A Comparative Study of Conventional Risk Models for Coronary Artery Disease and Computed Tomography Coronary Angiography in the Indian Population

Rationale, design, and baseline characteristics of Chinese registry in early detection and risk stratification of coronary plaques (C-STRAT) study.

Background Chest pain is a leading cause of outpatient and emergency department visits; advancements in artificial intelligence (AI) could improve coronary CT angiography (CCTA) workflows for these patients. Purpose To evaluate the performance of an on-premise AI-based coronary artery calcium scoring (CACS) and CCTA analysis software against expert interpretation based on Coronary Artery Disease Reporting and Data System (CAD-RADS) 2.0. Materials and Methods This retrospective study included consecutive patients undergoing CCTA for coronary analysis at a tertiary academic center between January 2017 and October 2021 across four scanners from three vendors. Patients with stents, bypass grafts, anomalies, or nondiagnostic studies were excluded. On-premise AI output included CACS, CAD-RADS category, and segment involvement score (SIS) within less than 5 minutes. Original CCTA reports were used as the reference, and discrepancies between AI and reports were further adjudicated by two blinded level-III readers with 8 and 5 years of CCTA experience. Agreement among CACS risk categories, CAD-RADS categories, and plaque burden scores was measured with the weighted κ. The area under the receiver operating characteristic curve, positive predictive value, and negative predictive value were used to evaluate diagnostic performance. Bootstrapping was used to estimate 95% CIs. Results A total of 1032 patients (median age, 62 [IQR, 54-69] years; 581 female) with 1041 CCTA images were included: 361 of the 1041 images (35%) were classified as CAD-RADS 0, 274 (26%) as CAD-RADS 1, 186 (18%) as CAD-RADS 2, 101 (10%) as CAD-RADS 3, 95 (9%) as CAD-RADS 4A, 11 (1%) as CAD-RADS 4B, and 13 (1%) as CAD-RADS 5. There was substantial agreement between AI and expert CAD-RADS stenosis severity categories (weighted κ = 0.73). AI demonstrated high performance (per-scan area under the receiver operating characteristic curve, 0.90; 95% CI: 0.87, 0.92) for CAD-RADS greater than or equal to 3 or greater than or equal to 4A and high negative predictive value (98%; 95% CI: 97, 99) but low positive predictive value (39%; 95% CI: 32, 45) for CAD-RADS greater than or equal to 4A. AI-based plaque burden scores derived from CACS reached near-perfect agreement with experts (weighted κ = 0.97), whereas those derived from SIS showed substantial agreement (weighted κ = 0.79). Conclusion On-premise AI accurately ruled out obstructive coronary artery disease at CCTA and achieved substantial to near-perfect agreement with human experts for CAD-RADS 2.0 stenosis severity and plaque burden. © RSNA, 2025 Supplemental material is available for this article. See also the editorial by van Assen and De Cecco in this issue.

Coronary Inflammation in Patients With and Without Standard Modifiable Cardiovascular Risk Factors: Insights From a Novel Computed Tomography Imaging Biomarker Pericoronary Adipose Tissue Attenuation.

BackgroundPCAT attenuation is a non-invasive biomarker of coronary inflammation and is a poor prognostic factor in patients with coronary artery disease.ObjectivesTo compare the severity of PCAT attenuation amongst patients with...

This scientific statement reviews the scientific data for cardiac computed tomography (CT) related to imaging of coronary artery disease (CAD) and atherosclerosis. Cardiac CT is a CT imaging technique that accounts for cardiac motion, typically through the use of ECG gating. The utility and limitations of generations of cardiac CT systems are reviewed in this statement with emphasis on CT measurement of CAD and coronary artery calcified plaque (CACP) and noncalcified plaque. Successive generations of CT technology have been applied to cardiac imaging beginning in the early 1980s with conventional CT, electron beam CT (EBCT) in 1987, and multidetector CT (MDCT) in 1999. Compared with other imaging modalities, cardiac CT has undergone an accelerated …

In recent decades, there has been an appropriate focus on ensuring gender equity in the quantity and quality of evidence to guide female-specific, optimal management strategies for suspected and known ischemic heart disease (IHD). The evolving evidence supports a multifactorial pathophysiology of coronary atherosclerosis that includes obstructive coronary artery disease (CAD) and dysfunction of the coronary microvasculature and endothelium, and therefore, the term IHD best encompasses this varied pathophysiology in women. An overwhelming body of evidence has documented undertreatment and undertesting of women, leading to higher case fatality rates and increased morbid complications among women.1–3 Accordingly, to increase our knowledge base, women were given the status of a priority population, which resulted in federal policy to include proportional representation of females in clinical trials and registries.4 The past decade provided abundant evidence to guide clinical decision making regarding diagnostic testing for suspected IHD. In 2005, the American Heart Association (AHA) published an evidence synthesis on the use of CAD imaging for the evaluation of symptomatic women with suspected myocardial ischemia.5 Numerous reports have since provided additional high-quality evidence, including data on coronary computed tomographic angiography (CCTA) and cardiac magnetic resonance imaging (CMR), which in 2005 were considered research techniques.5 The present statement provides an update to the 2005 document and synthesizes contemporary evidence on appropriate symptomatic female candidates for diagnostic testing, as well as sex-specific data on the diagnostic and prognostic accuracy for exercise treadmill testing (ETT) with electrocardiography, stress echocardiography, stress myocardial perfusion imaging (MPI) with single-photon emission computed tomography (SPECT) or positron emission tomography (PET), stress CMR, and CCTA.5 Within this document, quality evidence is synthesized, and important gaps in knowledge about the assessment of IHD risk in women are identified. The 2005 document included sections on the evaluation of asymptomatic …

Traditional prognostic risk assessment in patients undergoing non-invasive imaging is based upon a limited selection of clinical and imaging findings. Machine learning (ML) can consider a greater number and complexity of variables. Therefore, we investigated the feasibility and accuracy of ML to predict 5-year all-cause mortality (ACM) in patients undergoing coronary computed tomographic angiography (CCTA), and compared the performance to existing clinical or CCTA metrics. The analysis included 10 030 patients with suspected coronary artery disease and 5-year follow-up from the COronary CT Angiography EvaluatioN For Clinical Outcomes: An InteRnational Multicenter registry. All patients underwent CCTA as their standard of care. Twenty-five clinical and 44 CCTA parameters were evaluated, including segment stenosis score (SSS), segment involvement score (SIS), modified Duke index (DI), number of segments with non-calcified, mixed or calcified plaques, age, sex, gender, standard cardiovascular risk factors, and Framingham risk score (FRS). Machine learning involved automated feature selection by information gain ranking, model building with a boosted ensemble algorithm, and 10-fold stratified cross-validation. Seven hundred and forty-five patients died during 5-year follow-up. Machine learning exhibited a higher area-under-curve compared with the FRS or CCTA severity scores alone (SSS, SIS, DI) for predicting all-cause mortality (ML: 0.79 vs. FRS: 0.61, SSS: 0.64, SIS: 0.64, DI: 0.62; P< 0.001). Machine learning combining clinical and CCTA data was found to predict 5-year ACM significantly better than existing clinical or CCTA metrics alone.

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.

AimsWe wished to assess whether different clinical definitions of coronary artery disease (CAD) [segment stenosis and involvement score (SSS, SIS), Coronary Artery Disease—Reporting and Data System (CAD-RADS)] affect which patients are considered to progress and which risk factors affect progression.Methods and resultsWe enrolled 115 subsequent patients (60.1 ± 9.6 years, 27% female) who underwent serial coronary computed tomography angiography (CTA) imaging with >1year between the two examinations. CAD was described using SSS, SIS, and CAD-RADS. Linear mixed models were used to investigate the effects of risk factors on the overall amount of CAD and the effect on annual progression rate of different definitions. Coronary plaque burdens were SSS 4.63 ± 4.06 vs. 5.67 ± 5.10, P < 0.001; SIS 3.43 ± 2.53 vs. 3.89 ± 2.65, P < 0.001; CAD-RADS 0:8.7% vs. 0.0% 1:44.3% vs. 40.9%, 2:34.8% vs. 40.9%, 3:7.0% vs. 9.6% 4:3.5% vs. 6.1% 5:1.7% vs. 2.6%, P < 0.001, at baseline and follow-up, respectively. Overall, 53.0%, 29.6%, and 28.7% of patients progressed over time based on SSS, SIS, and CAD-RADS, respectively. Of the patients who progressed based on SSS, only 54% showed changes in CAD-RADS. Smoking and diabetes increased the annual progression rate of SSS by 0.37/year and 0.38/year, respectively (both P < 0.05). Furthermore, each year increase in age raised SSS by 0.12 [confidence interval (CI) 0.05–0.20, P = 0.001] and SIS 0.10 (CI 0.06–0.15, P < 0.001), while female sex was associated with 2.86 lower SSS (CI −4.52 to −1.20, P < 0.001) and 1.68 SIS values (CI −2.65 to −0.77, P = 0.001).ConclusionCAD-RADS could not capture the progression of CAD in almost half of patients with serial CTA. Differences in CAD definitions may lead to significant differences in patients who are considered to progress, and which risk factors are considered to influence progression.

Aim to evaluate the relationships between functional and anatomical information obtained by myocardial perfusion imaging (MPI) and coronary computed tomography angiography (CCTA) in a series of consecutive patients at intermediate probability of coronary artery disease (CAD). Material and Methods — The study group comprised 139 patients (83 men, age of 61.6±7.5 years) who underwent CCTA and single-photon emission computed tomography myocardial perfusion imaging (SPECT-MPI). Based on CCTA results patients were divided into three groups: 1) with the absence of coronary atherosclerosis on CCTA; 2) with non-obstructive CAD (&lt;50%); 3) with obstructive (≥50%) CAD. The Segment Involvement Score, Segment Stenosis Score (SSS) and CTA Risk Score were calculated as measures of global atherosclerosis burden. MPI studies were considered abnormal in the presence of SSS≥4. Results — Abnormal myocardial perfusion was detected in 60% of cases in group 1 and 2; in 75% of cases in group 3. The overall frequencies of normal and abnormal MPI studies differed significantly only in obstructive CAD patients and did not differ in group 1 and 2. There were no significant correlations between calcium score, atherosclerotic lesion length, positive remodelling index and MPI results in patients with non-obstructive as well as in patients with obstructive CAD. In group of patients with obstructive CAD Segment Stenosis Score correlated wekly with SSS (r=0.39, p=0.001) and SDS (r=0.28; p=0.012); the CTA Risk Score showed correlationes with SSS (r=0.38, p=0.002) and SDS (r=0.30, p=0.020). Conclusion — Myocardial perfusion abnormalities may develop even in the absence of critical coronary artery lesions. The extent of myocardial ischemia correlates with measures of global CAD burden only in patients with obstructive CAD.

Coronary artery disease (CAD) is a leading cause of mortality and disability worldwide. CT coronary angiography is a fast and non-invasive method for diagnosing coronary artery pathology. To standardize the assessment of CT coronary angiography results, the CAD-RADS (Coronary Artery Disease – Reporting and Data System) was developed, which is based on determining the degree of coronary artery stenosis. According to recent studies, in addition to CT coronary angiography, an important tool for cardiovascular risk stratification in asymptomatic patients is the assessment of coronary calcium score using the Agatston scale (CAC Score). Aim. To assess the relationship and degree of correlation between the CAC Score and the presence of significant coronary artery stenosis when combining CAC Score and CT coronary angiography. Materials and Methods. The data of 464 patients from the National Institute of Cardiovascular Surgery were analyzed. These patients presented with typical or atypical angina symptoms and had a low or moderate risk of coronary artery disease between September 1, 2024, and January 15, 2025. All patients underwent clinical examination, risk factor assessment, CAC Score evaluation, and CT coronary angiography. Patients with atherosclerosis were divided into two groups: those with non-significant stenosis and those with significant stenosis (≥50% in the left main coronary artery or ≥70% in the major epicardial coronary arteries). Results. The study included 464 patients, predominantly male (55.6%), with a mean age of 59 ± 10.22 years. Atherosclerotic coronary artery disease was detected in 273 patients (58.8%). Based on the CAD-RADS, 24.54% of patients had CAD-RADS 1, 31.14% had CAD-RADS 2, 16.12% had CAD-RADS 3, 20.88% had CAD-RADS 4A, 5.49% had CAD-RADS 4B, and 1.83% had CAD-RADS 5. In the first group (non-significant stenosis), there were 196 patients (71.8%), while the second group (significant stenosis) included 77 patients (28.2%). The median CAC Score in the first group was 24.5 (1–103.25), while in the second group, it was 271.5 (88–666.5), p&lt;0.001. A moderately strong positive correlation was found between the CAC Score and the presence of significant coronary artery stenosis (ρ=0.635, p&lt;0.001). ROC curve analysis shows that the optimal cutoff value of the CAC Score for detecting significant stenosis was 282, with a sensitivity of 48.7%, specificity of 88.8%, and an AUC of 0.819. Conclusions. The study identified a statistically significant correlation between the level of coronary calcification and the presence of significant stenosis. The CAC Score is a reliable prognostic marker for significant coronary artery stenosis in patients with suspected coronary artery disease.

AimsWe aimed to compare semiquantitative coronary computed tomography angiography (CCTA) risk scores – which score presence, extent, composition, stenosis and/or location of coronary artery disease (CAD) – and their prognostic value between patients with and without diabetes mellitus (DM). Risk scores derived from general chest-pain populations are often challenging to apply in DM patients, because of numerous confounders. MethodsOut of a combined cohort from the Leiden University Medical Center and the CONFIRM registry with 5-year follow-up data, we performed a secondary analysis in diabetic patients with suspected CAD who were clinically referred for CCTA. A total of 732 DM patients was 1:1 propensity-matched with 732 non-DM patients by age, sex and cardiovascular risk factors. A subset of 7 semiquantitative CCTA risk scores was compared between groups: 1) any stenosis ≥50%, 2) any stenosis ≥70%, 3) stenosis-severity component of the coronary artery disease-reporting and data system (CAD-RADS), 4) segment involvement score (SIS), 5) segment stenosis score (SSS), 6) CT-adapted Leaman score (CT-LeSc), and 7) Leiden CCTA risk score. Cox-regression analysis was performed to assess the association between the scores and the primary endpoint of all-cause death and non-fatal myocardial infarction. Also, area under the receiver-operating characteristics curves were compared to evaluate discriminatory ability. ResultsA total of 1,464 DM and non-DM patients (mean age 58 ± 12 years, 40% women) underwent CCTA and 155 (11%) events were documented after median follow-up of 5.1 years. In DM patients, the 7 semiquantitative CCTA risk scores were significantly more prevalent or higher as compared to non-DM patients (p ≤ 0.022). All scores were independently associated with the primary endpoint in both patients with and without DM (p ≤ 0.020), with non-significant interaction between the scores and diabetes (interaction p ≥ 0.109). Discriminatory ability of the Leiden CCTA risk score in DM patients was significantly better than any stenosis ≥50% and ≥70% (p = 0.003 and p = 0.007, respectively), but comparable to the CAD-RADS, SIS, SSS and CT-LeSc that also focus on the extent of CAD (p ≥ 0.265). ConclusionCoronary atherosclerosis scoring with semiquantitative CCTA risk scores incorporating the total extent of CAD discriminate major adverse cardiac events well, and might be useful for risk stratification of patients with DM beyond the binary evaluation of obstructive stenosis alone.

ObjectivesThe 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). MethodsA 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. ResultsIn 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. ConclusionsThe 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.

Objective: To analyze features of coronary artery disease (CAD) on CT angiography (CTA) in the cases of atypical chest pain with troponin-negative (TNEG), by the retrospective study of 4.5-year data documents, Jan 2020 to Apr 2024. Materials and Methods: There were 270 eligible cases of atypical chest pain with troponin-negative (TNEG), determined by data documents via Picture Archiving and Communication System (PACS) and Hospital Information System (HIS), Jan 2020 to Apr 2024. The known cases of CAD, status post coronary stent (s), and surgical bypass graft (s) were excluded. The eligible cases were analyzed cardiovascular risk factors, determined by Framingham Risk Score (FRS), the degrees of coronary artery stenosis, the degrees of coronary artery calcification, and high-risk plaque features (HRPF) on CT angiography (CTA), using the standardized CAD reporting and data system (CAD-RADS) version 2.0 (2022) of American College of Cardiology (ACC)/American Heart Association (AHA). Logistic regression, t-test, Chi-square, Spearman’s correlation, and Adjusted Odd ratio were used analyzed the significance of variables. Results: The mean age [range] of 270 eligible cases was 57 [24-90] years. Evidence of coronary artery disease (CAD-RADS 1 to 5, from 1% to 100% coronary stenosis) and obstructive coronary artery (&gt;50% stenosis) were present in 118 cases (42.7%) and 66 cases (24.4%), respectively. The HRPF was found in 48 cases (17.8%), mean age [range]: 61 [42-77] years. The age’s group of 50 to 99 years-old had significant associated with CAD and obstructive CAD. There were 190 cases (70%) had intermediate to high cardiovascular risk (FRS). The intermediate FRS was associated with positive CAD at 6.34 times to the low FRS [Multivariate adjusted OR = 6.34 (95% CI (3.16-12.73)]. The high FRS was associated with positive CAD at 10.3 times to the low FRS [Multivariate adjusted OR = 10.3 (95% CI (4.40-24.20)]. Moreover, there were the strong correlation between CAD-RADS 0 (0% stenosis) and the coronary artery calcium score (CACS) 0-100 [0 and P1], and moderate to strong correlation between the obstructive coronary artery (CAD-RADS 3-5) and CACS &gt; 301 [P3 and P4]. Conclusion: Atypical chest pain with troponin-negative (TNEG) on the age &gt; 50-year-old with intermediate to high cardiovascular risk (FRS) had significant obstructive coronary disease (&gt; 50% stenosis, CAD-RADS 3 to 5) and presence of high-risk plaque features (HRPF), determined by CT angiography.

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.