Improved diagnostic accuracy of vessel-specific myocardial ischemia by coronary computed tomography angiography (CCTA).

BACKGROUND: Discrepancies between stenosis severity assessed at coronary computed tomography angiography (CCTA) and ischemia might depend on vessel type. Coronary plaque features are associated with ischemia. Thus, we evaluated the vessel-specific correlation of CCTA-derived diameter stenosis (DS) and invasive fractional flow reserve (FFR) and explored whether integrating morphological plaque features stratified by vessel might increase the predictive yield in identifying vessel-specific ischemia. METHODS: Observational cohort study including patients undergoing CCTA for suspected coronary artery disease, with at least one vessel with DS â€‹≥ â€‹50 â€‹% at CCTA, undergoing invasive coronary angiography and FFR. Plaque analysis was performed using validated semi-automated software. Coronary vessels were stratified in left anterior descending (LAD), left circumflex (LCX), and right coronary artery (RCA). Per vessel independent predictors of ischemia among CCTA-derived anatomical and morphologic plaque features were tested at univariable and multivariable logistic regression analysis. The best cut-off to predict ischemia was determined by Youden's index. Ischemia was defined by FFR≤0.80. RESULTS: The study population consisted of 192 patients, of whom 224 vessels (61 â€‹% LAD, 19 â€‹% LCX, 20 â€‹% RCA) had lesions with DS â€‹≥ â€‹50 â€‹% interrogated by FFR. Despite similar DS, the rate of FFR≤0.80 was higher in the LAD compared to LCX and RCA (67.2 â€‹% vs 43.2 â€‹% and 44.2 â€‹%, respectively, p â€‹= â€‹0.018). A significant correlation between DS and FFR was observed only in LAD (p â€‹= â€‹0.003). At multivariable analysis stratified by vessel, the vessel-specific independent predictors of positive FFR were percent atheroma volume (threshold>17 â€‹%) for LAD, non-calcified plaque volume (threshold >130 â€‹mm3) for LCX, and lumen volume (threshold <844 â€‹mm3) for RCA. Integrating DS and vessel-specific morphological plaque features significantly increased the predictive yield for ischemia compared to DS alone (AUC ranging from 0.51 to 0.63 to 0.76-0.80). CONCLUSIONS: Integrating DS and vessel-specific morphological plaque features significantly increased the predictive yield for vessel-specific ischemia compared to DS alone, potentially improving patients' referral to the catheterization laboratory.

The Effects of Red Palm Oil, Koja Bay Leaves, and Passion Fruit Seeds Formulation on Antioxidant Activity, Antihyperlipidemia, BDNF, and Lipase Enzyme Activity on Sprague-Dawley Rats.

Background: Local wisdom food ingredients in North Sumatra, Indonesia, are a source of phenolics which have antioxidant, antihyperlipidemia, neuronal survival, and growth. Administering products with antioxidant properties can provide a supporting effect in preventing inflammation and neurodegenerative process. Objective: The main objective of this study was to analyze the formulation of red palm oil (Elaeis guineensis Jacq), koja bay leaves (Murraya koenigii L Spreng), and passion fruit seeds (Passiflora edulis Sims) to improve lipid profile, antioxidant activity, Brain-Derived Neurotrophic Factor (BDNF), and lipase enzyme activity of Sprague-Dawley rats. Methods: This study was an in vivo and pre-post experimental study, starting with analyzing flavonoid of the three extract ingredients, then tested by giving it to rats for 14 days and ending with induction administration of lipopolysaccharide (LPS) for two days. This pre-post study on animals involved 36 rats divided into 6 groups. At the end of the study, termination and examination of malondialdehyde, lipid profile, glucose, BDNF, lipase enzyme activity and histopathological examination were carried out. Results: The study results showed that there were significant values in several parameters, which were body weight, LDL, LDL/HDL ratio, BDNF, and lipase enzyme activity especially in the group of rats given LPS and the group with high calories-fat-protein. This study showed that there were significant differences in body weight, LDL levels, and LDL/HDL ratio in each group of rats, especially in the group given the formulation of the three extract ingredients, the significant dose showed in 300mg/kg body weight (p < 0.001). Conclusion: The formulation of red palm oil, koja bay leaves, and passion fruit seeds showed significant reduction in LDL levels, LDL/HDL ratio, BDNF, and lipase enzyme activity.

Utility of Artificial Intelligence Plaque Quantification: Results of the DECODE Study.

Background: Artificial Intelligence Plaque Analysis (AI-QCPA, HeartFlow) provides, from a CCTA, quantitative plaque burden information including total plaque and plaque subtype volumes. We sought to evaluate the clinical utility of AI-QCPA in clinical decision making. Methods: One hundred cases were reviewed by 3 highly experienced practicing cardiologists who are SCCT level 3 CCTA readers. Patients had varying levels of calcium (median CACS: 99.5) and CAD-RADS scores. Initial management plan for each case was a majority decision based upon patient demographics, clinical history, and CCTA report. AI-QCPA was then provided for each patient, and the plan was reconsidered. The primary endpoint was the reclassification rate (RR). In a secondary analysis of 40 cases, the above process was repeated but the initial plan was based upon review of the actual CCTA images. Results: RR following AI-QCPA review was 66% (66/100) of cases (95% CI, 56.72%-75.28%). RR ranged from 47% in cases with CACS 0 to 96% in cases with CACS >400, and from 40% in CAD-RADS 1 cases to 94% in CAD-RADS 4 cases. RR was higher in cases with coronary stenoses ≥50% (89.5%) vs cases with stenoses <50% (51.6%). RR was 39% in cases with LDL <70 mg/dL vs 70% in LDL ≥70 mg/dL. Following review of the CCTA images rather than the CCTA report, the RR was 50% (95% CI of 34.51% - 65.49%). The primary reclassification effect was to intensify preventative medical therapy. Conclusions: Adding AI-QCPA to CCTA alone leads to a change in clinical care in two-thirds of patients.

Additional prognostic impact of plaque characterization with on-site CT-derived fractional flow reserve in coronary CT angiography.

BACKGROUND: On-site computed tomography-derived fractional flow reserve (CT-FFR) is a feasible method for examining lesion-specific ischemia, and plaque analysis of coronary CT angiography (CCTA) is useful for predicting future cardiac events. However, their utility and association on a per-vessel level remain unclear. METHODS: We analyzed vessels showing 50-90 % stenosis on CCTA where planned revascularization was not performed after CCTA within 90 days. Relevant features, including CT-FFR and the plaque burden [necrotic core to the total plaque volume (% necrotic core), and non-calcified plaque (NCP) to vessel volume (% NCP)] using a novel algorithm for analyzing plaque to predict vessel-oriented composite outcomes (VOCO), including cardiac death, non-fatal myocardial infarction, and unplanned vessel-related revascularization, were assessed. RESULTS: In 256 patients (68.7 ±â€¯9.4 years; 73.8 % male) with 354 vessels (10.5 % CT-FFR ≤ 0.80), VOCO occurred in 24 vessels (6.8 %) during a median follow-up of 3.6 years. Multivariable Cox analysis revealed CT-FFR ≤ 0.80 had the pronounced impact on VOCO, and moreover, higher % necrotic core and % NCP were independently associated with VOCO [adjusted hazard ratio 3.43 (95 % confidence interval 1.42-8.29) and 4.05 (1.19-13.71), respectively], especially for vessels with CT-FFR > 0.80. CONCLUSIONS: In vessels without planned revascularization, per-vessel CT-FFR ≤ 0.80 was the notable predictor of future cardiac events. Additionally, necrotic core volume and NCP were identified as independent predictors along with CT-FFR.

Application of Quantitative Assessment of Coronary Atherosclerosis by Coronary Computed Tomographic Angiography.

Coronary computed tomography angiography (CCTA) has emerged as a pivotal tool for diagnosing and risk-stratifying patients with suspected coronary artery disease (CAD). Recent advancements in image analysis and artificial intelligence (AI) techniques have enabled the comprehensive quantitative analysis of coronary atherosclerosis. Fully quantitative assessments of coronary stenosis and lumen attenuation have improved the accuracy of assessing stenosis severity and predicting hemodynamically significant lesions. In addition to stenosis evaluation, quantitative plaque analysis plays a crucial role in predicting and monitoring CAD progression. Studies have demonstrated that the quantitative assessment of plaque subtypes based on CT attenuation provides a nuanced understanding of plaque characteristics and their association with cardiovascular events. Quantitative analysis of serial CCTA scans offers a unique perspective on the impact of medical therapies on plaque modification. However, challenges such as time-intensive analyses and variability in software platforms still need to be addressed for broader clinical implementation. The paradigm of CCTA has shifted towards comprehensive quantitative plaque analysis facilitated by technological advancements. As these methods continue to evolve, their integration into routine clinical practice has the potential to enhance risk assessment and guide individualized patient management. This article reviews the evolving landscape of quantitative plaque analysis in CCTA and explores its applications and limitations.

Artificial intelligence-powered insights into high-risk, non-obstructive coronary atherosclerosis: a case report.

Background: Advanced coronary plaque analysis by cardiac computed tomography (CT) has recently emerged as a promising technique for better prognostic stratification. However, this evaluation application in clinical practice is still uncertain. Case summary: In the present case, we described the clinical picture of a 44-year-old tennis player with ectopic ventricular beats in which cardiac CT enabled the identification of a non-obstructive but high-risk plaque on proximal left anterior descendent artery. The application of artificial intelligence (AI)-enhanced software enabled to better stratify the patients' risk. The present case describes how early identification of non-obstructive but high-risk coronary plaque evaluated by cardiac CT using AI-enhanced software enabled accurate and personalized risk assessment. Discussion: The main clinical message of this case report is that advanced plaque analysis by cardiac CT, especially when performed with AI-based software, may provide important prognostic information leading to a personalized preventive approach. Moreover, AI-based software may contribute to promote a routine evaluation of these important data already included in traditional cardiac CT.

Predictors of percutaneous coronary intervention derived from CCTA in patients with chronic coronary syndrome.

BACKGROUND: To identify anatomical and morphological plaque features predictors of PCI and create a multiparametric score to increase the predictive yield. Moreover, we assessed the incremental predictive value of FFRCT (Fractional Flow Reserve derived from CCTA) trans-lesion gradient (ΔFFRCT) when integrated into the score. METHODS: Observational cohort study including patients undergoing CCTA for suspected coronary artery disease, with FFRCT available, referred to invasive coronary angiogram and assessment of fractional flow reserve. Plaque analysis was performed using validated semi-automated software. Logistic regression was performed to identify anatomical and morphological plaque features predictive of PCI. Optimal thresholds were defined by area under the receiver-operating characteristics curve (AUC) analysis. A scoring system was developed in a derivation cohort (70 â€‹% of the study population) and tested in a validation cohort (30 â€‹% of patients). RESULTS: The overall study population included 340 patients (455 vessels), among which 238 patients (320 vessels) were included in the derivation cohort. At multivariate logistic regression analysis, absence of left main disease, diameter stenosis (DS), non-calcified plaque (NCP) volume, and percent atheroma volume (PAV) were independent predictors of PCI. Optimal thresholds were: DS â€‹≥ â€‹50 â€‹%, volume of NCP>113 â€‹mm3 and PAV>17 â€‹%. A weighted score (CT-PCI Score) ranging from 0 to 11 was obtained. The AUC of the score was 0.80 (95%CI 0.74-0.86). The integration of ΔFFRCT in the CT-PCI score led to a mild albeit not significant increase in the AUC (0.82, 95%CI 0.77-0.87, p â€‹= â€‹0.328). CONCLUSIONS: Plaque anatomy and morphology derived from CCTA could aid in identifying patients amenable to PCI.

Characteristics and evaluation of atherosclerotic plaques: an overview of state-of-the-art techniques.

Atherosclerosis is an important cause of cerebrovascular and cardiovascular disease (CVD). Lipid infiltration, inflammation, and altered vascular stress are the critical mechanisms that cause atherosclerotic plaque formation. The hallmarks of the progression of atherosclerosis include plaque ulceration, rupture, neovascularization, and intraplaque hemorrhage, all of which are closely associated with the occurrence of CVD. Assessing the severity of atherosclerosis and plaque vulnerability is crucial for the prevention and treatment of CVD. Integrating imaging techniques for evaluating the characteristics of atherosclerotic plaques with computer simulations yields insights into plaque inflammation levels, spatial morphology, and intravascular stress distribution, resulting in a more realistic and accurate estimation of plaque state. Here, we review the characteristics and advancing techniques used to analyze intracranial and extracranial atherosclerotic plaques to provide a comprehensive understanding of atheroma.

Interplay Between Zero CAC, Quantitative Plaque Analysis, and Adverse Events in a Diverse Patient Cohort.

BACKGROUND: Coronary artery calcium scoring (CAC) has garnered attention in the diagnostic approach to chest pain patients. However, little is known about the interplay between zero CAC, sex, race, ethnicity, and quantitative coronary plaque analysis. METHODS: We conducted a retrospective analysis from our computed tomography registry of patients with stable angina without prior myocardial infarction or revascularization undergoing coronary computed tomography angiography at Montefiore Healthcare System. Follow-up end points collected included invasive angiography, type-1 myocardial infarction, coronary revascularization, cardiovascular and all-cause death. RESULTS: A total of 2249 patients were included (66% female). The median follow-up was 5.5 years. The median age of those without CAC was 52 years (interquartile range, 44-59) and 60 years (interquartile range, 53-68) in those with CAC. Most patients were Hispanic (58%), and the rest were non-Hispanic Black (28%), non-Hispanic White (10%), and non-Hispanic Asian (5%). The majority had CAC=0 (55%). The negative predictive value of CAC=0 was 92.8%, 99.9%, and 99.9% for any plaque, obstructive coronary artery stenosis, and the composite outcome of all-cause death, myocardial infarction, or coronary revascularization, respectively. Among patients without CAC (n=1237), 89 patients (7%) had evidence of plaque on their coronary computed tomography angiography with a median low-attenuation noncalcified plaque burden of 4% (2-7). There were no significant differences in the negative predictive value for CAC=0 by sex, race, or ethnicity. Patients with ≥2 risk factors had higher odds of having plaque with zero CAC. CONCLUSIONS: In summary, no sex, race, or ethnicity differences were demonstrated in the negative predictive value of a zero CAC; however, patients with ≥2 risk factors had a higher prevalence of plaque. A small percentage (7%) of symptomatic patients undergoing coronary computed tomography angiography with zero CAC had noncalcified coronary plaque, with the implication that caution is needed for downscaling of preventive treatment in patients with zero CAC, chest pain, and multiple risk factors.

High-quality annotations for deep learning enabled plaque analysis in SCAPIS cardiac computed tomography angiography.

Background: Plaque analysis with coronary computed tomography angiography (CCTA) is a promising tool to identify high risk of future coronary events. The analysis process is time-consuming, and requires highly trained readers. Deep learning models have proved to excel at similar tasks, however, training these models requires large sets of expert-annotated training data. The aims of this study were to generate a large, high-quality annotated CCTA dataset derived from Swedish CArdioPulmonary BioImage Study (SCAPIS), report the reproducibility of the annotation core lab and describe the plaque characteristics and their association with established risk factors. Methods and results: The coronary artery tree was manually segmented using semi-automatic software by four primary and one senior secondary reader. A randomly selected sample of 469 subjects, all with coronary plaques and stratified for cardiovascular risk using the Systematic Coronary Risk Evaluation (SCORE), were analyzed. The reproducibility study (n = 78) showed an agreement for plaque detection of 0.91 (0.84-0.97). The mean percentage difference for plaque volumes was -0.6% the mean absolute percentage difference 19.4% (CV 13.7%, ICC 0.94). There was a positive correlation between SCORE and total plaque volume (rho = 0.30, p < 0.001) and total low attenuation plaque volume (rho = 0.29, p < 0.001). Conclusions: We have generated a CCTA dataset with high-quality plaque annotations showing good reproducibility and an expected correlation between plaque features and cardiovascular risk. The stratified data sampling has enriched high-risk plaques making the data well suited as training, validation and test data for a fully automatic analysis tool based on deep learning.

The value of quantitative plaque analysis based on coronary computed tomography angiography in predicting the percutaneous coronary intervention outcome of chronic total occlusion lesions.

Background: Due to the uncertainty of the success of percutaneous coronary intervention (PCI) and the complexity of selecting suitable treatment cases, the interventional outcome of coronary chronic total occlusion (CTO) remains challenging. The purpose of this study was to evaluate the role of quantitative plaque analysis based on coronary computed tomography angiography (CCTA) in predicting the CTO-PCI outcome. Methods: We retrospectively included 78 patients with CTO (80 lesions) confirmed by invasive coronary angiography from July 2016 to December 2018. All patients underwent PCI treatment according to standard practice. A total of 47 lesions in 47 patients were successfully treated with PCI. PCI failed in the remaining 33 lesions in 31 patients. The following conventional CCTA morphologic parameters were evaluated and compared between the PCI-success and PCI-failure groups: stump morphology; occlusion length, tortuous course; CTO lesion calcium; bridging collateral vessel; retrograde collateral vessel; the appearance of the occluded distal segment; and quantitative CTO plaque characteristics, including total plaque volume, calcified plaque (CP) volume, noncalcified plaque (NCP) volume, low-density noncalcified plaque (LDNCP) volume, and plaque length. Univariate and multivariate logistic regression analyses were performed to determine independent parameters predictive of CTO-PCI outcomes. The predictive performances were assessed using receiver operating characteristic curve analysis. Results: The blunt stump was the only independent CCTA morphologic parameter to predict the outcome of CTO-PCI [odds ratio (OR): 10.807; P<0.001]. NCP volume (OR: 1.018; P<0.001), CP volume (OR: 1.026; P=0.049), and plaque length (OR: 1.058; P=0.037) were independent quantitative CTO plaque characteristics predictive of CTO-PCI outcomes. The plaque-based model combining NCP volume with CP volume and plaque length had a higher area under the curve (AUC =0.96) than did the morphology-based model that included blunt stump (AUC 0.68) in predicting the outcomes of CTO-PCI (P<0.001). Conclusions: The CCTA-based plaque characteristics, including NCP volume, CP volume, and plaque length, outperformed morphologic parameters in predicting the CTO-PCI outcomes.

Plaque volume, composition, and fraction versus ischemia and outcomes in patients with coronary artery disease.

BACKGROUND: The various plaque components have been associated with ischemia and outcomes in patients with coronary artery disease (CAD). The main goal of this analysis was to test the hypothesis that, at patient level, the fraction of non-calcified plaque volume (PV) of total PV is associated with ischemia and outcomes in patients with CAD. This ratio could be a simple and clinically useful parameter, if predicting outcomes. METHODS: Consecutive patients with suspected CAD undergoing coronary computed tomography angiography with selective positron emission tomography perfusion imaging were selected. Plaque components were quantitatively analyzed at patient level. The fraction of various plaque components were expressed as percentage of total PV and examined among patients with non-obstructive CAD, suspected stenosis with normal perfusion, and those with reduced myocardial perfusion. Clinical outcomes included all-cause mortality and myocardial infarction. RESULTS: In total, 494 patients (age 63 â€‹± â€‹9 years, 55% male) were included. Total PV and all plaque components were significantly larger in patients with reduced myocardial perfusion compared to patients with normal perfusion and those with non-obstructive CAD. During follow-up 35 events occurred. Patients with any plaque component â€‹≥ â€‹median showed worse outcomes (log-rank p â€‹< â€‹0.001 for all). In addition, low-attenuation plaque â€‹≥ â€‹median was associated with worse outcomes independent of total PV (adjusted HR: 2.754, 95% CI: 1.022-7.0419, p â€‹= â€‹0.045). The fractions of the various plaque components were not associated with outcomes. CONCLUSION: Larger total PV or any plaque component at patient level are associated with abnormal myocardial perfusion and adverse events. The various plaque components as fraction of total PV lack additional prognostic value.

Sex differences in computed tomography angiography-derived coronary plaque burden in relation to invasive fractional flow reserve.

BACKGROUND: Distinct sex-related differences exist in coronary artery plaque burden and distribution. We aimed to explore sex differences in quantitative plaque burden by coronary CT angiography (CCTA) in relation to ischemia by invasive fractional flow reserve (FFR). METHODS: This post-hoc analysis of the PACIFIC trial included 581 vessels in 203 patients (mean age 58.1 â€‹± â€‹8.7 years, 63.5% male) who underwent CCTA and per-vessel invasive FFR. Quantitative assessment of total, calcified, non-calcified, and low-density non-calcified plaque burden were performed using semiautomated software. Significant ischemia was defined as invasive FFR ≤0.8. RESULTS: The per-vessel frequency of ischemia was higher in men than women (33.5% vs. 7.5%, p â€‹< â€‹0.001). Women had a smaller burden of all plaque subtypes (all p â€‹< â€‹0.01). There was no sex difference on total, calcified, or non-calcified plaque burdens in vessels with ischemia; only low-density non-calcified plaque burden was significantly lower in women (beta: -0.183, p â€‹= â€‹0.035). The burdens of all plaque subtypes were independently associated with ischemia in both men and women (For total plaque burden (5% increase): Men, OR: 1.15, 95%CI: 1.06-1.24, p â€‹= â€‹0.001; Women, OR: 1.96, 95%CI: 1.11-3.46, p â€‹= â€‹0.02). No significant interaction existed between sex and total plaque burden for predicting ischemia (interaction p â€‹= â€‹0.108). The addition of quantitative plaque burdens to stenosis severity and adverse plaque characteristics improved the discrimination of ischemia in both men and women. CONCLUSIONS: In symptomatic patients with suspected CAD, women have a lower CCTA-derived burden of all plaque subtypes compared to men. Quantitative plaque burden provides independent and incremental predictive value for ischemia, irrespective of sex.

Coronary plaque characterization assessed by delayed enhancement dual-layer spectral CT angiography and optical coherence tomography.

This study aimed to prospectively evaluate delayed enhancement imaging by spectral computed tomography using soluble iodine containing contrast media to improve the in vivo characterization of coronary plaque types based on the quantification of delayed iodine enhancement. Patients with known or suspected coronary artery disease (CAD) underwent spectral coronary CT-angiography (SCCTA). Absolute delayed iodine enhancement in all visible coronary plaques was assessed. Patients with significant CAD (> 50% stenosis) further underwent invasive coronary angiography (ICA) including optical coherence tomography (OCT). We identified 50 non-calcified coronary plaques in 72 patients undergoing SCCTA. 17 patients with significant CAD underwent further ICA including OCT imaging. In those, we were able to match 35 plaques by both SCCTA and OCT. Based on OCT imaging, 22/35 matched plaques (63%) were characterized as high-risk coronary plaques (thin-cap fibroatheroma n = 2, fibroatheroma n = 20), whereas 13/35 (37%) were characterized as low-risk plaques (fibrocalcific lesion n = 3, fibrous plaques n = 9, and early-onset fibroatheroma n = 1). All plaques showed similar HU's and could not be classified into high-risk or low-risk plaques by conventional CT measures. Minimal delayed iodine enhancement within plaques as quantified by SCCTA demonstrated significantly lower values in high-risk as compared to low-risk coronary plaques (1.0 ± 1.5 mg/ml vs. 2.2 ± 1.1 mg/ml, p = 0.021) which allowed estimation of high-risk plaques with high sensitivity and moderate specificity (77% and 56%). Measurement of delayed enhancement iodine uptake within stable coronary artery plaques using dual-layer SCCTA might contribute to a more precise estimation of plaque vulnerability surpassing conventional CT techniques.

Plaque Burden and 1-Year Outcomes in Acute Chest Pain: Results From the Multicenter RAPID-CTCA Trial.

BACKGROUND: In patients with stable chest pain, computed tomography (CT) plaque burden is an independent predictor of future coronary events. OBJECTIVES: The purpose of this study was to determine whether plaque burden and characteristics can predict subsequent death or myocardial infarction in patients with acute chest pain. METHODS: In a post hoc analysis of a multicenter trial of early coronary CT angiography, the authors performed quantitative plaque analysis to assess the association between primary endpoint of 1-year all-cause death or nonfatal myocardial infarction and the GRACE (Global Registry of Acute Coronary Events) score, presence of obstructive coronary artery disease, and plaque burden in 404 patients with suspected acute coronary syndrome. RESULTS: Following the index event, 25 patients had a primary event that was associated with a higher GRACE score (134 ± 44 vs 113 ± 35; P = 0.012), larger burdens of total (46% [IQR: 43%-50%] vs 36% [IQR: 21%-46%]; P < 0.001), noncalcified (41% [IQR: 37%-%47] vs 33% [IQR: 20%-41%]; P < 0.001), and low-attenuation plaque (4.22% [IQR: 3.3%-5.68%] vs 2.14% [IQR: 0.5%-4.88%]; P < 0.001), but not obstructive coronary artery disease (P = 0.065). Total, noncalcified, and low-attenuation plaque burden were the strongest predictors of future events independent of GRACE score and obstructive coronary artery disease (P ≤ 0.002 for all). Patients with a low-attenuation burden above the median had nearly an 8-fold increased risk of the primary endpoint (HR: 7.80 [95% CI: 2.33-26.0]; P < 0.001), outperforming either a GRACE score of >140 (HR: 3.80 [95% CI :1.45-6.98]; P = 0.004) or obstructive coronary artery disease (HR: 2.07 [95% CI: 0.94-4.53]; P = 0.07). CONCLUSIONS: In patients with suspected acute coronary syndrome, low-attenuation plaque burden is a major predictor of 1-year death or recurrent myocardial infarction. (Rapid Assessment of Potential Ischaemic Heart Disease With CTCA [RAPID-CTCA]; NCT02284191).

Association between coronary plaque volume and myocardial ischemia detected by dynamic perfusion CT imaging.

Introduction: We aimed to evaluate the relationship between quantitative plaque metrics derived from coronary CT angiography (CTA) and segmental myocardial ischemia using dynamic perfusion CT (DPCT). Methods: In a prospective single-center study, patients with > 30% stenosis on rest CTA underwent regadenoson stress DPCT. 480 myocardium segments of 30 patients were analyzed. Quantitative plaque assessment included total plaque volume (PV), area stenosis, and remodeling index (RI). High-risk plaque (HRP) was defined as low-attenuation plaque burden > 4% or RI > 1.1. Absolute myocardial blood flow (MBF) and relative MBF (MBFi: MBF/75th percentile of all MBF values) were quantified. Linear and logistic mixed models correcting for intra-patient clustering and clinical factors were used to evaluate the association between total PV, area stenosis, HRP and MBF or myocardial ischemia (MBF < 101 ml/100 g/min). Results: Median MBF and MBFi were 111 ml/100 g/min and 0.94, respectively. The number of ischemic segments were 164/480 (34.2%). Total PV of all feeding vessels of a given myocardial territory differed significantly between ischemic and non-ischemic myocardial segments (p = 0.001). Area stenosis and HRP features were not linked to MBF or MBFi (all p > 0.05). Increase in PV led to reduced MBF and MBFi after adjusting for risk factors including hypertension, diabetes, and statin use (per 10 mm3; ß = -0.035, p < 0.01 for MBF; ß = -0.0002, p < 0.01 for MBFi). Similarly, using multivariate logistic regression total PV was associated with ischemia (OR = 1.01, p = 0.033; per 10 mm3) after adjustments for clinical risk factors, area stenosis and HRP. Conclusion: Total PV was independently associated with myocardial ischemia based on MBF, while area stenosis and HRP were not.

Editor's Choice - Eligibility of Common Femoral Artery Atherosclerotic Disease for Endovascular Treatment - the CONFESS Study.

OBJECTIVE: Advances in endovascular technologies have allowed the treatment of common femoral artery (CFA) steno-occlusive disease by minimally invasive means; however, the proportion of lesions treated with common femoral artery endarterectomy (CFAE) which would be amenable to endovascular treatment is unknown. This observational study aimed to describe the morphology and composition of CFA lesions treated with CFAE and report the proportion that would be amenable to endovascular treatment with modern technologies. METHODS: Patients presenting with symptomatic peripheral artery disease who underwent CFAE from January 2014 to December 2018 in two tertiary NHS hospitals were included. Extensive data relating to patient demographics, risk factors, clinical outcomes, as well as anatomical and morphological characteristics of the CFA atherosclerotic lesions, were collected which included detailed plaque analysis using 3D reconstruction of pre-operative computed tomography angiograms. CFA lesions were considered suitable for endovascular treatment if presented with patent iliac inflow, at least one patent outflow vessel (superficial femoral artery [SFA] or profunda femoral artery [PFA]), and stenotic rather than occluded CFA. RESULTS: A total of 829 CFAs in 737 consecutive patients who underwent CFAE were included (mean age 71 ± 10 years; 526 males, 71%); 451 (62%) presented with chronic limb threatening ischaemia. Overall, 35% of CFAs had a localised lesion (no bifurcation disease) that could possibly be treated endovascularly. In total, 376 (45%) target vessels did not feature severe calcium load, with a patent CFA, PFA, and proximal SFA and therefore would have been amenable to endovascular treatment; while 271 CFAs (33%) featured a significant calcium load which would have potentially required stenting. CONCLUSION: A significant proportion of patients with atherosclerotic CFA lesions who undergo surgery could potentially be candidates for endovascular treatment. A randomised trial comparing CFAE and new endovascular techniques in this clinical context is required.

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

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

Whole-Blood Transcriptional Profiles Enable Early Prediction of the Presence of Coronary Atherosclerosis and High-Risk Plaque Features at Coronary CT Angiography.

Existing tools to estimate cardiovascular (CV) risk have sub-optimal predictive capacities. In this setting, non-invasive imaging techniques and omics biomarkers could improve risk-prediction models for CV events. This study aimed to identify gene expression patterns in whole blood that could differentiate patients with severe coronary atherosclerosis from subjects with a complete absence of detectable coronary artery disease and to assess associations of gene expression patterns with plaque features in coronary CT angiography (CCTA). Patients undergoing CCTA for suspected coronary artery disease (CAD) were enrolled. Coronary stenosis was quantified and CCTA plaque features were assessed. The whole-blood transcriptome was analyzed with RNA sequencing. We detected highly significant differences in the circulating transcriptome between patients with high-degree coronary stenosis (≥70%) in the CCTA and subjects with an absence of coronary plaque. Notably, regression analysis revealed expression signatures associated with the Leaman score, the segment involved score, the segment stenosis score, and plaque volume with density <150 HU at CCTA. This pilot study shows that patients with significant coronary stenosis are characterized by whole-blood transcriptome profiles that may discriminate them from patients without CAD. Furthermore, our results suggest that whole-blood transcriptional profiles may predict plaque characteristics.

Machine Learning with 18F-Sodium Fluoride PET and Quantitative Plaque Analysis on CT Angiography for the Future Risk of Myocardial Infarction.

Coronary 18F-sodium fluoride (18F-NaF) PET and CT angiography-based quantitative plaque analysis have shown promise in refining risk stratification in patients with coronary artery disease. We combined both of these novel imaging approaches to develop an optimal machine-learning model for the future risk of myocardial infarction in patients with stable coronary disease. Methods: Patients with known coronary artery disease underwent coronary 18F-NaF PET and CT angiography on a hybrid PET/CT scanner. Machine-learning by extreme gradient boosting was trained using clinical data, CT quantitative plaque analysis, measures and 18F-NaF PET, and it was tested using repeated 10-fold hold-out testing. Results: Among 293 study participants (65 ± 9 y; 84% male), 22 subjects experienced a myocardial infarction over the 53 (40-59) months of follow-up. On univariable receiver-operator-curve analysis, only 18F-NaF coronary uptake emerged as a predictor of myocardial infarction (c-statistic 0.76, 95% CI 0.68-0.83). When incorporated into machine-learning models, clinical characteristics showed limited predictive performance (c-statistic 0.64, 95% CI 0.53-0.76) and were outperformed by a quantitative plaque analysis-based machine-learning model (c-statistic 0.72, 95% CI 0.60-0.84). After inclusion of all available data (clinical, quantitative plaque and 18F-NaF PET), we achieved a substantial improvement (P = 0.008 versus 18F-NaF PET alone) in the model performance (c-statistic 0.85, 95% CI 0.79-0.91). Conclusion: Both 18F-NaF uptake and quantitative plaque analysis measures are additive and strong predictors of outcome in patients with established coronary artery disease. Optimal risk stratification can be achieved by combining clinical data with these approaches in a machine-learning model.