inter-reviewer agreement (IRA) was tested using the intra-class correlation (ICC).ResultsThe study enrolled 111 cases with non-significant CAD and 66 cases with significant CAD based on ICA findings. According to the reviewer, the CAD-RADS had a sensitivity, specificity, and accuracy of 90.9 to 100%, 89.2 to 94.6%, and 93.16 to 93.2%, respectively, for predicting severe CAD. The IRA for CAD-RADS categories was excellent (ICC = 0.960). The best cut-off value for predicting severe CAD was CAD-RADS > 3. Significant relation between Ca and severe CAD (p<0.001) was detected.ConclusionThe current study provides a good understanding of CAD-RADS as a standard tool with high diagnostic accuracy.