Predicting coronary artery severity in patients undergoing coronary computed tomographic angiography: Insights from pan-immune inflammation value and atherogenic index of plasma.

BACKGROUND AND AIMS: Coronary computed tomographic angiography (CCTA) is pivotal in diagnosing coronary artery disease (CAD). We explored the link between CAD severity and two biomarkers, Pan-Immune Inflammation Value (PIV) and Atherogenic Index of Plasma (AIP), in stable CAD patients. METHODS AND RESULTS: A retrospective observational study of 409 CCTA patients with stable angina pectoris. Logistic regression identified predictors of severe CAD, stratified by CAD-RADS score. Receiver Operating Characteristic (ROC) curves evaluated predictive performance. PIV and AIP were significant predictors of severe CAD (PIV: OR 1.002, 95% CI: 1.000-1.004, p < 0.021; AIP: OR 0.963, 95% CI: 0.934-0.993, p < 0.04). AUC values for predicting severe CAD were 0.563 (p < 0.001) for PIV and 0.625 (p < 0.05) for AIP. Combined with age, AUC improved to 0.662 (p < 0.02). CONCLUSIONS: PIV and AIP were associated with severe CAD, with AIP demonstrating superior predictive capability. Incorporating AIP into risk assessment could enhance CAD prediction, offering a cost-effective and accessible method for identifying individuals at high risk of coronary atherosclerosis.

Epicardial fat volume and its association with cardiac arrhythmias in CT coronary angiography.

Purpose: This retrospective study aimed to investigate the epicardial fat volume in cardiac computed tomography (CT), its relationship with cardiac arrhythmias, and its correlation with the coronary artery disease reporting and data system (CAD-RADS) score. Material and methods: Ninety-six patients who underwent CT coronary angiography (CTCA) were included in this study. Patient data, including demographic information, clinical history, and imaging data were collected retrospectively. Epicardial fat volume was quantified using a standardised algorithm, the CAD-RADS scoring system was applied to assess the extent of coronary artery disease (CAD). Descriptive statistics, correlation analyses, and receiver operating characteristics methods were used. Results: The study found a significant correlation between epicardial fat volume and CAD-RADS score (r2 = 0.31; p < 0.001), indicating the known influence of epicardial fat on CAD risk. Moreover, patients with higher epicardial fat volumes were more likely to experience cardiac tachyarrhythmia (p < 0.001). Receiver operating characteristic analysis established a threshold value of 123 cm3 for epicardial fat volume to predict tachyarrhythmia with 80% sensitivity (AUC = 0.69). Conclusions: In this study a volume of at least 123 cm3 epicardial fat in native coronary calcium scans is associated with cardiac tachyarrhythmia. In these patients, careful selection of suitable imaging protocols is advised.

Effectiveness of clinical scores in predicting coronary artery disease in familial hypercholesterolemia: a coronary computed tomography angiography study.

PURPOSE: One of the major challenges in the management of familial hypercholesterolemia (FH) is the stratification of cardiovascular risk in asymptomatic subjects. Our purpose is to investigate the performance of clinical scoring systems, Montreal-FH-score (MFHS), SAFEHEART risk (SAFEHEART-RE) and FH risk score (FHRS) equations and Dutch Lipid Clinic Network (DLCN) diagnostic score, in predicting extent and severity of CAD at coronary computed tomography angiography (CCTA) in asymptomatic FH. MATERIAL AND METHODS: One-hundred and thirty-nine asymptomatic FH subjects were prospectively enrolled to perform CCTA. MFHS, FHRS, SAFEHEART-RE and DLCN were assessed for each patient. Atherosclerotic burden scores at CCTA (Agatston score [AS], segment stenosis score [SSS]) and CAD-RADS score were calculated and compared to clinical indices. RESULTS: Non-obstructive CAD was found in 109 patients, while 30 patients had a CAD-RADS ≥ 3. Classifying the two groups according to AS, values varied significantly for MFHS (p < 0.001), FHRS (p < 0.001) and SAFEHEART-RE (p = 0.047), while according to SSS only MFHS and FHRS showed significant differences (p < 0.001). MFHS, FHRS and SAFEHEART-RE, but not DLCN, showed significant differences between the two CAD-RADS groups (p < .001). MFHS proved to have the best discriminatory power (AUC = 0.819; 0.703-0.937, p < 0.001) at ROC analysis, followed by FHRS (AUC = 0.795; 0.715-0.875, p < .0001) and SAFEHEART-RE (AUC = .725; .61-.843, p < .001). CONCLUSIONS: Greater values of MFHS, FHRS and SAFEHEART-RE are associated to higher risk of obstructive CAD and might help to select asymptomatic patients that should be referred to CCTA for secondary prevention.

Relationship of Atherosclerotic Plaque Structure with the History of COVID-19 in Patients Undergoing Coronary Computed Tomographic Angiography.

Although there is no sign of reinfection, individuals who have a history of coronavirus disease 2019 (COVID-19) may experience prolonged chest discomfort and shortness of breath on exertion. This study aimed to examine the relationship between atherosclerotic coronary plaque structure and COVID-19. This retrospective cohort comprised 1269 consecutive patients who had coronary computed tomographic angiography (CCTA) for suspected coronary artery disease (CAD) between July 2020 and April 2021. The type of atherosclerotic plaque was the primary outcome. Secondary outcomes included the severity of coronary stenosis as determined via the Coronary Artery Disease-Reporting and Data System (CAD-RADS) classification and the coronary artery calcium (CAC) score. To reveal the relationship between the history of COVID-19 and the extent and severity of CAD, propensity score analysis and further multivariate logistic regression analysis were performed. The median age of the study population was 52 years, with 53.5% being male. COVID-19 was present in 337 individuals. The median duration from COVID-19 diagnosis to CCTA extraction was 245 days. The presence of atherosclerotic soft plaque (OR: 2.05, 95% confidence interval [CI]: 1.32-3.11, P = 0.001), mixed plaque (OR: 2.48, 95% CI: 1.39-4.43, P = 0.001), and high-risk plaque (OR: 2.75, 95% CI: 1.98-3.84, P < 0.001) was shown to be linked with the history of COVID-19 on the conditional multivariate regression analysis of the propensity-matched population. However, no statistically significant association was found between the history of COVID-19 and the severity of coronary stenosis based on CAD-RADS and CAC score. We found that the history of COVID-19 might be associated with coronary atherosclerosis assessed via CCTA.

Computed tomography independent quantitative determinants of CAD-RADS 4 versus CAD-RADS 3 for calcified coronary lesions.

Purpose: An investigation of coronary computed tomography angiography (CCTA)-derived quantitative parameters to determine CAD-RADS 4 versus CAD-RADS 3 of coronary lesions with moderate to severe calcification. Material and methods: The study included 150 coronary lesions proven to have moderate or severe stenosis by invasive coronary angiography and showing moderate to severe calcification in CCTA. Various CCTA-quantitative parameters were correlated to the degree of stenosis (moderately versus severely stenosed lesions). Their sensitivity and specificity to detect severe stenosis (supposed to be corresponding to CAD-RADS 4) were examined at multiple cut-off points. Results: The calcification remodelling index (CRI) was the only statistically significant independent computed tomo-graphy angiography-derived predictor of severe stenosis versus moderate stenosis on multivariate regression analysis. The best cut-off value was ≤ 0.84, with 77.78% sensitivity and 86.46% specificity. Conclusions: From all quantitative-derived CCTA parameters, CRI ≤ 0.84 was the predictor with the highest diagnostic performance for severe versus moderate stenosis in moderately to severely calcified coronary lesions. Accordingly, CRI can help to determine CAD-RADS 4 versus CAD-RADS 3.

Prognostic value of CAD-RADS classification by coronary CTA in patients with suspected CAD.

BACKGROUND: The study sought to compare Coronary Artery Disease Reporting and Data System (CAD-RADS) classification with traditional coronary artery disease (CAD) classifications and Duke Prognostic CAD Index for predicting the risk of all-cause mortality in patients with suspected CAD. METHODS: 9625 consecutive suspected CAD patients were assessed by coronary CTA for CAD-RADS classification, traditional CAD classifications and Duke Prognostic CAD Index. Kaplan-Meier and multivariable Cox models were used to estimate all-cause mortality. Discriminatory ability of classifications was assessed using time dependent receiver-operating characteristic (ROC) curves and The Hosmer-Lemeshow goodness-of-fit test was employed to evaluate calibration. RESULTS: A total of 540 patients died from all causes with a median follow-up of 4.3 ± 2.1 years. Kaplan-Meier survival curves showed the cumulative events increased significantly associated with CAD-RADS, three traditional CAD classifications and Duke Prognostic CAD Index. In multivariate Cox regressions, the risk for the all-cause death increased from HR 0.861 (95% CI 0.420-1.764) for CAD-RADS 1 to HR 2.761 (95% CI 1.961-3.887) for CAD-RADS 4B&5, using CAD-RADS 0 as the reference group. The relative HRs for all-cause death increased proportionally with the grades of the three traditional CAD classifications and Duke Prognostic CAD Index. The area under the time dependent ROC curve for prediction of all-cause death was 0.7917, 0.7805, 0.7991for CAD-RADS in 1 year, 3 year, 5 year, respectively, which was non-inferior to the traditional CAD classifications and Duke Prognostic CAD Index. CONCLUSIONS: The CAD-RADS classification provided important prognostic information for patients with suspected CAD with noninvasive evaluation, which was non-inferior than Duke Prognostic CAD Index and traditional stenosis-based grading schemes in prognostic value of all-cause mortality. Traditional and simplest CAD classification should be preferable, given the more number of groups and complexity of CAD-RADS and Duke prognostic index, without using more time consuming classification.

Relationship between c-reactive protein to albumin ratio and coronary artery calcium score and CAD-RADS scores with coronary computed tomography angiography.

BACKGROUND: : C-reactive protein (CRP) to albumin ratio (CAR) is predictive marker of systemic inflammatory state in atherosclerotic coronary diseases when compared to predictive value of these two markers separately. We aimed to evaluate the relationship between CAR and the coronary artery calcium (CAC) score, Coronary Artery Disease-Reporting and Data System (CAD-RADS) score in patients' unknown diagnosis of coronary artery disease (CAD) underwent coronary CTA (Computed Tomography Angiography) and were classified by CAD-RADS scores. METHODS: A total of 187 patients consecutively referred for the evaluation of their chest pain underwent coronary CTA were included retrospectively. RESULTS: CRP, CAR, and CAD-RADS scores were higher in patients with CAC score > 400 than the other groups (p < 0.001). We found positive correlation between CAR and CAC score (r= 0.384, p < 0.001), and also there was a positive correlation between CAR and CAD-RADS score (r= 0.462, p < 0.001). Multivariate logistic regression analyses showed that low density lipoprotein cholesterol (LDL-C), CAD-RADS score, and CAR were independent predictors of CAC score (p < 0.05). DISCUSSION: Higher CAR can be a predictive marker of atherosclerosis and CAD. CAR may be useful in the management of patients before invasive coronary angiography. Further studies are needed to clarify the pathophysiologic role of CAR in patients with atherosclerotic coronary heart diease.

Inter-observer agreement of the Coronary Artery Disease Reporting and Data System (CAD-RADSTM) in patients with stable chest pain.

PURPOSE: To assess inter-observer variability of the Coronary Artery Disease - Reporting and Data System (CAD-RADS) for classifying the degree of coronary artery stenosis in patients with stable chest pain. MATERIAL AND METHODS: A prospective study was conducted upon 96 patients with coronary artery disease, who underwent coronary computed tomography angiography (CTA). The images were classified using the CAD-RAD system according to the degree of stenosis, the presence of a modifier: graft (G), stent (S), vulnerable plaque (V), or non-diagnostic (n) and the associated coronary anomalies, and non-coronary cardiac and extra-cardiac findings. Image analysis was performed by two reviewers. Inter-observer agreement was assessed. RESULTS: There was excellent inter-observer agreement for CAD-RADS (k = 0.862), at 88.5%. There was excellent agreement for CAD-RADS 0 (k = 1.0), CAD-RADS 1 (k = 0.92), CAD-RADS 3 (k = 0.808), CAD-RADS 4 (k = 0.826), and CAD-RADS 5 (k = 0.833) and good agreement for CAD-RADS 2 (k = 0.76). There was excellent agreement for modifier G (k = 1.0) and modifier S (k = 1.0), good agreement for modifier N (k = 0.79), and moderate agreement for modifier V (k = 0.59). There was excellent agreement for associated coronary artery anomalies (k = 0.845), non-coronary cardiac findings (k = 0.857), and extra-cardiac findings (k = 0.81). CONCLUSIONS: There is inter-observer agreement of CAD-RADS in categorising the degree of coronary arteries stenosis, and the modifier of the system and associated cardiac and extra-cardiac findings.

Assessment of atherosclerotic plaque burden: comparison of AI-QCT versus SIS, CAC, visual and CAD-RADS stenosis categories.

This study assesses the agreement of Artificial Intelligence-Quantitative Computed Tomography (AI-QCT) with qualitative approaches to atherosclerotic disease burden codified in the multisociety 2022 CAD-RADS 2.0 Expert Consensus. 105 patients who underwent cardiac computed tomography angiography (CCTA) for chest pain were evaluated by a blinded core laboratory through FDA-cleared software (Cleerly, Denver, CO) that performs AI-QCT through artificial intelligence, analyzing factors such as % stenosis, plaque volume, and plaque composition. AI-QCT plaque volume was then staged by recently validated prognostic thresholds, and compared with CAD-RADS 2.0 clinical methods of plaque evaluation (segment involvement score (SIS), coronary artery calcium score (CACS), visual assessment, and CAD-RADS percent (%) stenosis) by expert consensus blinded to the AI-QCT core lab reads. Average age of subjects were 59 ± 11 years; 44% women, with 50% of patients at CAD-RADS 1-2 and 21% at CAD-RADS 3 and above by expert consensus. AI-QCT quantitative plaque burden staging had excellent agreement of 93% (k = 0.87 95% CI: 0.79-0.96) with SIS. There was moderate agreement between AI-QCT quantitative plaque volume and categories of visual assessment (64.4%; k = 0.488 [0.38-0.60]), and CACS (66.3%; k = 0.488 [0.36-0.61]). Agreement between AI-QCT plaque volume stage and CAD-RADS % stenosis category was also moderate. There was discordance at small plaque volumes. With ongoing validation, these results demonstrate a potential for AI-QCT as a rapid, reproducible approach to quantify total plaque burden.

The differences between patients with nonalcoholic fatty liver disease (NAFLD) and those without NAFLD, as well as predictors of functional coronary artery ischemia in patients with NAFLD.

BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) is a chronic liver disease associated with metabolic syndrome. It is the most common cause of cryptogenic cirrhosis. The disease is also involved in the occurrence and development of type 2 diabetes and atherosclerosis and can directly affect the outcome of patients with coronary heart disease. Therefore, the focus of treatment of nonalcoholic fatty liver disease has also begun to focus on the treatment of risk factors for atherosclerotic heart disease. In this study, we investigated the difference between patients with coronary artery stenosis combined with NAFLD and those without NAFLD and evaluated the predictive factors and value of functional coronary artery ischemia in patients with NAFLD. HYPOTHESIS: Many clinical factors (such as age, BMI, hyperglycemia) and imaging parameters (such as CACS grade) in the NAFLD group were different from those in the non-NAFLD group. The predictive model combined with multiple influencing factors has a good value in predicting coronary artery ischemia in patients with NAFLD. METHODS: We collected the clinical and imaging data of patients who underwent coronary computed tomography angiography and coronary artery calcification score (CACS) scans between January and June 2023. A total of 392 patients were included and divided into the NAFLD group and the non-NAFLD group. Based on CT fractional flow reserve (CT-FFR), patients with NAFLD were divided into CT-FFR ≤ 0.08 group and CT-FFR > 0.08 group. RESULTS: Significant differences were observed between the non-NAFLD and NAFLD groups in terms of age, body mass index, hyperglycemia, hyperlipidemia, triglyceride, high-density lipoprotein, coronary artery disease-reporting and data system (CAD-RADS) classification, CACS classification, number of diseased coronary arteries, and CT-FFR ≤ 0.80 ratio (p < .05). The CAD-RADS and CACS classifications can independently predict functional coronary artery ischemia in NAFLD patients. The combined use of CAD-RADS and CACS classifications resulted in an area under the curve of 0.819 (95% confidence interval: 0.761-0.876) for predicting coronary artery ischemia in NAFLD patients, which was higher than the individual classification methods (CAD-RADS: 0.762, CACS: 0.742) (p = .000). CONCLUSIONS: There are differences between patients with coronary artery stenosis and NAFLD and those without NAFLD. The CAD-RADS classification and CACS classification can economically and efficiently predict functional coronary artery ischemia in patients with NAFLD, which has crucial value in clinical diagnosis and treatment.

[Screening, risk stratification and CT imaging for assessment of coronary artery disease]. / Screening, Risikostratifizierung und CT-Bildgebung zur Abklärung der koronaren Herzkrankheit.

BACKGROUND: Early treatment of cardiovascular risk factors and characterization of coronary plaques is essential to collect prognostic information about coronary artery disease (CAD) and prevent cardiovascular events. OBJECTIVES: Discussion of the most important risk factors of CAD, basic diagnostic of CAD, prevention, and prognostic factors of CAD with focus on cardiac computed tomography (CT). MATERIALS AND METHODS: Prevalence and prognostic impact of CAD risk factors; description of specific assessment of risk profiles; estimation of pretest probability; conventional prevention of CAD; prognostic assessment of CAD using the Calcium Scoring and coronary CT angiography. RESULTS: Assessment of risk profiles and estimation of pretest probability for obstructive coronary stenosis necessitates a thorough evaluation of medical history and laboratory values. The composition and extent of calcified and noncalcified plaques in CT exams based on the criteria of the Coronary Artery Disease-Reporting and Data System give important prognostic information about the risk of cardiovascular events, which increases with high plaque burden and vice versa. Initial imaging with CT for evaluation of CAD leads to a reduction of invasive coronary angiographies and catheter-associated complications. CONCLUSIONS: Besides early detection of cardiovascular risk factors, the additional assessment of plaque burden and significant stenosis in CT gives further prognostic information to facilitate effective therapies to prevent cardiovascular events and in the case of low plaque burden avoid invasive coronary angiography. However, systmatic screening using Calcium Scoring is not established yet due to insufficient data, although it could potentially be used for an early risk stratification in patients with multiple risk factors.

CAD-RADS scoring of coronary CT angiography with Multi-Axis Vision Transformer: A clinically-inspired deep learning pipeline.

BACKGROUND AND OBJECTIVE: The standard non-invasive imaging technique used to assess the severity and extent of Coronary Artery Disease (CAD) is Coronary Computed Tomography Angiography (CCTA). However, manual grading of each patient's CCTA according to the CAD-Reporting and Data System (CAD-RADS) scoring is time-consuming and operator-dependent, especially in borderline cases. This work proposes a fully automated, and visually explainable, deep learning pipeline to be used as a decision support system for the CAD screening procedure. The pipeline performs two classification tasks: firstly, identifying patients who require further clinical investigations and secondly, classifying patients into subgroups based on the degree of stenosis, according to commonly used CAD-RADS thresholds. METHODS: The pipeline pre-processes multiplanar projections of the coronary arteries, extracted from the original CCTAs, and classifies them using a fine-tuned Multi-Axis Vision Transformer architecture. With the aim of emulating the current clinical practice, the model is trained to assign a per-patient score by stacking the bi-dimensional longitudinal cross-sections of the three main coronary arteries along channel dimension. Furthermore, it generates visually interpretable maps to assess the reliability of the predictions. RESULTS: When run on a database of 1873 three-channel images of 253 patients collected at the Monzino Cardiology Center in Milan, the pipeline obtained an AUC of 0.87 and 0.93 for the two classification tasks, respectively. CONCLUSION: According to our knowledge, this is the first model trained to assign CAD-RADS scores learning solely from patient scores and not requiring finer imaging annotation steps that are not part of the clinical routine.

Comparison of coronary CT angiography and invasive coronary angiography results.

INTRODUCTION: Coronary artery disease (CAD) is a leading cause of death worldwide. Accurate diagnosis and management are critical. Non-invasive imaging, such as coronary computed tomography angiography (CCTA), is vital for early diagnosis and treatment planning. This study evaluates the accuracy of CAD-Reporting and Data System (CAD-RADS) scoring and the compatibility between CCTA and invasive coronary angiography (ICA) in patients suspected of having CAD. MATERIALS AND METHODS: From January 1, 2022 to January 15, 2024, 214 patients suspected of CAD underwent both CCTA and ICA. CCTA artifacts led to the exclusion of 32 patients and 128 vessels, leaving 586 vessels for analysis. CAD-RADS scoring categorized coronary stenosis. Diagnostic performance was measured by specificity, sensitivity, accuracy, positive and negative predictive value (NPV). Extracardiac findings were analyzed with a wide field of view (FOV) during CCTA. RESULTS: A total of 214 patients (67.3% male, median age 56) were examined. Hypertension, smoking, calcium score, and high-risk plaques correlated with CCTA and ICA CAD-RADS scores; calcium score also related to hypertension, smoking, diabetes, and dyslipidemia (p < 0.05). CCTA showed a sensitivity of 80.8% and NPV of 90.3% for detecting stenosis of 70% or more; for 50% stenosis, sensitivity was 93.5% and NPV 92.1%. Agreement between CCTA and ICA was excellent in bypass patients; stenosis detection in stented patients had 85.7% sensitivity and 96.2% NPV. CONCLUSION: This study highlights the importance of CAD-RADS and CCTA in CAD diagnosis and treatment planning. CCTA effectively evaluates stents and grafts, emphasizing the benefits of extracardiac findings and a wide FOV.

Risk factors for high CAD-RADS scoring in CAD patients revealed by machine learning methods: a retrospective study.

Objective: This study aimed to investigate a variety of machine learning (ML) methods to predict the association between cardiovascular risk factors and coronary artery disease-reporting and data system (CAD-RADS) scores. Methods: This is a retrospective cohort study. Demographical, cardiovascular risk factors and coronary CT angiography (CCTA) characteristics of the patients were obtained. Coronary artery disease (CAD) was evaluated using CAD-RADS score. The stenosis severity component of the CAD-RADS was stratified into two groups: CAD-RADS score 0-2 group and CAD-RADS score 3-5 group. CAD-RADS scores were predicted with random forest (RF), k-nearest neighbors (KNN), support vector machines (SVM), neural network (NN), decision tree classification (DTC) and linear discriminant analysis (LDA). Prediction sensitivity, specificity, accuracy and area under the curve (AUC) were calculated. Feature importance analysis was utilized to find the most important predictors. Results: A total of 442 CAD patients with CCTA examinations were included in this study. 234 (52.9%) subjects were CAD-RADS score 0-2 group and 208 (47.1%) were CAD-RADS score 3-5 group. CAD-RADS score 3-5 group had a high prevalence of hypertension (66.8%), hyperlipidemia (50%) and diabetes mellitus (DM) (35.1%). Age, systolic blood pressure (SBP), mean arterial pressure, pulse pressure, pulse pressure index, plasma fibrinogen, uric acid and blood urea nitrogen were significantly higher (p < 0.001), and high-density lipoprotein (HDL-C) lower (p < 0.001) in CAD-RADS score 3-5 group compared to the CAD-RADS score 0-2 group. Nineteen features were chosen to train the models. RF (AUC = 0.832) and LDA (AUC = 0.81) outperformed SVM (AUC = 0.772), NN (AUC = 0.773), DTC (AUC = 0.682), KNN (AUC = 0.707). Feature importance analysis indicated that plasma fibrinogen, age and DM contributed most to CAD-RADS scores. Conclusion: ML algorithms are capable of predicting the correlation between cardiovascular risk factors and CAD-RADS scores with high accuracy.

The value of Coronary Artery Disease - Reporting and Data System (CAD-RADS) in Outcome Prediction of CAD Patients; a Systematic Review and Meta-analysis.

Introduction: Coronary computed tomographic angiography (CCTA) reporting has traditionally been operator-dependent, and no precise classification is broadly used for reporting Coronary Artery Disease (CAD) severity. The Coronary Artery Disease Reporting and Data Systems (CAD-RADS) was introduced to address the inconsistent CCTA reports. This systematic review with meta-analysis aimed to comprehensively appraise all available studies and draw conclusions on the prognostic value of the CAD-RADS classification system in CAD patients. Method: Online databases of PubMed, Embase, Scopus, and Web of Science were searched until September 19th, 2022, for studies on the value of CAD-RADS categorization for outcome prediction of CAD patients. Results: 16 articles were included in this systematic review, 14 of which had assessed the value of CAD-RADS in the prediction of major adverse cardiovascular events (MACE) and 3 articles investigated the outcome of all-cause mortality. Our analysis demonstrated that all original CAD-RADS categories can be a predictor of MACE [Hazard ratios (HR) ranged from 3.39 to 8.63] and all categories, except CAD-RADS 1, can be a predictor of all-cause mortality (HRs ranged from 1.50 to 3.09). Moreover, higher CAD-RADS categories were associated with an increased hazard ratio for unfavorable outcomes among CAD patients (p for MACE = 0.007 and p for all-cause mortality = 0.018). Conclusion: The evidence demonstrated that the CAD-RADS classification system can be used to predict incidence of MACE and all-cause mortality. This indicates that the implementation of CAD-RADS into clinical practice, besides enhancing the communication between physicians and improving patient care, can also guide physicians in risk assessment of the patients and predicting their prognosis.

Coronary artery disease reporting and data system (CAD-RADS), vascular inflammation and plaque vulnerability.

BACKGROUND: Coronary artery disease reporting and data system (CAD-RADS) predicts future cardiovascular events in patients with coronary artery disease (CAD). However, information on vascular inflammation and vulnerability remains scarce. METHODS: Patients who underwent coronary computed tomography angiography (CTA) and optical coherence tomography (OCT) prior to coronary intervention were enrolled. All three coronary arteries were evaluated for CAD-RADS score and pericoronary adipose tissue (PCAT) attenuation, while the culprit vessel was analyzed for plaque vulnerability by OCT. RESULTS: A total of 385 patients with 915 lesions were divided into two groups based on CAD-RADS score: 103 (26.8%) were categorized as CAD-RADS 4b/5 and 282 (73.2%) as CAD-RADS ≤4a. Patients with CAD-RADS 4b/5 had a higher level of PCAT attenuation (mean of 3 coronary arteries) than those with CAD-RADS ≤4a (-68.4 â€‹± â€‹6.7 HU vs. -70.1 â€‹± â€‹6.5, P â€‹= â€‹0.022). The prevalence of macrophage was higher, and lipid index was greater in patients with CAD-RADS 4b/5 than CAD-RADS ≤4a (94.2% vs. 83.0%, P â€‹= â€‹0.004, 1845 vs. 1477; P â€‹= â€‹0.003). These associations were significant in the culprit vessels of patients with chronic coronary syndrome but not in those with acute coronary syndromes. CONCLUSIONS: Higher CAD-RADS score was associated with higher levels of vascular inflammation and plaque vulnerability.

Comparison of prognostic value between CAD-RADS 1.0 and CAD-RADS 2.0 evaluated by convolutional neural networks based CCTA.

Objectives: The aim of the present study was to investigate the prognostic value of the novel coronary artery disease reporting and data system (CAD-RADS) 2.0 compared with CAD-RADS 1.0 in patients with suspectedcoronary artery disease (CAD) evaluated by convolutional neural networks (CNN) based coronary computed tomography angiography (CCTA). Methods: A total of 1796 consecutive inpatients with suspected CAD were evaluated by CCTA for CAD-RADS 1.0 and CAD-RADS 2.0 classifications. Kaplan-Meier and multivariate Cox models were used to estimate major adverse cardiovascular events (MACE) inclusive of all-cause mortality or myocardial infarction (MI). The C-statistic was used to assess the discriminatory ability of the two classifications. Results: In total, 94 (5.2%) MACE occurred over the median follow-up of 45.25 months (interquartile range 43.53-46.63 months). The annualized MACE rate was 0.014 (95% CI: 0.011-0.017). Kaplan-Meier survival curves indicated that the CAD-RADS classification, segment involvement score (SIS) grade, and Computed Tomography Fractional Flow Reserve (CT-FFR) classification were all significantly associated with the increase in the cumulative MACE (all P < 0.001). CAD-RADS classification, SIS grade, and CT-FFR classification were significantly associated with endpoint in univariate and multivariate Cox analysis. CAD-RADS 2.0 showed a further incremental increase in the prognostic value in predicting MACE (c-statistic 0.702, 95% CI: 0.641-0.763, P = 0.047), compared with CAD-RADS 1.0. Conclusions: The novel CAD-RADS 2.0 evaluated by CNN-based CCTA showed higher prognostic value of MACE than CAD-RADS 1.0 in patients with suspected CAD.

Pericoronary adipose tissue attenuation is associated with non-calcified plaque burden in patients with chronic coronary syndromes.

BACKGROUND: Pericoronary adipose tissue attenuation (PCAT) is a marker of inflammation of the pericoronary fat tissue, which can be assessed by coronary computed tomography angiography (CCTA). Its prognostic value was reported in previous studies. Nevertheless, the relationship between PCAT, plaque burden and coronary artery disease (CAD) severity, are not well defined. AIM: We sought to evaluate the relationship between PCAT, CAD severity based on the CAD-RADS 2.0 score and plaque burden in patients with chronic coronary syndrome (CCS). METHODS: Consecutive patients with a clinical indication for CCTA due to suspected or known CCS were included in our study. PCAT was measured in the proximal 4 â€‹cm of each of the right coronary artery (RCA), left anterior descending artery (LAD), and the left circumflex artery (LCX). The CAD-RADS 2.0 score was assessed in all patients and total, calcified, and non-calcified plaque burden was quantitatively measured. RESULTS: 868 patients (median age of 67.0 (IQR â€‹= â€‹58.0-75.0)yrs., 400 (46.1%) female) underwent CCTA between September 2020 and August 2022 due to CCS. Weak correlations were found between PCAT and the total plaque burden, as well as with the Agatston score, whereas no correlations were found between PCAT and CAD-RADS 2.0 score. Associations were also observed between the PCAT of the LAD, RCA and LCX with non-calcified plaque burden (Odds ratios of 1.22 (95%CI â€‹= â€‹1.15-1.29), 1.11 (95%CI â€‹= â€‹1.07-1.17) and 1.14 (95%CI â€‹= â€‹1.08-1.14), respectively, p â€‹< â€‹0.001 for all) which were independent of age, the Agatston score, and the CAD-RADS 2.0 score). In addition, higher PCAT were noticed with increasing number of plaques, exhibiting high-risk features per patient (p â€‹< â€‹0.05 by ANOVA for all). CONCLUSION: PCAT exhibits significant associations with non-calcified plaque burden and plaques with high-risk features in patients undergoing CCTA for CCS. Thus, PCAT may identify high-risk patients who could benefit from more aggressive preventive therapy, which merits further investigation in future studies.

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

BACKGROUND AND OBJECTIVE: In 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. METHODS: In 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. RESULTS: Experimental 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. CONCLUSIONS: Our algorithm might provide clinicians with decision support, potentially reducing the interobserver variability for coronary artery stenosis evaluation.

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.