Non-calcified coronary atherosclerotic plaque visualization on CT: effects of contrast-enhancement and lipid-content fractions

Burden of coronary artery calcification in ANCA-associated vasculitis

BACKGROUND

Cardiovascular disease (CVD) is a leading cause of death in ANCA-associated vasculitis (AAV). Screening and primary cardiovascular prevention may improve outcomes.

METHODS

We identified patients in the 2002-2019 Mass General Brigham AAV cohort with thoracic CT scans obtained for other clinical purposes. Coronary artery calcium (CAC) scores and age, sex and race-standardised CAC percentiles were calculated. Quantile regression was used to identify differences by ANCA type, and Gray's test examined differences in major adverse cardiac events by CAC score.

RESULTS

Of 175 included patients, 127 (73%) were MPO-ANCA+and 48 (27%) were PR3-ANCA+. The median CAC score was 17 (IQR 0, 334) and CAC percentile was 45 (IQR 0, 78); 65 (39%) patients had CAC of ≥100. The total CAC score was higher in patients with MPO-ANCA+AAV vs PR3-ANCA+AAV (median 24 vs 1, p=0.003), as was the standardised CAC percentile (50th vs 34th, p=0.02). Of 116 (66%) patients with non-zero CAC scores, only 29 (25%) were on a statin. In a time-to-event analysis, CAC of 100 or higher trended towards association with higher risk of major adverse cardiovascular events (χ2=1.9, p=0.16).

CONCLUSION

A majority of patients with AAV had clinically significant CAC. There were differences in CAC burden among those with MPO-ANCA+AAV versus PR3-ANCA+AAV. Although CAC is associated with CVD risk and an indication for statins, the use was inconsistent. The role of CT imaging to screen for CVD and guide primary prevention in AAV requires further study.

The biomedical knowledge graph of symptom phenotype in coronary artery plaque: machine learning-based analysis of real-world clinical data

A knowledge graph can effectively showcase the essential characteristics of data and is increasingly emerging as a significant means of integrating information in the field of artificial intelligence. Coronary artery plaque represents a significant etiology of cardiovascular events, posing a diagnostic challenge for clinicians who are confronted with a multitude of nonspecific symptoms. To visualize the hierarchical relationship network graph of the molecular mechanisms underlying plaque properties and symptom phenotypes, patient symptomatology was extracted from electronic health record data from real-world clinical settings. Phenotypic networks were constructed utilizing clinical data and protein‒protein interaction networks. Machine learning techniques, including convolutional neural networks, Dijkstra's algorithm, and gene ontology semantic similarity, were employed to quantify clinical and biological features within the network. The resulting features were then utilized to train a K-nearest neighbor model, yielding 23 symptoms, 41 association rules, and 61 hub genes across the three types of plaques studied, achieving an area under the curve of 92.5%. Weighted correlation network analysis and pathway enrichment were subsequently utilized to identify lipid status-related genes and inflammation-associated pathways that could help explain the differences in plaque properties. To confirm the validity of the network graph model, we conducted coexpression analysis of the hub genes to evaluate their potential diagnostic value. Additionally, we investigated immune cell infiltration, examined the correlations between hub genes and immune cells, and validated the reliability of the identified biological pathways. By integrating clinical data and molecular network information, this biomedical knowledge graph model effectively elucidated the potential molecular mechanisms that collude symptoms, diseases, and molecules.

A study of the relationship between brachial artery vasodilation and platelet/lymphocyte ratio in diabetic patients with coronary atherosclerosis

BACKGROUND

To investigate the correlation between brachial artery flow-mediated endothelium-dependent dilation (FMD) and platelet-lymphocyte ratio (PLR) in peripheral blood and coronary atherosclerosis in diabetic patients.

METHODS

Seventy-five diabetic patients aged 62 ± 9 years, 68% male and 32% female, who underwent brachial artery endothelial function test and coronary CT scan were collected. Coronary artery calcification (CAC) was observed to assess the presence of coronary atherosclerosis, and high-resolution extravascular ultrasound was used to detect FMD. Platelet count and lymphocyte count were recorded by routine blood tests, and PLR was calculated for each study subject. Statistical methods were used to verify the association of FMD and PLR with CAC assessed by CT, respectively.

RESULTS

Patients with coronary atherosclerosis had decreased FMD and increased PLR compared with patients with normal coronary arteries. Univariate logistic regression analysis showed that CAC score was significantly associated with both FMD (odds ratio: 0.167; 95% confidence interval: 0.049-0.565; p = 0.002) and PLR (odds ratio: 0.127; 95% confidence interval: 0.033-0.484; p = 0.001) at FMD < 5.1% or PLR > 130. The area under the ROC curve of FMD and PLR alone was 0.760 and 0.763, respectively. In addition, combined diagnosis of FMD and PLR showed the highest area under the ROC curve (0.830).

CONCLUSION

FMD combined with PLR is expected to be a precise diagnostic modality for coronary artery calcification in diabetic patients.

Noninvasive Discrimination of Coronary Chronic Total Occlusion and Subtotal Occlusion by Coronary Computed Tomography Angiography

OBJECTIVES

The aim of this study was to investigate whether noninvasive discrimination of chronic total occlusion (CTO), a complete interruption of coronary artery flow, and subtotal occlusion (STO), a functional total occlusion, is feasible using coronary computed tomography angiography (CTA).

BACKGROUND

CTO and STO may be different in pathophysiology and clinical treatment strategy.

METHODS

We included 486 consecutive patients (median age 63 years, 82% male) who showed a total of 553 completely occluded coronary arteries in coronary CTA. The length of occlusion, side branches, shape of proximal stump, and collateral vessels were measured as anatomical findings. Transluminal attenuation gradient, which reflects intraluminal contrast kinetics and functional extent of collateral flow, was measured as a physiological surrogate. All patients were followed by invasive coronary angiography.

RESULTS

Coronary arteries with CTO showed longer occlusion length (cutoff ≥ 15 mm), higher distal transluminal attenuation gradient (cutoff ≥-0.9 Hounsfield units [HU]/10 mm), more frequent side branches, blunted stump, cross-sectional calcification ≥ 50%, and collateral vessels compared with arteries with STO (p < 0.001, all). The combination of these findings could distinguish CTO from STO (c-statistics = 0.88 [95% confidence interval: 0.94 to 0.90], sensitivity 83%, specificity 77%, positive predictive value 55%, negative predictive value 93%; p < 0.001). Percutaneous coronary intervention (PCI) was attempted in 342 arteries and was successful in 279 arteries (82%). The computed tomography findings could predict the unsuccessful PCI (c-statistics = 0.70 [95% confidence interval: 0.65 to 0.75], sensitivity 63%, specificity 73%, positive predictive value 91%, negative predictive value 31%; p < 0.001).

CONCLUSIONS

Noninvasive coronary CTA could discern CTO from STO, and also could predict the success of attempted PCI.

The association of hemoglobin A1c and high risk plaque and plaque extent assessed by coronary computed tomography angiography

The objective of this study was to investigate the relationship of Hemoglobin A1c (HbA1c) and plaque characteristics including high risk plaque and plaque extent. We retrospectively examined 1079 consecutive coronary computed tomography (CT) angiography scans and the HbA1c results. We divided the patients into four groups by the HbA1c status: non-diabetic, ≤6.0; borderline, 6.1-6.4; diabetic low, 6.5-7.1; diabetic high, >7.1. We determined segment involvement score >4 as extensive disease. High risk plaque was defined as two feature positive (FP) plaque which consists of positive remodeling (remodeling index >1.1) and low attenuation (<30 HU). Univariate and multivariate analysis including conventional cardiovascular risk factors, symptoms and medication was performed. Univariate analysis showed that diabetic patients as well as borderline patients were significantly related with 2FP plaque and extensive disease. Although the relationship of borderline patients and 2FP plaque was marginal in multivariate analysis [odds ratio (OR) 1.53, 95% confidence interval (CI) 0.95-2.40, p = 0.07], the elevation of HbA1c was strongly associated with 2FP plaque (diabetic low, OR 2.19, 95% CI 1.37-3.45, p < 0.005; diabetic high, OR 4.14, 95% CI 2.57-6.67, p < 0.0005). The association of HbA1c elevation and extensive disease was quite similar between borderline and diabetic patients (borderline, OR 1.96, 95% CI 1.29-2.95, p < 0.005; diabetic low, OR 1.94, 95% CI 1.25-3.01, p < 0.005; diabetic high, OR 2.19, 95% CI 1.39-3.43, p < 0.005). Patients with elevated HbA1c of >6.0 are potentially at risk for future cardiovascular events due to increased high risk plaque and extensive disease, even below the diabetic level of 6.5. Coronary CT could be used for risk stratification of these patients.

Coronary plaque characterization using CT

OBJECTIVE. In this article, we review the histopathologic classification of coronary atherosclerotic plaques and describe the possibilities and limitations of CT regarding the evaluation of coronary artery plaques. CONCLUSION. The composition of atherosclerotic plaques in the coronary arteries displays substantial variability and is associated with the likelihood for rupture and downstream ischemic events. Accurate identification and quantification of coronary plaque components on CT is challenging because of the limited temporal, spatial, and contrast resolutions of current scanners. Nonetheless, CT may provide valuable information that has potential for characterization of coronary plaques. For example, the extent of calcification can be determined, lipid-rich lesions can be separated from more fibrous ones, and positive remodeling can be identified.