Coronary Artery Disease Reporting Research Articles

Automated classification of coronary LEsions fRom coronary computed Tomography angiography scans with an updated deep learning model: ALERT study.

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. This single-center retrospective study included 50 patients that underwent CCTA to rule out obstructive coronary artery disease between 2017-2022. Two expert CCTA readers and CorEx-2.0 independently assessed all 150 vessels using Coronary Artery Disease-Reporting and Data System (CAD-RADS). Inter-reader agreement analysis and diagnostic performance of CorEx-2.0, compared with each expert reader as references, were evaluated using percent agreement, Cohen's kappa for the binary CAD-RADS classification (CAD-RADS 0-3 versus 4-5) at patient level, and linearly weighted kappa for the 6-group CAD-RADS classification at vessel level. Overall, 50 patients and 150 vessels were evaluated. Inter-reader agreement using the binary classification at patient level was 91.8% (45/49) with a Cohen's kappa of 0.80. For the 6-group classification at vessel level, inter-reader agreement was 67.6% (100/148) with a linearly weighted kappa of 0.77. CorEx-2.0 showed 100% sensitivity for detecting CAD-RADS ≥ 4 and kappa values of 0.86 versus both readers using the binary classification at patient level. For the 6-group classification at vessel level, CorEx-2.0 demonstrated weighted kappa values of 0.71 versus reader 1 and 0.73 versus reader 2. CorEx-2.0 identified all patients with severe stenosis (CAD-RADS ≥ 4) compared with expert readers and approached expert reader performance at vessel level (weighted kappa > 0.70). Question Can deep learning models improve objectivity in coronary stenosis grading and reporting as coronary CT angiography (CTA) workloads rise? Findings The deep learning model (CorEx-2.0) identified all patients with severe stenoses when compared with expert readers and approached expert reader performance at vessel level. Clinical relevance CorEx-2.0 is a reliable tool for identifying patients with severe stenoses (≥ 70%), underscoring the potential of using this deep learning model to prioritize coronary CTA reading by flagging patients at risk of severe obstructive coronary artery disease.

Calcific versus non-calcific plaque: a CAD-RADS and FFRCT study

Coronary Artery Disease-Reporting and Data System (CAD-RADS) standardises Computed Tomography Coronary Angiography (CTCA) reporting. Coronary calcification can overestimate stenosis. We hypothesized where CADRADS category is assigned due to predominantly calcified maximal stenosis (Ca+), the CTCA-derived Fractional Flow Reserve (FFRCT) would be lower compared to predominantly non-calcified maximal stenoses (Ca-) of the same CAD-RADS category. Consecutive patients undergoing routine clinical CTCA (September 2018 to May 2020) with ≥1 stenosis ≥25% with FFRCT correlation were included. CTCA’s were subdivided into Ca+ and Ca-. FFRCT was measured in the left anterior descending (LAD), left circumflex (LCx) and right coronary artery (RCA). Potentially flow-limiting classified as FFRCT≤0.8. A subset had Invasive Coronary Angiography (ICA). 561 patients screened, 320 included (60% men, 69±10 years). Ca+ in 51%, 69% and 50% of CAD-RADS 2, 3 and 4 respectively. There was no difference in the prevalence of FFRCT≤0.8 between Ca+ and Ca- stenoses for each CAD-RADS categories. No difference was demonstrated in the median maximal stenoses FFRCT or end-vessel FFRCT within CAD-RADS 2 and 4. CAD-RADS 3 Ca+ had a lower FFRCT (maximal stenosis p= .02, end-vessel p= .005) vs Ca-. No difference in the prevalence of obstructive disease at ICA between predominantly Ca+ and Ca- for any CAD-RADS category. There was no difference in median FFRCT values or rate of obstructive disease at ICA between Ca+ and Castenosis in both CAD-RADS 2 and 4. Ca+ CAD-RADS 3 was suggestive of an underestimation based on FFRCT but not corroborated at ICA.

Abstract 4119869: Automatic assignment of CAD-RADS categories in coronary CTA reports using large language model

Background: The effectiveness of different large language models (LLM) in extracting important information from CT angiography (CTA) radiology reports is unknown. Purpose: The Coronary Artery Disease-Reporting and Data System (CAD-RADS) is a standardized reporting system for CTA that categorizes the severity of coronary artery disease to aid in clinical decision-making. To evaluate the agreement between human readers and LLMs in assigning CAD-RADS categories based on CTA reports. Materials and Methods: This retrospective study analyzed reports from patients who underwent coronary CTA for screening or diagnosis at Johns Hopkins Hospital. Reports with CAD-RADS 0–5 findings were collected from Jan. 2020 to Oct. 2023. Initially, 6,212 CTA reports were retrieved. After excluding non-organized reports and retaining only those with CAD-RADS scores, 590 reports remained. Board-certified radiologists and three large language models (GPT-3.5, GPT-4, and Llama3) assigned CAD-RADS categories based on the original radiologists' findings. Two radiologists reviewed the CAD scores, with only one discrepancy in the 590 reports, indicating high inter-agreement. Agreement between human readers and LLMs for CAD-RADS categories was assessed using the Gwet agreement coefficient (AC1 value). Frequencies of changes in CAD-RADS category assignments that could potentially affect clinical management (CAD-RADS 0-2 vs. 3-5) were calculated and compared. Typically, CAD-RADS 0-2 indicates mild disease leading to conservative management, while CAD-RADS 3-5 denotes further testing or disease requiring aggressive intervention to prevent adverse cardiovascular events. Results: Among 590 reports, agreement between original and reviewing radiologists was almost perfect (AC1 = 0.985). Good agreement was also found between original radiologists and Llama3, GPT-3.5, and GPT-4 (AC1 = 0.861, 0.907, 0.941, respectively). Differences in CAD-RADS category upgrades/downgrades potentially affecting clinical management were observed: 0 of 590 (0%) for human readers, 56 of 590 (9.4%) for Llama3, 33 of 590 (5.5%) for GPT-3.5, and 23 of 590 (3.9%) for GPT-4 upgrading CAD-RADs; 8 of 590 (1.4%) for human readers, 19 of 590 (3.2%) for Llama3, 22 of 590 (3.7%) for GPT-3.5, and 12 of 590 (2.0%) for GPT-4 downgrading RAD-CADs. Conclusion: LLMs showed good agreement with human readers in assigning CAD-RADS categories on CTA reports. However, they were more likely to upgrade CAD-RADS category when compared to radiologists.

Plaque characterization and individualized risk assessment

Risk assessment and accurate plaque characterization are key to individual prognosis in coronary artery disease (CAD). The standard of care is cardiac computed tomography (CT), including calcium scoring and coronary CT angiography (CCTA). Diagnosis is based on the CAD-RADS (Coronary Artery Disease-Reporting and Data System) classification. New developments include CT-based fractional flow reserve (CT-FFR) and plaque quantification ("virtual histology"). Acalcium score of0 indicates an event risk of less than 1% over 10years [7, 17]. CAD-RADS classes1 to5 allow risk assessment compared to patients without coronary plaques [2]. CT-FFR has high accuracy (area under the curve [AUC] 0.90; 95% confidence interval 0.87-0.94) in assessing the hemodynamic significance of stenoses compared with invasive coronary angiography [25]. Plaque quantification has shown that anecrotic core greater than 4% is associated with an almost fivefold increase in 5‑year event risk [29]. The presence of obstructive CAD (stenosis > 50%) is astrong prognostic factor. The evaluation of the hemodynamic relevance of 40-90% stenoses by CT-FFR or other functional tests is already guideline-compliant in the USA, but not yet in Germany. Quantitative approaches to measure plaque volume and composition are gaining importance in research and are expected to become relevant in clinical practice. The CAD-RADS 2.0 classification, which also provides therapy recommendations, should be used to assess the extent of CAD.

Correlation of Sarcopenia with Coronary Artery Disease Severity and Pericoronary Adipose Tissue Attenuation: A Coronary CT Study.

To investigate the association between sarcopenia, as appraised with CT-derived muscle metrics, and cardiovascular status, as assessed via coronary CT angiography (CCTA) using the Coronary Artery Disease-Reporting and Data System (CAD-RADS) and with pericoronary adipose tissue (pCAT) metrics. A retrospective observational study conducted on patients who underwent CCTA. The cross-sectional area (CSA) and attenuation values of the paravertebral muscles at the T8 level and the pectoralis major muscles at the T6 level were measured. The patient height was employed for the normalization of the skeletal muscle CSA. The pCAT attenuation around the coronary arteries was assessed, and the CAD severity was graded using the CAD-RADS reporting system. Regression analyses were performed to assess the impact of demographics, clinical factors, and CT variables on the CAD-RADS and pCAT. A total of 220 patients were included (132 males, median age 65 years). Regression analyses showed the associations of CAD with age and sex (p < 0.001). Familiarity with CAD was related to the left anterior descending artery pCAT (p = 0.002) and circumflex artery pCAT (p = 0.018), whereas age was related to the left anterior descending artery pCAT (p = 0.032). Weak positive correlations were found between the lower muscle density and lower pCAT attenuation (ρ = 0.144-0.240, p < 0.039). This study demonstrated weak associations between the sarcopenia indicators and the cardiovascular risk, as assessed by the CAD severity and pCAT inflammation. However, these correlations were not strong predictors of CAD severity, as age and traditional cardiovascular risk factors overshadowed the impact of sarcopenia in the cardiovascular risk assessment.

Combined strategy of upfront CTCA and optimal treatment for stable chest pain: rationale and design of the CLEAR-CAD trial.

Patients with stable chest pain suspected of coronary artery disease (CAD) usually undergo multiple diagnostic tests to confirm or rule out obstructive CAD. Some tests may not effectively assess the presence of CAD, precluding optimal treatment. Adiagnostic strategy of upfront computed tomography coronary angiography (CTCA) combined with optimal medical therapy (OMT) tailored to the extent of CAD may be superior to standard care in preventing major adverse cardiac events. The CLEAR-CAD trial is aprospective, open-label, multicentre, randomised, superiority trial of an upfront CTCA-guided strategy in 6444 patients presenting in an outpatient setting with suspected CAD compared with standard care, in approximately 30participating centres in the Netherlands. The upfront CTCA-guided strategy consists of an initial CTCA which is assessed using the Coronary Artery Disease-Reporting and Data System (CAD-RADS2.0). In patients without CAD (CAD-RADS0) no specific cardiac medication is mandated. Patients with non-obstructive CAD (CAD-RADS 1-2) are treated with preventive OMT. Patients with obstructive CAD (CAD-RADS ≥ 3) are treated with preventive and anti-anginal OMT; in the presence of pharmacologically refractory symptoms patients undergo selective revascularisation after non-invasive functional imaging for myocardial ischaemia (≥ 10%). Patients with significant left main or proximal left anterior descending coronary artery stenosis on CTCA undergo direct invasive coronary angiography and subsequent revascularisation. The primary endpoint is the composite of all-cause death and myocardial infarction. The CLEAR-CAD trial is the first randomised study to investigate the efficacy of acombined upfront CTCA-guided medical and selective revascularisation strategy in an outpatient setting with suspected CAD compared with standard care.

Moving towards auniform diagnosis of coronary artery disease on coronary CTA : Coronary Artery Disease-Reporting and Data System2.0.

The Coronary Artery Disease-Reporting and Data System (CAD-RADS) is astandardised reporting method which was created in order to improve communication with referring physicians as well as for management considerations. The CAD-RADS score denotes the absence or presence of stenosis, while plaque burden and potential modifiers provide insight into plaque extent and characteristics. The modifier ischaemia enables the incorporation of fractional flow reserve CT and CT perfusion, while the modifier exception is used to denote potential coronary abnormalities. Higher CAD-RADS categories demonstrate incremental prognostic value, with further improvement when taking plaque burden into account. CAD-RADS improves communication with the referring clinician as well as guiding therapeutic management and as such is relevant to uniform patient care in the Netherlands.

Clinical Validation of a Deep Learning Algorithm for Automated Coronary Artery Disease Detection and Classification Using a Heterogeneous Multivendor Coronary Computed Tomography Angiography Data Set.

We sought to clinically validate a fully automated deep learning (DL) algorithm for coronary artery disease (CAD) detection and classification in a heterogeneous multivendor cardiac computed tomography angiography data set. In this single-centre retrospective study, we included patients who underwent cardiac computed tomography angiography scans between 2010 and 2020 with scanners from 4 vendors (Siemens Healthineers, Philips, General Electrics, and Canon). Coronary Artery Disease-Reporting and Data System (CAD-RADS) classification was performed by a DL algorithm and by an expert reader (reader 1, R1), the gold standard. Variability analysis was performed with a second reader (reader 2, R2) and the radiologic reports on a subset of cases. Statistical analysis was performed stratifying patients according to the presence of CAD (CAD-RADS >0) and obstructive CAD (CAD-RADS ≥3). Two hundred ninety-six patients (average age: 53.66 ± 13.65, 169 males) were enrolled. For the detection of CAD only, the DL algorithm showed sensitivity, specificity, accuracy, and area under the curve of 95.3%, 79.7%, 87.5%, and 87.5%, respectively. For the detection of obstructive CAD, the DL algorithm showed sensitivity, specificity, accuracy, and area under the curve of 89.4%, 92.8%, 92.2%, and 91.1%, respectively. The variability analysis for the detection of obstructive CAD showed an accuracy of 92.5% comparing the DL algorithm with R1, and 96.2% comparing R1 with R2 and radiology reports. The time of analysis was lower using the DL algorithm compared with R1 ( P < 0.001). The DL algorithm demonstrated robust performance and excellent agreement with the expert readers' analysis for the evaluation of CAD, which also corresponded with significantly reduced image analysis time.

The Plaque Analysis Classifies the Coronary Artery Disease-Reporting and Data System (CAD-RADS) Stenosis and Plaque Burden Categories: Association of the Plaque Features, Fat Attenuation Index, Coronary Computed Tomography Fractional Flow Reserve, and the Combination of Stenosis and Calcification.

The coronary artery disease-reporting and data system (CAD-RADS) 2.0 is used to standardize the reporting of coronary computed tomography angiography (CCTA) results. Artificial intelligence software can quantify the plaque composition, fat attenuation index, and fractional flow reserve. To analyze plaque features of varying severity in patients with a combination of CAD-RADS stenosis and plaque burden categorization and establish a random forest classification model. The data of 100 patients treated between April 2021 and February 2022 were retrospectively collected. The most severe plaque observed in each patient was the target lesion. Patients were categorized into three groups according to CAD-RADS: CAD-RADS 1-2 + P0-2, CAD-RADS 3-4B + P0-2, and CAD-RADS 3-4B + P3-4. Differences and correlations between variables were assessed between groups. AUC, accuracy, precision, recall, and F1 score were used to evaluate the diagnostic performance. A total of 100 patients and 178 arteries were included. The differences of computed tomography fractional flow reserve (CT-FFR) (H = 23.921, p < 0.001),the volume of lipid component (H = 12.996, p = 0.002),the volume of fibro-lipid component (H = 8.692, p = 0.013),the proportion of lipid component volume (H = 22.038, p < 0.001),the proportion of fibro-lipid component volume (H = 11.731, p = 0.003),the proportion of calcification component volume (H = 11.049, p = 0.004), and plaque type (χ2 = 18.110, p = 0.001) was statistically significant. CT-FFR, volume and proportion of lipid and fibro-lipid components of plaques, the proportion of calcified components, and plaque type were valuable for CAD-RADS stenosis + plaque burden classification, especially CT-FFR, volume, and proportion of lipid and fibro-lipid components. The model built using the random forest was better than the clinical model (AUC: 0.874 vs. 0.647).

Prognostic significance of CAD-RADS for patients with suspected coronary artery disease: A systematic review and meta-analysis

Abstract Background Coronary Artery Disease-Reporting and Data System (CAD-RADS) is a standardized reporting system that offers a structured method for interpreting and reporting results obtained through coronary computed tomography angiography. It has been deemed useful in the prognostication of patients with suspected coronary artery disease (CAD). Purpose The present meta-analysis sought to assess the prognostic value of CAD-RADS in individuals with suspected CAD. Materials and Methods We conducted a systematic search of the electronic databases of PubMed, Web of Science Core Collection, Cochrane advanced search, and EMBASE. A random-effects model was implemented to evaluate the pooled hazard ratio (HR) for each CAD-RADS category and area under the receiver operating characteristics curve for predicting major adverse cardiovascular events. Results Data from 37 596 coronary computed tomography angiography examinations from 13 studies were analyzed. During follow-up, 2,536 (6.7%) adverse events were observed. Pooled HRs for prediction of adverse events were significant for all CAD-RADS categories, with incremental increase in HRs with progressively higher categories. For prediction of all-cause mortality, all categories except CAD-RADS 1 showed significant HR compared with CAD-RADS 0. Combination of CAD-RADS to conventional clinical risk factors and CAC resulted in a high predictive capacity for adverse events (pooled area under the receiver operating characteristics curve: 0.82 ([95% confidence interval, 0.73-0.91]). Conclusion The CAD-RADS category imparts information on the CAD severity and shows incremental increase in HR for adverse events with progressively higher categories, especially beyond CAD-RADS4b.

Screening, risk stratification and CT imaging for assessment of coronary artery disease

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. 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). 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. Assessment of risk profiles and estimation of pretest probability for obstructive coronary stenosis necessitates athorough 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 areduction of invasive coronary angiographies and catheter-associated complications. 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.

Impact of atherosclerosis imaging-quantitative computed tomography on diagnostic certainty, downstream testing, coronary revascularization, and medical therapy: the CERTAIN study.

The incremental impact of atherosclerosis imaging-quantitative computed tomography (AI-QCT) on diagnostic certainty and downstream patient management is not yet known. The aim of this study was to compare the clinical utility of the routine implementation of AI-QCT versus conventional visual coronary CT angiography (CCTA) interpretation. In this multi-centre cross-over study in 5 expert CCTA sites, 750 consecutive adult patients referred for CCTA were prospectively recruited. Blinded to the AI-QCT analysis, site physicians established patient diagnoses and plans for downstream non-invasive testing, coronary intervention, and medication management based on the conventional site assessment. Next, physicians were asked to repeat their assessments based upon AI-QCT results. The included patients had an age of 63.8 ± 12.2 years; 433 (57.7%) were male. Compared with the conventional site CCTA evaluation, AI-QCT analysis improved physician's confidence two- to five-fold at every step of the care pathway and was associated with change in diagnosis or management in the majority of patients (428; 57.1%; P < 0.001), including for measures such as Coronary Artery Disease-Reporting and Data System (CAD-RADS) (295; 39.3%; P < 0.001) and plaque burden (197; 26.3%; P < 0.001). After AI-QCT including ischaemia assessment, the need for downstream non-invasive and invasive testing was reduced by 37.1% (P < 0.001), compared with the conventional site CCTA evaluation. Incremental to the site CCTA evaluation alone, AI-QCT resulted in statin initiation/increase an aspirin initiation in an additional 28.1% (P < 0.001) and 23.0% (P < 0.001) of patients, respectively. The use of AI-QCT improves diagnostic certainty and may result in reduced downstream need for non-invasive testing and increased rates of preventive medical therapy.

The triglyceride-glucose index might be a better indicator for predicting poor cardiovascular outcomes in chronic coronary syndrome

Abstract Background Abnormal glucose, and lipid metabolism all play major roles in the process of atherogenesis and are thus substantial risk factors for the development of coronary artery disease. Purpose We sought to investigate whether the triglyceride-glucose index (TyG) and the Atherogenic Index of Plasma (AIP), surrogates for atherosclerosis, are linked to major adverse cardiovascular events (MACE) in patients with Chronic Coronary Syndrome (CCS). Methods and Results Seven hundred ten-five consecutive patients with intermediate risk for CCS who presented to the outpatient clinic between June 2020 and August 2022 were included retrospectively. MACE included non-fatal myocardial infarction, heart failure, cerebrovascular events, non-cardiac mortality, and cardiac mortality. The composite of MACE was determined to be the primary outcome at follow-up after discharge. Secondary outcomes were defined as each individual parameter of MACE. For the primary outcome's time-to-event analysis, the Kaplan-Meier plots, and Cox proportional hazard models were done.The overall population’s median age was 55 years, with males accounting for 58%. The median follow-up time was 17 months. On multivariate cox regression analysis, age, hypertension, Coronary Artery Disease-Reporting and Data System score, and TyG index were found to be independent predictors of the primary outcome. The primary outcome rate was higher in individuals with high TyG than in those with low TyG (18.7% vs 3.8%, p&amp;lt;0.001). Likewise, patients with high TyG had statistically higher rates of cerebrovascular events, heart failure, non-fatal MI, non-cardiac mortality, and cardiac mortality than the latter. Conclusion We found that the TyG might predict poor cardiovascular outcomes in CCS patients.Table 1Figure 1

The Triglyceride-Glucose Index Might Be a Better Indicator for Predicting Poor Cardiovascular Outcomes in Chronic Coronary Syndrome.

This study aimed to explore the potential association between the triglyceride-glucose index (TyG) and the atherogenic index of plasma (AIP)-both considered surrogate markers for atherosclerosis-and major adverse cardiovascular events (MACEs) in patients diagnosed with chronic coronary syndrome (CCS). We conducted a retrospective analysis, encompassing 715 consecutive patients with intermediate CCS risk, who presented at the outpatient clinic between June 2020 and August 2022. MACEs included non-fatal myocardial infarction, hospitalization for heart failure, cerebrovascular events, non-cardiac mortality, and cardiac mortality. The primary outcome was the composite occurrence of MACEs during the follow-up period. For time-to-event analysis of the primary outcome, we employed Kaplan-Meier plots and Cox proportional hazard models. The median age of the overall study population was 55 years, with a median follow-up duration of 17 months. Multivariate Cox regression analysis identified age, hypertension, Coronary Artery Disease-Reporting and Data System score, and TyG index as independent predictors of the primary outcome. Notably, individuals with high TyG levels exhibited a significantly higher primary outcome rate compared to those with low TyG levels (18.7% vs. 3.8%, p < 0.001). Similarly, patients with elevated TyG values demonstrated statistically higher rates of cerebrovascular events, hospitalizations for heart failure, non-fatal myocardial infarctions, non-cardiac mortality, and cardiac mortality. These findings suggest that TyG may serve as a predictive marker for adverse cardiovascular outcomes in patients with CCS.

Atherosclerosis on CT coronary angiography and the risk of long-term cardiovascular events after liver transplantation.

Computed tomography coronary angiography (CTCA) is increasingly utilized for preoperative risk stratification before liver transplantation (LT). We sought to assess the predictors of advanced atherosclerosis on CTCA using the recently developed Coronary Artery Disease-Reporting and Data System (CAD-RADS) score and its impact on the prediction of long-term major adverse cardiovascular events (MACE) following LT. We conducted a retrospective cohort study of consecutive patients who underwent CTCA for LT work-up between 2011 and 2018. Advanced atherosclerosis was defined as coronary artery calcium scores > 400 or CAD-RADS score ≥ 3 (≥50% coronary artery stenosis). MACE was defined as myocardial infarction, heart failure, stroke, or resuscitated cardiac arrest. Overall, 229 patients underwent CTCA (mean age 66 ± 5y, 82% male). Of these, 157 (68.5%) proceeded with LT. The leading etiology of cirrhosis was hepatitis (47%), and 53% of patients had diabetes before transplant. On adjusted analysis, male sex (OR 4.6, 95% CI 1.5-13.8, p = 0.006), diabetes (OR 2.2, 95% CI 1.2-4.2, p = 0.01) and dyslipidemia (OR 3.1, 95% CI 1.3-6.9, p = 0.005) were predictors of advanced atherosclerosis on CTCA. Thirty-two patients (20%) experienced MACE. At a median follow-up of 4 years, CAD-RADS ≥ 3, but not coronary artery calcium scores, was associated with a heightened risk of MACE (HR 5.8, 95% CI 1.6-20.6, p = 0.006). Based on CTCA results, 71 patients (31%) commenced statin therapy which was associated with a lower risk of all-cause mortality (HR 0.48, 95% CI 0.24-0.97, p = 0.04). The standardized CAD-RADS classification on CTCA predicted the occurrence of cardiovascular outcomes following LT, with a potential to increase the utilization of preventive cardiovascular therapies.

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.

Comparison of Systemic Immune-Inflammation Index and Naples Prognostic Score for Prediction Coronary Artery Severity Patients Undergoing Coronary Computed Tomographic Angiography.

This study compared the predictive power of the systemic immune-inflammation index (SII) and Naples prognostic score (NPS) in determining the severity of coronary artery disease (CAD). The study included 1138 patients who underwent coronary computed tomographic angiography (CCTA). The primary outcome was the evaluation of CAD severity, determined by the Coronary Artery Disease-Reporting and Data System (CAD-RADS) obtained from the CCTA scans. A basic statistical model including age, gender, chest pain, diabetes mellitus, hypertension, hyperlipidemia, and smoking was built, and categorical variables, NPS (Naples 3,4 vs 0,1,2) and SII, were added to the basic statistical model. The net benefits of the predictive parameters were determined by a decision curve analysis, and the association between CAD-RADS and NPS, SII was quantified by odds ratios (OR) and 95% confidence intervals (CI). The decision curve analysis showed that adding SII to the statistical model had a better full range of probability of clinical net benefit compared with the baseline model (OR: 5.77, 95% CI 4.15-8.02, P < .001). However, adding the NPS (P = .11) to the model did not outperform the basic statistical model. In conclusion, the SII may have a net predictive effect on top of traditional risk factors.

How scan parameter choice affects deep learning-based coronary artery disease assessment from computed tomography

Recently, algorithms capable of assessing the severity of Coronary Artery Disease (CAD) in form of the Coronary Artery Disease-Reporting and Data System (CAD-RADS) grade from Coronary Computed Tomography Angiography (CCTA) scans using Deep Learning (DL) were proposed. Before considering to apply these algorithms in clinical practice, their robustness regarding different commonly used Computed Tomography (CT)-specific image formation parameters—including denoising strength, slab combination, and reconstruction kernel—needs to be evaluated. For this study, we reconstructed a data set of 500 patient CCTA scans under seven image formation parameter configurations. We select one default configuration and evaluate how varying individual parameters impacts the performance and stability of a typical algorithm for automated CAD assessment from CCTA. This algorithm consists of multiple preprocessing and a DL prediction step. We evaluate the influence of the parameter changes on the entire pipeline and additionally on only the DL step by propagating the centerline extraction results of the default configuration to all others. We consider the standard deviation of the CAD severity prediction grade difference between the default and variation configurations to assess the stability w.r.t. parameter changes. For the full pipeline we observe slight instability (± 0.226 CAD-RADS) for all variations. Predictions are more stable with centerlines propagated from the default to the variation configurations (± 0.122 CAD-RADS), especially for differing denoising strengths (± 0.046 CAD-RADS). However, stacking slabs with sharp boundaries instead of mixing slabs in overlapping regions (called true stack ± 0.313 CAD-RADS) and increasing the sharpness of the reconstruction kernel (± 0.150 CAD-RADS) leads to unstable predictions. Regarding the clinically relevant tasks of excluding CAD (called rule-out; AUC default 0.957, min 0.937) and excluding obstructive CAD (called hold-out; AUC default 0.971, min 0.964) the performance remains on a high level for all variations. Concluding, an influence of reconstruction parameters on the predictions is observed. Especially, scans reconstructed with the true stack parameter need to be treated with caution when using a DL-based method. Also, reconstruction kernels which are underrepresented in the training data increase the prediction uncertainty.

Microvascular Myocardial Ischemia in Patients With Diabetes Without Obstructive Coronary Stenosis and Its Association With Angina.

To investigate the incidence of microvascular myocardial ischemia in diabetic patients without obstructive coronary artery disease (CAD) and its relationship with angina. Diabetic patients and an intermediate-to-high pretest probability of CAD were prospectively enrolled. Non-diabetic patients but with an intermediate-to-high pretest probability of CAD were retrospectively included as controls. The patients underwent dynamic computed tomography-myocardial perfusion imaging (CT-MPI) and coronary computed tomography angiography (CCTA) to quantify coronary stenosis, myocardial blood flow (MBF), and extracellular volume (ECV). The proportion of patients with microvascular myocardial ischemia, defined as any myocardial segment with a mean MBF ≤ of 100 mL/min/100 mL, in patients without obstructive CAD (Coronary Artery Disease-Reporting and Data System [CAD-RADS] grade 0-2 on CCTA) was determined. Various quantitative parameters of the patients with and without diabetes without obstructive CAD were compared. Multivariable analysis was used to determine the association between microvascular myocardial ischemia and angina symptoms in diabetic patients without obstructive CAD. One hundred and fifty-two diabetic patients (mean age: 59.7 ± 10.7; 77 males) and 266 non-diabetic patients (62.0 ± 12.3; 167 males) were enrolled; CCTA revealed 113 and 155 patients without obstructive CAD, respectively. For patients without obstructive CAD, the mean global MBF was significantly lower for those with diabetes than for those without (152.8 mL/min/100 mL vs. 170.4 mL/min/100 mL, P < 0.001). The mean ECV was significantly higher for diabetic patients (27.2% vs. 25.8%, P = 0.009). Among the patients without obstructive CAD, the incidence of microvascular myocardial ischemia (36.3% [41/113] vs. 10.3% [16/155], P < 0.001) and interstitial fibrosis (69.9% [79/113] vs. 33.3% [8/24], P = 0.001) were significantly higher in diabetic patients than in the controls. The presence of microvascular myocardial ischemia was independently associated with angina symptoms (adjusted odds ratio = 3.439, P = 0.037) in diabetic patients but without obstructive CAD. Dynamic CT-MPI + CCTA revealed a high incidence of microvascular myocardial ischemia in diabetic patients without obstructive CAD. Microvascular myocardial ischemia is strongly associated with angina.

Gender-Dependent Comparison of Coronary Computed Tomography Angiographic Characteristics among Patients with Suspected Atherosclerosis: A Single-Center Experience.

In this study, we aimed to examine gender-based differences in coronary artery disease (CAD) risk factors, the presence and severity of atherosclerosis, and the distribution of plaque type in patients presenting with chest pain. A total of 1496 patients who applied to our cardiology outpatient clinic with chest pain and underwent computed coronary tomographic angiography (CTA) between August 2020 and October 2021 were included in the study. Plaque characteristics, Agatston score, and Coronary Artery Disease-Reporting and Data System (CAD-RADS) score obtained from the patients' CTAs were compared by gender. Of the 1496 patients evaluated, 47.9% were female. Coronary atherosclerosis was detected in 35.4% of females and 52.9% of males (P <0.001). Diabetes mellitus [155 (21.8%) vs. 123 (15.7%); P <0.001] and hypertension [271 (38.1%) vs. 249 (32%); P <0.001] rates were higher in females than in males. Plaque burden and high-risk plaque rate were found to be higher in males (P <0.001). Next, the rate of moderate-to-high coronary artery stenosis (CAD-RADS ≥3) was observed at 21.6% in men and 12.2% in women (P <0.001). Agatston score was found to be higher in males than in females for all age groups (P <0.001). The severity of CAD increased sharply with age in females (P interaction = 0.003). Although female patients demonstrated higher rates of traditional risk factors, the male gender was associated with increased coronary plaque burden, high-risk plaque, CADRADS, and Agatston scores. Therefore, patient-based approaches that consider gender-related differences could provide effective treatment and follow-up.