Noninvasive Assessment of Stenotic Severity and Plaque Characteristics by Coronary CT Angiography in Patients Scheduled for Carotid Artery Revascularization

Association Between Carotid Plaque Characteristics and High-risk Coronary Plaque: A Virtual Histology Intravascular Ultrasound Study

This retrospective study aimed to investigate the association between carotid artery plaque characteristics evaluated by virtual histology intravascular ultrasound (VH-IVUS) and high-risk coronary plaques determined by coronary computed tomography angiography (CCTA) among patients (n = 72) who underwent carotid artery stenting (CAS). VH-IVUS examination was performed during CAS and CCTA examination before CAS. Logistic regression models were used to explore the association between carotid plaque characteristics and high-risk coronary plaque. Of 72 patients, 46 (63.9%) were diagnosed as having high-risk coronary plaques. The necrotic core (NC) area, NC percentage, dense calcium (DC) area, and symptomatic carotid artery stenosis were significantly associated with high-risk coronary plaques (all P < .05). Isolated upper-NC area, and upper-NC area combined with upper-DC area were associated with high-risk coronary plaques. Compared with the lower-NC area and lower-DC area groups, participants with upper-NC area and upper-NC area had 12.53 times higher odds ratio of having high-risk coronary plaques. This study showed that the NC area, NC percentage, DC area and symptomatic carotid artery stenosis were associated with high-risk coronary plaques. Participants with both upper-NC area and upper-DC area might have a higher risk of high-risk coronary plaques.

Diagnostic performance of coronary computed tomography angiography stenosis score for coronary stenosis

BACKGROUND

Coronary computed tomography angiography stenosis score (CCTA-SS) is a proposed diagnosis score that considers the plaque characteristics, myocardial function, and the diameter reduction rate of the lesions. This study aimed to evaluate the diagnostic performance of the CCTA-SS in seeking coronary artery disease (CAD).

METHODS

The 228 patients with suspected CAD who underwent CCTA and invasive coronary angiography (ICA) procedures were under examination. The diagnostic performance was evaluated with the receiver operating curve (ROC) for CCTA-SS in detecting CAD (defined as a diameter reduction of ≥ 50%) and severe CAD (defined as a diameter reduction of ≥ 70%).

RESULTS

The area under ROC (AUC) of CCTA-SS was 0.909 (95% CI: 0.864-0.943), which was significantly higher than that of CCTA (AUC: 0.826; 95% CI: 0.771-0.873; P = 0.0352) in diagnosing of CAD with a threshold of 50%. The optimal cutoff point of CCTA-SS was 51% with a sensitivity of 90.66%, specificity of 95.65%, positive predictive value of 98.80%, negative predictive value of 72.13%, and accuracy of 91.67%, whereas the optimal cutoff point of CCTA was 55%, and the corresponding values were 87.36%, 93.48%, 98.15%, 65.15%, and 88.60%, respectively. With a threshold of 70%, the performance of CCTA-SS with an AUC of 0.927 (95% CI: 0.885-0.957) was significantly higher than that of CCTA with an AUC of 0.521 (95% CI: 0.454-0.587) (P < 0.0001).

CONCLUSIONS

CCTA-SS significantly improved the diagnostic accuracy of coronary stenosis, including CAD and severe CAD, compared with CCTA.

Comparison of the Clinical Outcome of Carotid Artery Stenting Between Institutions With a Treatment Strategy Based on Risk Factors and Those With First-Line Treatment

PURPOSE

Carotid endarterectomy (CEA) and carotid artery stenting (CAS) are recommended based on certain risk factors. The volume of an institution's treatment experience may be associated with good clinical outcomes. There is a dilemma between the treatment strategy based on risk factors and the experience volume. Therefore, we investigated the clinical outcomes of CAS performed at institutions that selected the treatment strategy based on risk factors and those that performed CAS at the first-line treatment.

MATERIALS AND METHODS

Patients who underwent CAS at 5 institutions were included in this retrospective case-control study. We defined CEA/CAS institutions as those that selected the treatment option based on risk factors, and CAS-first institutions as those that performed CAS as the first-line treatment. We investigated cases of ischemic stroke, hemorrhagic stroke, myocardial infarction, and deaths within 30 days of the intervention between the CEA/CAS- and CAS-first institution groups. One-to-one propensity score matching was performed to compare rates of ischemic and hemorrhagic strokes within 30 days of the intervention.

RESULTS

A total of 239 and 302 patients underwent CAS at the CEA/CAS institutions and CAS-first institutions, respectively; ischemic stroke occurred in 12 (5.0%) and 7 patients (2.3%), respectively (p=0.09). No differences in major ischemic strokes (0.8% vs 1.3%; p=0.59), hemorrhagic strokes (0.4% vs 0.3%; p=0.87), or deaths (0.0% vs 0.7%; p=0.21) were observed. Myocardial infarction did not occur in either group. Propensity score analysis showed that ischemic stroke (odds ratio: 1.845, 95% confidence interval: 0.601-5.668, p=0.28) and hemorrhagic stroke (odds ratio: 1.000, 95% confidence interval: 0.0061-16.418, p=1.00) were not significantly associated with either institution group.

CONCLUSIONS

The CAS-specific treatment strategies for CAS can achieve the same level of outcomes as the treatment strategy based on risk factors. The CAS performed based on risk factors in CEA/CAS institutions and the treatment of more than 30 patients/year/institution in CAS-first institutions were associated with good clinical outcomes.

Predicting coronary artery calcified plaques using perivascular fat CT radiomics features and clinical risk factors

OBJECTIVE

The purpose of this study was to develop a combined radiomics model to predict coronary plaque texture using perivascular fat CT radiomics features combined with clinical risk factors.

METHODS

The data of 200 patients with coronary plaques were retrospectively analyzed and randomly divided into a training group and a validation group at a ratio of 7:3. In the training group, The best feature set was selected by using the maximum correlation minimum redundancy method and the least absolute shrinkage and selection operator. Radiomics models were built based on different machine learning algorithms. The clinical risk factors were then screened using univariate logistic regression analysis. and finally a combined radiomics model was developed using multivariate logistic regression analysis to combine the best performing radiomics model with clinical risk factors and validated in the validation group. The efficacy of the model was assessed by a receiver operating characteristic curve, the consistency of the nomogram was assessed using calibration curves, and the clinical usefulness of the nomogram was assessed using decision curve analysis.

RESULTS

Twelve radiomics features were used by different machine learning algorithms to construct the radiomics model. Finally, the random forest algorithm built the best radiomics model in terms of efficacy, and this was combined with age to construct a combined radiomics model. The area under curve for the training and validation group were 0.98 (95% confidence interval, 0.95-1.00) and 0.97 (95% confidence interval, 0.92-1.00) with sensitivities of 0.92 and 0.86 and specificities of 0.99 and 1, respectively. The calibration curve demonstrated that the nomogram had good consistency, and the decision curve analysis demonstrated that the nomogram had high clinical utility.

CONCLUSIONS

The combined radiomics model established based on CT radiomics features and clinical risk factors has high value in predicting coronary artery calcified plaque and can provide a reference for clinical decision-making.

Identification of Biomarkers of Autophagy-Related Genes Between Early and Advanced Carotid Atherosclerosis

Background

Accumulating evidence demonstrates that autophagy is important in inhibiting inflammation and cholesterol efflux. It suggested the autophagy may be a treatment of atherosclerosis. Thus, we screened autophagy-related mRNA to explore their mechanism of scientific basis for early diagnosis and therapy of atherosclerosis.

Methods

The GSE28829 datasets were assessed to analyze differentially expressed genes by GEO2R. And autophagy-related hub genes were identified by HADb. The biological function of autophagy-related DEmRNAs was examined by Metascape. The construction of a protein–protein network was explored by String. Cytohubba was utilized to screen hub genes. Analysis of DEmiRNA-mRNA pairs was executed by DIANA microT-CDS database. Finally, correlation analysis was carried out to identify the relationship between DEARGs and clinical and prognostic factors.

Results

A number of 1087 DEGs and 19 autophagy-related DEmRNAs were identified in advanced carotid atherosclerotic plaque compared with the early. The biological function containing development and growth was enriched. Moreover, we screened the top hub nodes with the highest degrees. MicroRNAs (miRNAs) are confirmed to participate in genesis and progression of atherosclerosis, so we further analyzed the miRNA–mRNA regulatory network genes with four hub genes to explore their potential mechanism in atherosclerosis. Then, we revealed co-expression of four key genes CTSB, ITGB1, CXCR4, TNFSF10 and autophagy-related genes. As for the clinical factors, hypertension factor showed higher expression of ITGB1. The probability of coronary heart disease factor was significantly increased with high expression of CTSB and CXCR4, as well as low expression of ITGB1 and TNFSF10. Diabetes factor tended to express distinguished levels of CTSB and ITGB1. TNFSF10 was highly expressed in both hyperlipidemia and ischemic stroke factor.

Conclusion

CTSB, ITGB1, CXCR4 and TNFSF10 may be critical in atherosclerosis development and were thought to be potential diagnostic biomarkers for atherosclerosis.

Relationship between epicardial adipose tissue and coronary artery stenoses on computed tomography in patients scheduled for carotid artery revascularization

BACKGROUND

We aimed to clarify the relationship between epicardial adipose tissue (EAT) volume and the presence of severe stenoses (SS) on coronary computed tomography angiography (CTA) for risk stratification of the patients with carotid artery stenoses.

METHODS

We prospectively performed CTA for 125 consecutive patients (72.4 ± 8.1 years, 85% men) without a history of coronary artery disease (CAD), who were scheduled for carotid artery revascularization from 2014 to 2020. SS was defined as ≥70% luminal stenosis on CTA. EAT was quantified automatically as the total volume of tissue with -190 to -30 HU.

RESULTS

Of 125 patients, 76 had SS. Between the patients with and without SS, there were significant differences in coronary artery calcium score (CACS), left ventricular ejection fraction (LVEF), dyslipidemia, and EAT, despite no differences in carotid echocardiography findings. After adjustment for age, gender, and dyslipidemia, EAT was an independent factor associated with SS (p=0.011), as well as CACS and LVEF. The addition of EAT to a baseline model including age, gender, dyslipidemia, LVEF, and CACS achieved both net reclassification improvement (0.505, p=0.003) and integrated discrimination improvement (0.059, p=0.003).

CONCLUSIONS

In patients with carotid stenoses, EAT is associated with CAD and is useful for additional risk stratification. Epicardial fat may have a specific role in the development of CAD in patients with suspected systemic atherosclerosis.

Survival and Prognostic Factors After Carotid Artery Stenting in Patients with Concomitant Coronary Disease

AIM: The aim of the study was to analyze the prognostic factors in patients after carotid artery stenting (CAS). METHODS: In 329 patients after CAS, the median survival (MS) and overall survival (OS) were calculated for a follow-up period of 2–101 months. All patients underwent coronary angiography before carotid stenting and, if indicated, coronary revascularization. Four groups of factors were analyzed: Carotid disease, coronary artery disease (CAD), underlying cardiac pathology, and concomitant diseases. RESULTS: MS in all patients was 86 months, OS at 1, 3, 5, and 9 years was - 94%, 85%, 73%, and 51%, respectively. Event free survival was 85 months. Log Rank-Mantel-Cox analysis demonstrated significantly reduced MS in 21 tested factors, most of them related to CAD. Two-step multifactorial Cox regression analysis defined only 7 of them as independent prognostic factors for the survival of patients after CAS: Left main stenosis, complete revascularization, late myocardial infarction, stroke, age over 70 years, valvular disease, and carotid score. CONCLUSION: Survival of patients after CAS is limited mainly by CAD and underlying cardiac pathology. Staged revascularization treatment strategy may improve the prognosis and survival of patients with both carotid and coronary disease.

Correlation Between Calcification Characteristics of Carotid Atherosclerotic Plaque and Plaque Vulnerability

Purpose

To investigate the relationship between calcification characteristics of carotid atherosclerotic plaque and lipid rich necrotic core (LRNC) and intraplaque hemorrhage (IPH).

Methods

Patients with severe carotid stenosis undergoing carotid endarterectomy (CEA) were selected. Ultrasound and CT angiography (CTA) were performed to evaluate the calcification characteristics of the plaque before the surgery.

Results

A total of 142 patients were included and 142 pathological specimens of postoperative plaque were obtained accordingly. There were 78 plaques (54.9%) with LRNC and 41 (28.9%) with IPH. The plaque with LRNC had higher calcification rate (93.6%) compared with the plaque with IPH (87.8%). LRNC was often found in multiple calcification (P = 0.003) and mixed type calcification (P = 0.001). Multiple calcification was more likely to combine with IPH (P = 0.008), while simple basal calcification was not likely to combine IPH (P = 0.002). Smaller granular calcification was more likely to be associated with IPH (P < 0.05). In multivariate regression analysis of IPH and calcification characteristics, simple basal calcification was still a protective factor for IPH (OR, 0.25; 95% CI, 0.09–0.66; P = 0.005), while multiple calcification was closely related to the occurrence of IPH (OR, 3.58; 95% CI, 1.49–8.61; P = 0.004).

Conclusion

Calcification characteristics of carotid atherosclerotic plaques are closely related to the vulnerability of plaques, especially multiple calcification and mixed type calcification.