Coronary Computed Tomography Angiography Analysis Research Articles

Highly inflamed coronary plaque detected by Angio-CT in a 28-year-old patient with STEMI and long COVID-19

AbstractLong COVID-19 syndrome increases the risk of cardiovascular events. Although rare in young people, acute coronary syndromes occur more often in those recently infected with COVID-19. This report discusses a rare case of myocardial infarction in a 28-year-old male with no prior medical issues, occurring four weeks after a mild COVID-19. Initially, the patient refused invasive coronary angiography, so a coronary computed tomography angiography (CCTA) was conducted during an ST-elevation myocardial infarction (STEMI). The CCTA, using fat attenuation index (FAI) technology, revealed significant inflammation at the culprit lesion. This CCTA and FAI analysis were done shortly after the STEMI onset, before revascularization, highlighting the case's uniqueness. In patients with recent COVID-19, CCTA combined with FAI analysis of perivascular inflammation can help identify those at risk for acute coronary events. In this case, FAI analysis detected high inflammation, suggesting a potential cause for STEMI in a young patient with long COVID-19.

Usefulness of FFR-CT to exclude haemodynamically significant lesions in high-risk NSTE-ACS.

Coronary computed tomography angiography (CCTA) and fractional flow reserve (FFR) derived from CCTA (FFR-CT) may provide a means of reducing unnecessary invasive coronary angiography (ICA) in patients with suspected non-ST-elevation acute coronary syndromes (NSTE-ACS). The aim of this study was to evaluate the capacity of FFR-CT and CCTA to rule out significant lesions in high-risk NSTE-ACS patients, using ICA with invasive FFR as the gold standard. High-risk NSTE-ACS patients admitted to 4 European centres were enrolled in this single-arm, prospective core lab-adjudicated study. Patients underwent CCTA with FFR-CT analysis, followed by ICA with invasive FFR. Out of the 250 initially planned NSTE-ACS patients, 168 were included, of whom 151 (92%) had sufficient CCTA image quality to undergo CCTA and FFR-CT analysis. The median high-sensitivity troponin T level at 1 hour post-hospitalisation was 5.3 (interquartile range: 1.8-18.6) times the upper reference limit. At the patient level, the diagnostic performance of FFR-CT was numerically higher as compared to CCTA though not statistically significant (sensitivity: 94% vs 93%, specificity: 63% vs 54%, positive predictive value: 83% vs 79%, negative predictive value: 85% vs 80% and accuracy: 83% vs 79%; p=0.58), suggesting an enhanced capability to avoid unnecessary ICA. At the lesion level, the ability of FFR-CT to detect significant lesions was significantly better than that of CCTA (receiver operating characteristic curves: 0.84 vs 0.65 respectively; p<0.01). In patients with high-risk NSTE-ACS, FFR-CT offers better diagnostic accuracy - though not statistically significant - and a higher ability to rule out haemodynamically significant stenoses as compared to CCTA. This indicates that FFR-CT can reduce unnecessary invasive procedures by more accurately identifying patients requiring further intervention.

Impact of hypertension on coronary artery plaques and FFR-CT in type 2 diabetes mellitus patients: evaluation utilizing artificial intelligence processed coronary computed tomography angiography.

This study utilized artificial intelligence (AI) to quantify coronary computed tomography angiography (CCTA) images, aiming to compare plaque characteristics and CT-derived fractional flow reserve (FFR-CT) in type 2 diabetes mellitus (T2DM) patients with or without hypertension (HTN). A retrospective analysis was conducted on 1,151 patients with suspected coronary artery disease who underwent CCTA at a single center. Patients were grouped into T2DM (n = 133), HTN (n = 442), T2DM (HTN+) (n = 256), and control (n = 320). AI assessed various CCTA parameters, including plaque components, high-risk plaques (HRPs), FFR-CT, severity of coronary stenosis using Coronary Artery Disease Reporting and Data System 2.0 (CAD-RADS 2.0), segment involvement score (SIS), and segment stenosis score (SSS). Statistical analysis compared these parameters among groups. The T2DM (HTN+) group had the highest plaque volume and length, SIS, SSS, and CAD-RADS 2.0 classification. In the T2DM group, 54.0% of the plaque volume was noncalcified and 46.0% was calcified, while in the HTN group, these values were 24.0 and 76.0%, respectively. The T2DM (HTN+) group had more calcified plaques (35.7% noncalcified, 64.3% calcified) than the T2DM group. The average necrotic core volume was 4.25 mm3 in the T2DM group and 5.23 mm3 in the T2DM (HTN+) group, with no significant difference (p > 0.05). HRPs were more prevalent in both T2DM and T2DM (HTN+) compared to HTN and control groups (p < 0.05). The T2DM (HTN+) group had a higher likelihood (26.1%) of FFR-CT ≤0.75 compared to the T2DM group (13.8%). FFR-CT ≤0.75 correlated with CAD-RADS 2.0 (OR = 7.986, 95% CI = 5.466-11.667, cutoff = 3, p < 0.001) and noncalcified plaque volume (OR = 1.006, 95% CI = 1.003-1.009, cutoff = 29.65 mm3, p < 0.001). HRPs were associated with HbA1c levels (OR = 1.631, 95% CI = 1.387-1.918). AI analysis of CCTA identifies patterns in quantitative plaque characteristics and FFR-CT values. Comorbid HTN exacerbates partially calcified plaques, leading to more severe coronary artery stenosis in patients with T2DM. T2DM is associated with partially noncalcified plaques, whereas HTN is linked to partially calcified plaques.

Coronary Plaque Radiomic Phenotypes Predict Fatal or Nonfatal Myocardial Infarction: Analysis of the SCOT-HEART Trial

BackgroundCoronary computed tomography (CT) angiography–derived attenuation-based plaque burden assessments can identify patients at risk of myocardial infarction. ObjectivesThis study sought to assess whether more detailed plaque morphology assessment using patient-based radiomic characterization could further enhance the identification of patients at risk of myocardial infarction during long-term follow-up. MethodsPost hoc analysis of coronary CT angiography was performed within the SCOT-HEART (Scottish Computed Tomography of the HEART) clinical trial. Coronary plaque segmentations were used to calculate plaque burdens and eigen radiomic features that described plaque morphology. Univariable and multivariable Cox proportional hazard models were used to evaluate the association between clinical and image-based features and fatal or nonfatal myocardial infarction, whereas Harrell’s C-statistic and cumulative/dynamic area under the curve (AUC) values with cross-validation were used to evaluate prognostic performance. ResultsScans from 1,750 patients (aged 58 ± 9 years; 56% male) were analyzed. Over a median of 8.6 years of follow-up, 82 patients had a fatal or nonfatal myocardial infarction. Among the eigen radiomic features, 15 were associated with myocardial infarction in univariable analysis, and 8 features retained their association following adjustment for cardiovascular risk score and plaque burden metrics. Adding plaque burden metrics to a clinical model incorporating cardiovascular risk score, Agatston score and presence of obstructive coronary artery disease had similar prediction performance (C-statistic 0.70 vs 0.70), whereas further addition of eigen radiomic features improved model performance (C-statistic 0.74). In temporal analysis, the model including eigen radiomic features had higher cumulative/dynamic AUC values following the fifth year of follow-up. ConclusionsRadiomics-based precision phenotyping of coronary plaque morphology provided improvements to long-term prediction of myocardial infarction by CT angiography over and above clinical factors and plaque burden. (Scottish Computed Tomography of the HEART [SCOT-HEART]; NCT01149590)

Artificial intelligence-assisted measurements of coronary computed tomography angiography parameters such as stenosis, flow reserve, and fat attenuation for predicting major adverse cardiac events in patients with coronary arterial disease.

Advancements in artificial intelligence (AI) offer promising tools for improving diagnostic accuracy and patient outcomes in cardiovascular medicine. This study explores the potential of AI-assisted measurements in enhancing the prediction of major adverse cardiac events (MACE) in patients with coronary artery disease (CAD). We conducted a retrospective cohort study involving patients diagnosed with CAD who underwent coronary computed tomography angiography (CCTA). Participants were classified into MACE and non-MACE groups based on their clinical outcomes. Clinical characteristics and AI-assisted measurements of CCTA parameters, including CT-derived fractional flow reserve (CT-FFR) and fat attenuation index (FAI), were collected. Both univariate and multivariable logistic regression analyses were performed to identify independent predictors of MACE, which were used to build predictive models. Statistical analyses revealed three independent predictors of MACE: severe stenosis, CT-FFR ≤ 0.8, and mean FAI (P < 0.05). Seven predictive models incorporating various combinations of these predictors were developed. The model combining all three predictors demonstrated superior performance, as evidenced by the receiver operating characteristic (ROC) curve, with an area under the curve (AUC) of 0.811 (95% confidence interval [CI] 0.774 - 0.847), a sensitivity of 0.776, and a specificity of 0.726. Our findings suggest that AI-assisted CCTA analysis, particularly using fractional flow reserve (FFR) and FAI, could significantly improve the prediction of MACE in patients with CAD, thereby potentially aiding clinical decision making.

Predictors and prognostic value of coronary computed tomography angiography for unrecognized myocardial infarction in patients with chronic coronary syndrome

ObjectiveUnrecognized myocardial infarction (UMI) on delayed-enhancement cardiac magnetic resonance imaging (DE-CMR) and coronary computed tomography angiography (CCTA) derived high-risk features provide prognostic information in patients with chronic coronary syndrome (CCS). The study aimed to assess the prognostic value of UMI and predictors of UMI using CCTA in patients with CCS who underwent elective percutaneous coronary intervention (PCI). MethodsThis study enrolled 181 patients with CCS who underwent DE-CMR and CCTA before elective PCI. The CCTA-derived predictors of UMI and the association of baseline clinical characteristics, CCTA findings, and CMR-derived factors, including UMI, with MACEs, defined as death, nonfatal myocardial infarction, unplanned late revascularization, hospitalization for congestive heart failure, and stroke, were investigated. ResultsUMI was detected in 57 (31.5%) patients. ROC analysis revealed that the optimal cut-off values of Agatston score and mean peri-coronary fat attenuation index (FAI) for predicting the presence of UMI were 397 and −69.8, respectively. The multivariable logistic regression analysis revealed that left ventricular mass, Agatston score >397, mean FAI >−69.8, positive remodeling of the target lesion, and CCTA-derived stenosis severity were independent predictors of UMI. Kaplan–Meier analysis revealed that patients with UMI were associated with increased risk of MACEs. The Cox proportional hazards analysis showed post-PCI minimum lumen diameter and the presence of UMI were independent predictors of MACEs. The risk of MACEs significantly increased according to the number of four preprocedural CCTA-relevant features of UMI. ConclusionPreprocedural comprehensive CCTA analysis may help predict the presence of UMI and provide prognostic information in patients with CCS who underwent PCI.

Quantitative coronary computed tomography assessment for differentiating between total occlusions and severe stenoses

BackgroundsThe impact of quantitative assessment to differentiate total occlusions (TOs) from severe stenoses on coronary computed tomography angiography (CCTA) remains unknown. ObjectiveThis study investigated whether quantitative characteristics assessed on CCTA could help differentiate a TO from a severe stenosis on invasive coronary angiography (ICA). MethodsThis study is a sub-analysis of the FASTTRACK CABG (NCT04142021) in which both CCTA and ICA were routinely performed. Quantitative analysis was performed with semi-automated CCTA plaque-analysis software. Blinded analysts compared TOs on CCTA, defined as a complete lack of contrast opacification within the coronary occlusion, with corresponding ICA. ResultsEighty-four TOs were seen on CCTA in 59 of the 114 patients enrolled in the trial. The concordance in diagnosing a TO between ICA and CCTA was 56.0% (n ​= ​47). Compared to severe stenoses, TOs had a significantly longer lesion length (25.1 ​± ​23.0 ​mm vs 9.4 ​± ​11.2 ​mm, P ​< ​0.001). The best cut-off value to differentiate a TO from severe stenosis was a lesion length of 5.5 ​mm (area under the curve 0.77, 95% CI: 0.66–0.87), with a 91.1% sensitivity and 61.1% specificity. Dense calcium percentage atheroma volume (PAV) was significantly higher in TOs compared to severe stenoses (18.7 ​± ​19.6% vs. 6.6 ​± ​13.0%, P ​< ​0.001), whilst the opposite was seen for fibro-fatty PAV (31.3 ​± ​14.2% vs. 19.5 ​± ​10.5%, P ​< ​0.001). On a multivariable logistic regression analysis, lesion length (>5.5 ​mm) was the only parameter associated with differentiating a TO from a severe stenosis. ConclusionIn quantitative CCTA analysis, a lesion length >5.5 ​mm was the only independent predictor differentiating a TO from a severe stenosis. NCT registration numberNCT04142021.

Artificial Intelligence–Enabled Quantitative Coronary Plaque and Hemodynamic Analysis for Predicting Acute Coronary Syndrome

BackgroundA lesion-level risk prediction for acute coronary syndrome (ACS) needs better characterization. ObjectivesThis study sought to investigate the additive value of artificial intelligence–enabled quantitative coronary plaque and hemodynamic analysis (AI-QCPHA). MethodsAmong ACS patients who underwent coronary computed tomography angiography (CTA) from 1 month to 3 years before the ACS event, culprit and nonculprit lesions on coronary CTA were adjudicated based on invasive coronary angiography. The primary endpoint was the predictability of the risk models for ACS culprit lesions. The reference model included the Coronary Artery Disease Reporting and Data System, a standardized classification for stenosis severity, and high-risk plaque, defined as lesions with ≥2 adverse plaque characteristics. The new prediction model was the reference model plus AI-QCPHA features, selected by hierarchical clustering and information gain in the derivation cohort. The model performance was assessed in the validation cohort. ResultsAmong 351 patients (age: 65.9 ± 11.7 years) with 2,088 nonculprit and 363 culprit lesions, the median interval from coronary CTA to ACS event was 375 days (Q1-Q3: 95-645 days), and 223 patients (63.5%) presented with myocardial infarction. In the derivation cohort (n = 243), the best AI-QCPHA features were fractional flow reserve across the lesion, plaque burden, total plaque volume, low-attenuation plaque volume, and averaged percent total myocardial blood flow. The addition of AI-QCPHA features showed higher predictability than the reference model in the validation cohort (n = 108) (AUC: 0.84 vs 0.78; P < 0.001). The additive value of AI-QCPHA features was consistent across different timepoints from coronary CTA. ConclusionsAI-enabled plaque and hemodynamic quantification enhanced the predictability for ACS culprit lesions over the conventional coronary CTA analysis. (Exploring the Mechanism of Plaque Rupture in Acute Coronary Syndrome Using Coronary Computed Tomography Angiography and Computational Fluid Dynamics II [EMERALD-II]; NCT03591328)

Comparison of Two Contemporary Quantitative Atherosclerotic Plaque Assessment Tools for Coronary Computed Tomography Angiography: Single-Center Analysis and Multi-Center Patient Cohort Validation.

Coronary computed tomography angiography (CCTA) provides non-invasive quantitative assessments of plaque burden and composition. The quantitative assessment of plaque components requires the use of analysis software that provides reproducible semi-automated plaque detection and analysis. However, commercially available plaque analysis software can vary widely in the degree of automation, resulting in differences in terms of reproducibility and time spent. To compare the reproducibility and time spent of two CCTA analysis software tools using different algorithms for the quantitative assessment of coronary plaque volumes and composition in two independent patient cohorts. The study population included 100 patients from two different cohorts: 50 patients from a single-center (Siemens Healthineers, SOMATOM Force (DSCT)) and another 50 patients from a multi-center study (5 different > 64 slice CT scanner types). Quantitative measurements of total calcified and non-calcified plaque volume of the right coronary artery (RCA), left anterior descending (LAD), and left circumflex coronary artery (LCX) were performed on a total of 300 coronaries by two independent readers, using two different CCTA analysis software tools (Tool #1: Siemens Healthineers, syngo.via Frontier CT Coronary Plaque Analysis and Tool #2: Siemens Healthineers, successor CT Coronary Plaque Analysis prototype). In addition, the total time spent for the analysis was recorded with both programs. The patients in cohorts 1 and 2 were 62.8 ± 10.2 and 70.9 ± 11.7 years old, respectively, 10 (20.0%) and 35 (70.0%) were female and 34 (68.0%) and 20 (40.0%), respectively, had hyperlipidemia. In Cohort #1, the inter- and intra-observer variabilities for the assessment of plaque volumes per patient for Tool #1 versus Tool #2 were 22.8%, 22.0%, and 26.0% versus 2.3%, 3.9%, and 2.5% and 19.7%, 21.4%, and 22.1% versus 0.2%, 0.1%, and 0.3%, respectively, for total, noncalcified, and calcified lesions (p < 0.001 for all between Tools #1 and 2 both for inter- and intra-observer). The inter- and intra-observer variabilities using Tool #2 remained low at 2.9%, 2.7%, and 3.0% and 3.8%, 3.7%, and 4.0%, respectively, for total, non-calcified, and calcified lesions in Cohort #2. For each dataset, the median processing time was higher for Tool #1 versus Tool #2 (459.5 s IQR = 348.0-627.0 versus 208.5 s; IQR = 198.0-216.0) (p < 0.001). The plaque analysis Tool #2 (CT-guided PCI) encompassing a higher degree of automated support required less manual editing, was more time-efficient, and showed a higher intra- and inter-observer reproducibility for the quantitative assessment of plaque volumes both in a representative single-center and in a multi-center validation cohort.

Human AI Teaming for Coronary CT Angiography Assessment: Impact on Imaging Workflow and Diagnostic Accuracy

As the number of coronary computed tomography angiography (CTA) examinations is expected to increase, technologies to optimize the imaging workflow are of great interest. The aim of this study was to investigate the potential of artificial intelligence (AI) to improve clinical workflow and diagnostic accuracy in high-volume cardiac imaging centers. A total of 120 patients (79 men; 62.4 (55.0–72.7) years; 26.7 (24.9–30.3) kg/m2) undergoing coronary CTA were randomly assigned to a standard or an AI-based (human AI) coronary analysis group. Severity of coronary artery disease was graded according to CAD-RADS. Initial reports were reviewed and changes were classified. Both groups were similar with regard to age, sex, body mass index, heart rate, Agatston score, and CAD-RADS. The time for coronary CTA assessment (142.5 (106.5–215.0) s vs. 195.0 (146.0–265.5) s; p &lt; 0.002) and the total reporting time (274.0 (208.0–377.0) s vs. 350 (264.0–445.5) s; p &lt; 0.02) were lower in the human AI than in the standard group. The number of cases with no, minor, or CAD-RADS relevant changes did not differ significantly between groups (52, 7, 1 vs. 50, 8, 2; p = 0.80). AI-based analysis significantly improves clinical workflow, even in a specialized high-volume setting, by reducing CTA analysis and overall reporting time without compromising diagnostic accuracy.

Coronary computed tomography angiography analysis using artificial intelligence for stenosis quantification and stent segmentation: a multicenter study.

Accurate interpretation of coronary computed tomography angiography (CCTA) is a labor-intensive and expertise-driven endeavor, as inexperienced readers may inadvertently overestimate stenosis severity. Recent artificial intelligence (AI) advances in medical imaging present compelling prospects for auxiliary diagnostic tools in CCTA. This study aimed to externally validate an AI-assisted analysis system capable of rapidly evaluating stenosis severity, exploring its potential integration into routine clinical workflows. This multicenter study consisted of an internal and external cohort of patients who underwent CCTA scans between April 2017 and February 2023. CCTA scans were evaluated using Coronary Artery Disease Reporting and Data System (CAD-RADS) scores to determine stenosis severity, while ground-truth stents were manually annotated by expert readers. The InferRead CT Heart (version 1.6; Infervision Medical Technology Co., Ltd., Beijing, China), which incorporates AI-assisted coronary artery stenosis quantification and automatic stent segmentation, was employed for CCTA scan analysis. AI-based stenosis assessment performance was determined using sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), while the AI-based stent segmentation overlap was assessed using the Dice similarity coefficient (DSC). For ≥50% stenosis diagnoses, the AI system attained per-patient sensitivity, specificity, PPV, and NPV surpassing 90.0% for the internal dataset; for the external dataset, the per-patient values were 88.0% [95% confidence interval (CI): 81.0-94.4%], 94.5% (95% CI: 90.7-97.6%), 90.0% (95% CI: 83.3-95.6%), and 93.4% (95% CI: 89.2-96.8%), respectively. For ≥70% stenosis diagnoses, the per-patient values on the internal dataset were 94.2% (95% CI: 89.2-98.1%), 95.8% (95% CI: 94.1-97.4%), 80.8% (95% CI: 73.5-87.7%), and 98.9% (95% CI: 97.9-99.6%), respectively; for the external dataset, the per-patient values were 91.9% (95% CI: 82.6-100.0%), 97.3% (95% CI: 94.9-99.1%), 85.0% (95% CI: 72.5-94.6%), and 98.6% (95% CI: 96.8-100.0%), respectively. Regarding CAD-RADS categorization, the Cohen kappa was 0.75 and 0.81 for the internal per-patient and per-vessel basis, respectively, and 0.72 and 0.76 for the external per-patient and per-vessel basis, respectively. The DSC for stent segmentation was 0.96±0.06. The AI-assisted analysis system for CCTA interpretation exhibited exceptional proficiency in stenosis quantification and stent segmentation, indicating that AI holds considerable potential in advancing CCTA postprocessing techniques.

Coronary Atherosclerosis Phenotypes in Focal and Diffuse Disease

BackgroundThe interplay between coronary hemodynamics and plaque characteristics remains poorly understood. ObjectivesThe aim of this study was to compare atherosclerotic plaque phenotypes between focal and diffuse coronary artery disease (CAD) defined by coronary hemodynamics. MethodsThis multicenter, prospective, single-arm study was conducted in 5 countries. Patients with functionally significant lesions based on an invasive fractional flow reserve ≤0.80 were included. Plaque analysis was performed by using coronary computed tomography angiography and optical coherence tomography. CAD patterns were assessed using motorized fractional flow reserve pullbacks and quantified by pullback pressure gradient (PPG). Focal and diffuse CAD was defined according to the median PPG value. ResultsA total of 117 patients (120 vessels) were included. The median PPG was 0.66 (IQR: 0.54-0.75). According to coronary computed tomography angiography analysis, plaque burden was higher in patients with focal CAD (87% ± 8% focal vs 82% ± 10% diffuse; P = 0.003). Calcifications were significantly more prevalent in patients with diffuse CAD (Agatston score per vessel: 51 [IQR: 11-204] focal vs 158 [IQR: 52-341] diffuse; P = 0.024). According to optical coherence tomography analysis, patients with focal CAD had a significantly higher prevalence of circumferential lipid-rich plaque (37% focal vs 4% diffuse; P = 0.001) and thin-cap fibroatheroma (TCFA) (47% focal vs 10% diffuse; P = 0.002). Focal disease defined by PPG predicted the presence of TCFA with an area under the curve of 0.73 (95% CI: 0.58-0.87). ConclusionsAtherosclerotic plaque phenotypes associate with intracoronary hemodynamics. Focal CAD had a higher plaque burden and was predominantly lipid-rich with a high prevalence of TCFA, whereas calcifications were more prevalent in diffuse CAD. (Precise Percutaneous Coronary Intervention Plan [P3]; NCT03782688)

Coronary Computed Tomography Angiography and Abdominal Aortic Calcification Screening among High-Risk Living Kidney Donors

Background: A unique scanning protocol combining coronary computed tomography angiography (CTA) with routine abdominal CTA is being used at the Rabin Medical Center as a method of screening high-risk candidates for living kidney donation. We aim to evaluate the potential impact of coronary CTA on the decision regarding eligibility for kidney donation and its correlation with abdominal aortic calcification (AAC). Methods: CCTA and abdominal CTA results of potential living kidney donors evaluated for donation between September 2020 and November 2021 were retrieved. A retrospective analysis of the abdominal CTA was used to calculate the AAC. Patients’ demographic, clinical, and imaging data were collected from the electronic files, as well as the final decision regarding eligibility for donation. Results: A total of 62 potential kidney donors were evaluated for donation using the combined scan. The mean age was 53.8 years, with male predominance (59.7%). Significant coronary artery stenosis (≥70% luminal stenosis) was present in two patients (3.2%), whereas five patients (8%) had moderate stenosis (50–69%). Thirteen patients (21%) were disqualified from donation due to moderate-to-significant coronary artery disease or abdominal atherosclerosis. The correlation between the coronary artery calcium score and the AAC was found to be positive, with a Pearson correlation coefficient of 0.88 (p &lt; 0.001). Conclusions: The use of coronary CTA in the evaluation of potential kidney donors may has a potential impact on the decision regarding eligibility for donation. A high correlation between the coronary artery calcium score and the AAC was found.

Relationship between coronary volume, myocardial mass, and post-PCI fractional flow reserve.

Fractional flow reserve (FFR) measured after percutaneous coronary intervention(PCI) carries prognostic information. Yet, myocardial mass subtended by a stenosis influences FFR. We hypothesized that a smaller coronary lumen volume and a large myocardial mass might be associated with lower post-PCI FFR. We sought to assess the relationship between vessel volume, myocardial mass, and post-PCIFFR. This was a subanalysis with an international prospective study of patients with significant lesions (FFR ≤ 0.80) undergoing PCI. Territory-specific myocardial mass was calculated from coronary computed tomography angiography (CCTA) using the Voronoi's algorithm. Vessel volume was extracted from quantitative CCTA analysis. Resting full-cycle ratio (RFR) and FFR were measured before and after PCI. We assessed the association between coronary lumen volume (V) and its related myocardial mass (M), and the percentof total myocardial mass (%M) with post-PCI FFR. We studied 120 patients (123 vessels: 94 left anterior descending arteries, 13 left Circumflex arteries, 16 right coronary arteries). Mean vessel-specific mass was 61 ± 23.1 g (%M 39.6 ± 11.7%). The mean post-PCI FFR was 0.88 ± 0.06 FFR units. Post-PCI FFR values were lower in vessels subtending higher mass (0.87 ± 0.05 vs. 0.89 ± 0.07, p = 0.047), and with lower V/M ratio (0.87 ± 0.06 vs. 0.89 ± 0.07, p = 0.02). V/M ratio correlated significantly with post-PCI RFR and FFR (RFR r = 0.37, 95% CI: 0.21-0.52,p < 0.001 and FFR r = 0.41, 95% CI: 0.26-0.55, p < 0.001). Post-PCI RFR and FFR are associated with the subtended myocardial mass and the coronary volume to mass ratio. Vessels with higher mass and lower V/M ratio have lower post-PCI RFR and FFR.

Discordance between Invasive and Non-Invasive Coronary Angiography: An In-Depth Functional and Anatomical Analysis

A 79-year-old male with chronic coronary syndrome with complex coronary artery disease was included in the first-in-man trial of surgical revascularization guided solely by coronary computed tomography angiography (CCTA) and fractional flow reserve derived from CCTA (FFRCT). In CCTA analysis, the patient had calcified three-vessel disease, with a global anatomical SYNTAX score of 27. In contrast, in the initial FFRCT, only the ramus intermediate stenosis was physiologically significant, with no other vessels having an FFRCT ≤ 0.80 (functional SYNTAX score of 2). Discordance between the results of the CCTA and FFRCT necessitated an in-depth analysis by using both invasive and non-invasive coronary angiography. Angiography-derived fractional flow reserve (FFR) confirmed that the stenosis in the proximal left anterior descending artery (LAD) was physiologically significant, while it remained functionally negative in the second assessment of FFRCT. Extensive calcification is the most plausible explanation for the underestimation of the stenosis of proximal LAD in CCTA-derived FFR technology.

Prediction of optical coherence tomography-detected calcified nodules using coronary computed tomography angiography

Diagnosis of calcified nodules (CNs) is critical in the proper management of coronary artery disease, but CNs can be detected only using intracoronary imaging modalities. This study aimed to investigate the ability of coronary computed tomography angiography (CCTA) in predicting CNs detected using optical coherence tomography (OCT). From 138 patients who underwent OCT-guided percutaneous coronary intervention (PCI) after CCTA evaluation, 141 PCI target vessels were retrospectively enrolled and classified into CN (12 vessels/11 patients; CNs in the PCI culprit lesion) and non-CN (129 vessels/127 patients; without CNs) groups based on the OCT analysis. Retrospective CCTA analysis revealed significantly higher coronary artery calcification score (CACS), calcified plaque volume (CPV), and maximum calcified plaque area (MCPA) of the target vessel in the CN group than in the non-CN group. Receiver operating characteristic curve indicated that CACS ≥ 162 (area under the ROC curve (AUC 0.76, sensitivity 83.3%, specificity 54.2%), CPV ≥ 20.1 mm3 (AUC 0.83, sensitivity 100%, specificity 57.3%), and MCPA ≥ 4.51 mm2 (AUC 0.87, sensitivity 91.7%, specificity 78.3%) were the best cutoff values for predicting CNs. MCPA showed the highest AUC among all the CCTA parameters. In conclusion, CCTA is useful for predicting OCT-detected CNs in PCI target vessels.

483 CORONARY ATHEROSCLEROSIS PHENOTYPES IN FOCAL AND DIFFUSE DISEASE

Abstract Introduction The pathophysiological interplay between coronary physiology and plaque characteristics remains poorly understood. Pullback pressure gradient (PPG) is a novel physiological index that discriminates focal from diffuse coronary artery disease (CAD) based on coronary physiology. We aimed to compare plaque characteristics using between atherosclerotic patterns defined by coronary physiology. Methods Multicenter, prospective, controlled, single-arm study conducted in five countries (NCT03782688). Patients with functionally significant lesions based on invasive fractional flow reserve (FFR&amp;lt;0.80) were included. Subjects underwent coronary computed tomography angiography (CCTA) with quantitative plaque analysis followed by an invasive procedure with optical coherence tomography (OCT) and motorized intracoronary pressure recordings. Fractional flow reserve (FFR) pullback curves were processed to calculate the PPG. The PPG ranges from 0, indicating diffuse disease, to 1, pointing to focal CAD. Focal and diffuse CAD were defined according to the median PPG value. Results Overall, 117 patients (120 vessels) were included. The mean age was 64±9, 80% were male, and 22% had diabetes (no difference between focal vs. diffuse). Median PPG was 0.66 [0.54, 0.75]. In CCTA analysis, the plaque burden at minimum lumen area was higher in patients with focal CAD (87±8% focal vs. 82±10% diffuse, p=0.003). Calcifications were significantly more prevalent in patients with diffuse CAD (Agatston score per vessel 50 [9, 166] focal vs. 151 [46, 360] diffuse, p=0.019). In OCT plaque analysis, patients with focal CAD had a significantly higher prevalence of circumferential lipid-rich plaque (37% focal vs. 4% diffuse, p=0.001) and thin-cap fibroatheroma (TCFA 47% focal vs. 10% diffuse, p=0.002). High PPG predicted the presence of TCFA with an AUC of 0.73 (95% CI 0.58 to 0.87). PPG and fibrous cap thickness were negatively correlated (r=-0.55, 95% CI -0.74 to -0.28) independently of FFR. Conclusions Atherosclerotic plaque phenotypes associate with intracoronary hemodynamics. Vessels with focal disease (high PPG) had a higher plaque burden and predominantly lipid-rich plaque with a high prevalence of TCFA, whereas calcifications were the hallmark of vessels with diffuse pressure loss.

Serial analysis of coronary artery disease progression by artificial intelligence assisted coronary computed tomography angiography: early clinical experience

BackgroundStudies have shown that quantitative evaluation of coronary artery plaque on Coronary Computed Tomography Angiography (CCTA) can identify patients at risk of cardiac events. Recent demonstration of artificial intelligence (AI) assisted CCTA shows that it allows for evaluation of CAD and plaque characteristics. Based on publications to date, we are the first group to perform AI augmented CCTA serial analysis of changes in coronary plaque characteristics over 13 years. We evaluated whether AI assisted CCTA can accurately assess changes in coronary plaque progression, which has potential clinical prognostic value in CAD management.Case presentation51-year-old male with hypertension, hyperlipidemia and family history of myocardial infarction, underwent CCTA exams for anginal symptom evaluation and CAD assessment. 5 CCTAs were performed between 2008 and 2021. Quantitative atherosclerosis plaque characterization (APC) using an AI platform (Cleerly), was performed to assess CAD burden. Total plaque volume (TPV) change-over-time demonstrated decreasing low-density non-calcified plaque (LD-NCP) with increasing overall NCP and calcified-plaque (CP). Examination of individual segments revealed a proximal-LAD lesion with decreasing NCP over-time and increasing CP. In contrast, although the D2/D1/ramus lesions showed increasing stenosis, CP, and total plaque, there were no significant differences in NCP over-time, with stable NCP and increased CP. Remarkably, we also consistently visualized small plaques, which typically readers may interpret as false positives due to artifacts. But in this case, they reappeared each study in the same locations, generally progressing in size and demonstrating expected plaque transformation over-time.ConclusionsWe performed the first AI augmented CCTA based serial analysis of changes in coronary plaque characteristics over 13 years. We were able to consistently assess progression of plaque volumes, stenosis, and APCs with this novel methodology. We found a significant increase in TPV composed of decreasing LD-NCP, and increasing NCP and CP, with variations in the evolution of APCs between vessels. Although the significance of evolving APCs needs to be investigated, this case demonstrates AI-based CCTA analysis can serve as valuable clinical tool to accurately define unique CAD characteristics over time. Prospective trails are needed to assess whether quantification of APCs provides prognostic capabilities to improve clinical care.

Detailed behaviour of endothelial wall shear stress across coronary lesions from non-invasive imaging with coronary computed tomography angiography.

Evolving evidence suggests that endothelial wall shear stress (ESS) plays a crucial role in the rupture and progression of coronary plaques by triggering biological signalling pathways. We aimed to investigate the patterns of ESS across coronary lesions from non-invasive imaging with coronary computed tomography angiography (CCTA), and to define plaque-associated ESS values in patients with coronary artery disease (CAD). Symptomatic patients with CAD who underwent a clinically indicated CCTA scan were identified. Separate core laboratories performed blinded analysis of CCTA for anatomical and ESS features of coronary atherosclerosis. ESS was assessed using dedicated software, providing minimal and maximal ESS values for each 3 mm segment. Each coronary lesion was divided into upstream, start, minimal luminal area (MLA), end and downstream segments. Also, ESS ratios were calculated using the upstream segment as a reference. From 122 patients (mean age 64 ± 7 years, 57% men), a total of 237 lesions were analyzed. Minimal and maximal ESS values varied across the lesions with the highest values at the MLA segment [minimal ESS 3.97 Pa (IQR 1.93-8.92 Pa) and maximal ESS 5.64 Pa (IQR 3.13-11.21 Pa), respectively]. Furthermore, minimal and maximal ESS values were positively associated with stenosis severity (P < 0.001), percent atheroma volume (P < 0.001), and lesion length (P ≤ 0.023) at the MLA segment. Using ESS ratios, similar associations were observed for stenosis severity and lesion length. Detailed behaviour of ESS across coronary lesions can be derived from routine non-invasive CCTA imaging. This may further improve risk stratification.

Coronary Computed Tomography Angiography Analysis of Calcium Content to Identify Non-culprit Vulnerable Plaques in Patients With Acute Coronary Syndrome.

BackgroundAside from the culprit plaque, the presence of vulnerable plaques in patients with acute coronary syndrome (ACS) may be associated with future cardiac events. A link between calcification and plaque rupture has been previously described.AimTo assess whether analysis of the calcium component of coronary plaques using CT angiography, coronary computed tomographic angiography (CCTA) can help to detect additional vulnerable plaques in patients with non-ST elevation myocardial infarction (NSTEMI).Materials And MethodsCross sectional study of consecutive patients referred for NSTEMI from 30 July to 30 August 2018 with CCTA performed before coronary angiography with systematic optical coherence tomography (OCT) analysis of all coronary arteries within 24 h of clinical onset of NSTEMI. Three types of plaques were defined: culprit plaques defined by angiography (vulnerable culprit plaques–VCP) – plaques with a fibrous cap thickness < 65 microns or thrombus in OCT (vulnerable non-culprit plaque–VNCP) – plaques with a fibrous cap thickness ≥ 65 microns in OCT (stable plaque–SP).ResultsA total of 134 calcified plaques were identified in 29 patients (73% male, 59 ± 14 years) with 29(22%) VCP, 28(21%) VNCP and 77(57%) SP. Using CCTA analysis of the calcium component, factors associated with vulnerable plaques were longer calcification length, larger calcification volume, lower calcium mass, higher Agatston score plaque-specific (ASp), presence of spotty calcifications and an intimal position in the wall. In multivariate analysis, ASp, calcification length and spotty calcifications were independently associated to vulnerable plaques. There was no difference between VCP and VNCP.ConclusionsCCTA analysis of calcium component of the plaque could help to identify additional vulnerable plaques in NSTEMI patients.