Coronary plaque characteristics on baseline CT predict the need for late revascularization in symptomatic patients after percutaneous intervention

Matrix metallopeptidase 9 contributes to the beginning of plaque and is a potential biomarker for the early identification of atherosclerosis in asymptomatic patients with diabetes

Aims

To determine the roles of matrix metallopeptidase-9 (MMP9) on human coronary artery smooth muscle cells (HCASMCs) in vitro, early beginning of atherosclerosis in vivo in diabetic mice, and drug naïve patients with diabetes.

Methods

Active human MMP9 (act-hMMP9) was added to HCASMCs and the expressions of MCP-1, ICAM-1, and VCAM-1 were measured. Act-hMMP9 (n=16) or placebo (n=15) was administered to diabetic KK.Cg-Ay/J (KK) mice. Carotid artery inflammation and atherosclerosis measurements were made at 2 and 10 weeks after treatment. An observational study of newly diagnosed drug naïve patients with type 2 diabetes mellitus (T2DM n=234) and healthy matched controls (n=41) was performed and patients had ultrasound of carotid arteries and some had coronary computed tomography angiogram for the assessment of atherosclerosis. Serum MMP9 was measured and its correlation with carotid artery or coronary artery plaques was determined.

Results

In vitro, act-hMMP9 increased gene and protein expressions of MCP-1, ICAM-1, VCAM-1, and enhanced macrophage adhesion. Exogenous act-hMMP9 increased inflammation and initiated atherosclerosis in KK mice at 2 and 10 weeks: increased vessel wall thickness, lipid accumulation, and Galectin-3+ macrophage infiltration into the carotid arteries. In newly diagnosed T2DM patients, serum MMP9 correlated with carotid artery plaque size with a possible threshold cutoff point. In addition, serum MMP9 correlated with number of mixed plaques and grade of lumen stenosis in coronary arteries of patients with drug naïve T2DM.

Conclusion

MMP9 may contribute to the initiation of atherosclerosis and may be a potential biomarker for the early identification of atherosclerosis in patients with diabetes.

Clinical trial registration

https://clinicaltrials.gov, identifier NCT04424706.

Prediction of microvascular complications in diabetic patients without obstructive coronary stenosis based on peri-coronary adipose tissue attenuation model

OBJECTIVES

To investigate the predictive value of peri-coronary adipose tissue (PCAT) attenuation for microvascular complications in diabetic patients without significant stenosis and to develop a prediction model for early risk stratification.

METHODS

This study retrospectively included patients clinically identified for coronary computed tomography angiography (CCTA) and type 2 diabetes between January 2017 and December 2020. All patients were followed up for at least 1 year. The clinical data and CCTA-based imaging characteristics (including PCAT of major epicardial vessels, high-risk plaque features) were recorded. In the training cohort comprising of 579 patients, two models were developed: model 1 with the inclusion of clinical factors and model 2 incorporating clinical factors + RCA_PCAT using multivariable logistic regression analysis. An internal validation cohort comprising 249 patients and an independent external validation cohort of 269 patients were used to validate the proposed models.

RESULTS

Microvascular complications occurred in 69.1% (758/1097) of the current cohort during follow-up. In the training cohort, model 2 exhibited improved predictive power over model 1 based on clinical factors (AUC = 0.820 versus 0.781, p = 0.003) with lower prediction error (Brier score = 0.146 versus 0.164) compared to model 1. Model 2 accurately categorized 78.58% of patients with diabetic microvascular complications. Similar performance of model 2 in the internal validation cohort and the external validation cohort was further confirmed.

CONCLUSIONS

The model incorporating clinical factors and RCA_PCAT predicts the development of microvascular complications in diabetic patients without significant coronary stenosis.

KEY POINTS

• Hypertension, HbA1c, duration of diabetes, and RCA_PCAT were independent risk factors for microvascular complications. • The prediction model integrating RCA_PCAT exhibited improved predictive power over the model only based on clinical factors (AUC = 0.820 versus 0.781, p = 0.003) and showed lower prediction error (Brier score=0.146 versus 0.164).

Long-term prognostic value of the serial changes of CT-derived fractional flow reserve and perivascular fat attenuation index

BACKGROUND

To investigate the serial changes of computed tomography (CT) fractional flow reserve (CT-FFR) and fat attenuation index (FAI), and explore their relationships with long-term clinical outcomes.

METHODS

Consecutive symptomatic patients with an intermediate pretest probability of coronary artery disease 1-4 were prospectively enrolled if coronary CT angiography (CCTA) revealed at least 1 lesion with 30-70% stenosis on major epicardial arteries. Follow-up CCTA was performed at 1 to 1.5-year intervals. All patients were further followed up after the second CCTA until September 2019. The Coronary Artery Disease - Reporting and Data System (CAD-RADS) grade, high-risk plaque features, lesion-specific CT-FFR, and FAI were measured for prognosis analysis.

RESULTS

A total of 263 patients were included in the analysis, and 38 major adverse cardiac events (MACEs) occurred. In the MACE group, the lesion-specific CT-FFR decreased significantly at the follow-up CCTA [0.80 (0.74-0.90) versus 0.85 (0.76-0.93); P=0.01], whereas the FAI did not notably increase (-70.4±8.9 versus -71.3±7.1 HU; P=0.436). In the non-MACE group, lesion-specific CT-FFR increased markedly [0.91 (0.84-0.95) versus 0.90 (0.82-0.94); P<0.001], while the FAI decreased substantially (-74.0±10.8 versus -72.4±11.5 HU; P=0.004). Decreased CT-FFR (adjusted overall hazard ratio =2.455; P=0.023) and increased FAI (adjusted hazard ratio =2.956; P=0.002) were the strongest independent predictors of MACEs. Serial changes of CT-FFR and FAI provided incremental prognostic value (Concordance statistic =0.716; P=0.003; over conventional clinical and imaging parameters (Concordance statistic =0.762; P=0.004).

CONCLUSIONS

Decreased CT-FFR and increased FAI at follow-up CCTA were the 2 strongest predictors of MACEs. Serial changes of CT-FFR and FAI provided incremental prognostic value over conventional clinical and imaging parameters for risk stratification. In addition, decreased CT-FFR provided incremental predictive value for MACEs from 15 months after second CCTA, while increased FAI added prognostic value from the second CCTA onwards.

Prognostic value of CT-derived myocardial blood flow, CT fractional flow reserve and high-risk plaque features for predicting major adverse cardiac events

Background

Myocardial blood flow (MBF), CT fractional flow reserve (CT-FFR) and high-risk plaque (HRP) features have been revealed to be associated with patients' prognosis. However, direct intra-individual comparison of these CT-derived parameters has not been explored yet. The aim of this study was to investigate the prognostic value of CT-derived MBF, CT-FFR and HRP features for predicting major adverse cardiac events (MACEs).

Methods

Consecutive patients with chest pain and intermediate-to-high pre-test probability of coronary artery disease (CAD) were prospectively enrolled. All patients were referred for dynamic CT myocardial perfusion imaging (CT-MPI) + coronary CT angiography (CCTA) and followed up for at least 1 year. MBF_ischemic (mean MBF of all ischemic segments), MBF_ratio (MBF of ischemic segments/MBF of reference segments), CT-FFR and HRP features were measured and multivariate analysis was used to evaluate the predictive value of all above parameters for MACEs.

Results

One hundred and forty-two patients were included into final analysis. MBF_ischemic and MBF_ratio was significantly lower in patients with MACE compared to patients without MACE (87 vs. 153 mL/100 mL/min and 0.64 vs. 0.95, both P<0.001). Similarly, CT-FFR was also markedly lower in patients with MACE (0.58 vs. 0.88, P<0.001) whereas coronary artery calcium score (CACS) was significantly higher (1,038.9 vs. 34.2, P<0.001). According to ROC curve analysis, MBF_ischemic, MBF_ratio and CACS had largest area under curve (AUC =0.872, 0.855 and 0.813 respectively, all P<0.001) for identifying patients with MACE. After adjusted by multivariate analysis, MBF_ischemic (hazard ratio =23.382, P=0.003) and CACS (hazard ratio =3.759, P=0.029) were revealed to be the independent predictors for MACE where CT-FFR and HRP features failed to have prognostic value.

Conclusions

MBF_ischemic derived from dynamic CT-MPI was the strongest predictor for MACE, followed by CACS. MBF_ischemic outperformed HRP features and CT-FFR for prediction of unfavorable clinical outcome.

Epicardial Adipose Tissue Volume Is Associated with High Risk Plaque Profiles in Suspect CAD Patients

OBJECTIVE

To explore the association between EAT volume and plaque precise composition and high risk plaque detected by coronary computed tomography angiography (CCTA).

METHODS

101 patients with suspected coronary artery disease (CAD) underwent CCTA examination from March to July 2019 were enrolled, including 70 cases acute coronary syndrome (ACS) and 31 cases stable angina pectoris (SAP). Based on CCTA image, atherosclerotic plaque precise compositions were analyzed using dedicated quantitative software. High risk plaque was defined as plaque with more than 2 high risk features (spotty calcium, positive remolding, low attenuation plaque, napkin-ring sign) on CCTA image. The association between EAT volume and plaque composition was assessed as well as the different of correlation between ACS and SAP was analyzed. Multivariable logistic regression analysis was used to explore whether EAT volume was independent risk factors of high risk plaque (HRP).

RESULTS

EAT volume in the ACS group was significantly higher than that of the SAP group (143.7 ± 49.8 cm3 vs. 123.3 ± 39.2 cm3, P = 0.046). EAT volume demonstrated a significant positive correlation with total plaque burden (r = 0.298, P = 0.003), noncalcified plaque burden (r = 0.245, P = 0.013), lipid plaque burden (r = 0.250, P = 0.012), and homocysteine (r = 0.413, P ≤ 0.001). In ACS, EAT volume was positively correlated with total plaque burden (r = 0.309, P = 0.009), noncalcified plaque burden (r = 0.242, P = 0.044), and lipid plaque burden (r = 0.240, P = 0.045); however, no correlation was observed in SAP. Patients with HRP have larger EAT volume than those without HRP (169 ± 6.2 cm3 vs. 130.6 ± 5.3 cm3, P = 0.002). After adjustment by traditional risk factors and coronary artery calcium score (CACS), EAT volume was an independent risk predictor of presence of HRP (OR: 1.018 (95% CI: 1.006-1.030), P = 0.004).

CONCLUSIONS

With the increasing EAT volume, more dangerous plaque composition burdens increase significantly. EAT volume is a risk predictor of HRP independent of convention cardiovascular risk factors and CACS, which supports the potential impact of EAT on progression of coronary atherosclerotic plaque.

Epicardial adipose tissue characteristics and CT high-risk plaque features: correlation with coronary thin-cap fibroatheroma determined by intravascular ultrasound

To investigate the correlation of epicardial adipose tissue (EAT) characteristics and high-risk plaque features characterized by coronary CT angiography (CCTA) for identifying the presence of thin-cap fibroatheroma (TCFA). Patients who underwent both CCTA and intravascular ultrasound (IVUS) within 4 weeks were retrospectively included. CT-derived quantitative and qualitative parameters, including diameter stenosis, low attenuation plaque (LAP), napkin-ring sign (NRS), positive remodeling and spotty calcification, were recorded. EAT volume and density were also measured. TCFA lesions and non-TCFA lesions were determined by IVUS. Multivariate regression analysis was used to determine the independent predictors of TCFA lesions. Sixty-eight patients (mean age: 68.6 ± 9.7 years; 40 males) with 91 lesions were finally included in our study. For CT-derived plaque features, LAP (77.8% versus 25%, p < 0.001) and NRS (40.7% versus 9.4%, p < 0.001) was more frequently presented in TCFA lesions than was in non-TCFA lesions. For EAT characteristics, EAT volume (110 ± 14 cm³ versus 98 ± 12 cm³, p < 0.001) was significantly larger whereas EAT density (-77 ± 4 HU versus -80 ± 5, p = 0.003) was markedly higher in TCFA lesions. According to multivariate logistic regression analysis, LAP, EAT volume and EAT density were significant predictors (odds ratio: 9.758, 1.095 and 1.202, all p value < 0.05) for the presence of TCFA lesions. EAT volume and density was greater in patients with TCFA lesions whereas LAP and NRS was more frequently presented. In addition, EAT characteristics and LAP were independent predictors of vulnerable plaques as determined by IVUS.

The value of quantified plaque analysis by dual-source coronary CT angiography to detect vulnerable plaques: a comparison study with intravascular ultrasound

Background

To investigate the diagnostic performance of quantified plaque analysis and high-risk plaque characterization by coronary computed tomography angiography (CCTA) for identifying thin-cap fibroatheroma (TCFA).

Methods

Patients who underwent both CCTA and intravascular ultrasound (IVUS) within 4 weeks were retrospectively included. CT-derived quantitative and qualitative parameters, including diameter stenosis, minimal lumen area (MLA), low attenuation plaque (LAP) volume napkin-ring sign (NRS), positive remodeling (PR) and spotty calcification, were recorded. TCFA lesions and non-TCFA lesions were determined by IVUS. Multivariate regression analysis was used to determine the independent predictors of TCFA lesions.

Results

Sixty-five patients (mean age: 69.8±9.2 years, 29 females) with 89 lesions were finally included. LAP and NRS were more frequently presented in the group of TCFA lesions. The mean LAP volume of TCFA lesions was significantly larger than that of non-TCFA lesions [16.5 (11.0-23.0) vs. 0 (0-1.5) mm³, P<0.001]. According to multivariate logistic regression analysis, LAP volume was the only significant predictor for IVUS-confirmed vulnerable plaques (odds ratio =3.294, 95% confidence interval: 1.177-9.223, P=0.023). LAP volume showed largest area under curve (AUC) for diagnosing TCFA lesions (AUC =0.901, 95% confidence interval: 0.819-0.954, P<0.0001). When using >8 mm³ as the best cutoff value, the diagnostic accuracy, sensitivity and specificity of LAP volume for predicting TCFA lesions were 91.0% (81/89), 84.6% (22/26) and 96.8% (61/63) respectively.

Conclusions

CT-derived LAP volume of TCFA lesions was significantly higher than those of non-TCFA lesions. LAP volume was the strongest predictor for TCFA lesions as validated by IVUS.

The best predictor of ischemic coronary stenosis: subtended myocardial volume, machine learning-based FFRCT, or high-risk plaque features?

OBJECTIVES

The present study aimed to compare the diagnostic performance of a machine learning (ML)-based FFR_CT algorithm, quantified subtended myocardial volume, and high-risk plaque features for predicting if a coronary stenosis is hemodynamically significant, with reference to FFR_ICA.

METHODS

Patients who underwent both CCTA and FFR_ICA measurement within 2 weeks were retrospectively included. ML-based FFR_CT, volume of subtended myocardium (V_sub), percentage of subtended myocardium volume versus total myocardium volume (V_ratio), high-risk plaque features, minimal lumen diameter (MLD), and minimal lumen area (MLA) along with other parameters were recorded. Lesions with FFR_ICA ≤ 0.8 were considered to be functionally significant.

RESULTS

One hundred eighty patients with 208 lesions were included. The lesion length (LL), diameter stenosis, area stenosis, plaque burden, V_sub, V_ratio, V_ratio/MLD, V_ratio/MLA, and LL/MLD⁴ were all significantly longer or larger in the group of FFR_ICA ≤ 0.8 while smaller minimal lumen area, MLD, and FFR_CT value were noted. The AUC of FFR_CT + V_ratio/MLD was significantly better than that of FFR_CT alone (0.935 versus 0.873, p < 0.001). High-risk plaque features failed to show difference between functionally significant and insignificant groups. V_ratio/MLD-complemented ML-based FFR_CT for "gray zone" lesions with FFR_CT value ranged from 0.7 to 0.8 and the combined use of these two parameters yielded the best diagnostic performance (86.5%, 180/208).

CONCLUSIONS

ML-based FFR_CT simulation and V_ratio/MLD both provide incremental value over CCTA-derived diameter stenosis and high-risk plaque features for predicting hemodynamically significant lesions. V_ratio/MLD is more accurate than ML-based FFR_CT for lesions with simulated FFR_CT value from 0.7 to 0.8.

KEY POINTS

• Machine learning-based FFR _CT and subtended myocardium volume both performed well for predicting hemodynamically significant coronary stenosis. • Subtended myocardium volume was more accurate than machine learning-based FFR _CT for "gray zone" lesions with simulated FFR value from 0.7 to 0.8. • CT-derived high-risk plaque features failed to correctly identify hemodynamically significant stenosis.