Coronary Computed Tomography Angiography-Derived Plaque Quantification in Patients With Acute Coronary Syndrome

The value of coordinated analysis of multimodal atherosclerotic plaque imaging in the assessment of cardiovascular and cerebrovascular events

Background

Although atherosclerosis (AS) can affect multiple vascular beds, previous studies have focused on the analysis of single-site AS plaques.

Objective

The aim of this study is to explore the differences or similarities in the characteristics of atherosclerotic plaque found in the internal carotid artery, cerebral artery, and coronary artery between patients with atherosclerotic cardiovascular disease (ASCVD) and those without events.

Methods

Patients aged ≥ 18 years who underwent both high-resolution vessel wall imaging (HR-VWI) and coronary computed tomography angiography (CCTA) were retrospectively collected and categorized into the ASCVD group and the non-event group. The plaques were then categorized into culprit plaques, non-culprit plaques, and non-event plaques. Plaque morphological data such as stenosis, stenosis grades, plaque length (PL), plaque volume (PV), minimal lumen area (MLA), enhancement grade, and plaque composition data such as calcified plaque volume (CPV), fibrotic plaque volume (FPV), fibro-lipid plaque volume (FLPV), lipid plaque volume (LPV), calcified plaque volume ratio (CPR), fibrotic plaque volume ratio (FPR), fibro-lipid plaque ratio (FLPR), lipid plaque volume ratio (LPR), intraplaque hemorrhage volume (IPHV), and intraplaque hemorrhage volume ratio (IPHR)were recorded and analyzed.

Results

A total of 44 patients (mean age 66 years, SD 9 years, 28 men) were included. In cervicocephalic plaques, the ASCVD group had more severe stenosis grades (p = 0.030) and demonstrated significant differences in LPV, LPR, and CPV (p = 0.044, 0.030, 0.020) compared with the non-event group. In coronary plaques, the ASCVD group had plaques with greater stenosis (p < 0.001), more severe stenosis grades (p < 0.001), larger volumes (p = 0.001), longer length (p = 0.008), larger FLPV (p = 0.012), larger FPV (p = 0.002), and higher FPR (p = 0.043) compared with the non-event group. There were significant differences observed in stenosis (HR-VWI, CCTA: p < 0.001, p < 0.001), stenosis grades (HR-VWI, CCTA: p < 0.001, p < 0.001), plaque length (HR-VWI, CCTA: p = 0.028, p < 0.001), and plaque volume (HR-VWI, CCTA: p = 0.013, p = 0.018) between the non-event plaque, non-culprit plaque, and culprit plaque. In the image analysis of HR-VWI, there were differences observed between IPHR (p < 0.001), LPR (p = 0.001), FPV (p = 0.011), and CPV (p = 0.015) among the three groups of plaques. FLPV and FPV were significantly different among the three different plaque types from the coronary artery (p = 0.043, p = 0.022).

Conclusion

There is a consistent pattern of change in plaque characteristics between the cervicocephalic and coronary arteries in the same patient.

Clinical value of perivascular fat attenuation index and computed tomography derived fractional flow reserve in identification of culprit lesion of subsequent acute coronary syndrome

Purpose

To explore the potential of perivascular fat attenuation index (FAI) and coronary computed tomography angiography (CCTA) derived fractional flow reserve (CT-FFR) in the identification of culprit lesion leading to subsequent acute coronary syndrome (ACS).

Methods

Thirty patients with documented ACS event who underwent invasive coronary angiography (ICA) from February 2019 to February 2021 and had received CCTA in the previous 6 months were collected retrospectively. 40 patients with stable angina pectoris (SAP) were matched as control group according to sex, age and risk factors. The study population has a mean age of 59.3 ± 12.3 years, with a male prevalence of 81.4%. The plaque characteristics, perivascular fat attenuation index (FAI), and coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) of 32 culprit lesions and 30 non-culprit lesions in ACS patients and 40 highest-grade stenosis lesions in SAP patients were statistically analyzed.

Results

FAI around culprit lesions was increased significantly (-72.4 ± 3.2 HU vs. -79.0 ± 7.7 HU, vs. -80.4 ± 7.0HU, all p < 0.001) and CT-FFR was decreased for culprit lesions of ACS patients [0.7(0.1) vs. 0.8(0.1), vs.0.8(0.1), p < 0.001] compared to other lesions. According to multivariate analysis, diameter stenosis (DS), FAI, and CT-FFR were significant predictors for identification of the culprit lesion. The integration model of DS, FAI, and CT-FFR showed the significantly highest area under the curve (AUC) of 0.917, compared with other single predictors (all p < 0.05).

Conclusions

This study proposes a novel integrated prediction model of DS, FAI, and CT-FFR that enhances the diagnostic accuracy of traditional CCTA for identifying culprit lesions that trigger ACS. Furthermore, this model also provides improved risk stratification for patients and offers valuable insights for predicting future cardiovascular events.

Gender Differences in Epicardial Adipose Tissue and Plaque Composition by Coronary CT Angiography: Association with Cardiovascular Outcome

Background: To investigate gender differences in epicardial adipose tissue (EAT) and plaque composition by coronary CT angiography (CCTA) and the association with cardiovascular outcome. Methods: Data of 352 patients (64.2 ± 10.3 years, 38% female) with suspected coronary artery disease (CAD) who underwent CCTA were retrospectively analyzed. EAT volume and plaque composition from CCTA were compared between men and women. Major adverse cardiovascular events (MACE) were recorded from follow-up. Results: Men were more likely to have obstructive CAD, higher Agatston scores, and a larger total and non-calcified plaque burden. In addition, men displayed more adverse plaque characteristics and EAT volume compared to women (all p < 0.05). After a median follow-up of 5.1 years, MACE occurred in 8 women (6%) and 22 men (10%). In multivariable analysis, Agatston calcium score (HR 1.0008, p = 0.014), EAT volume (HR 1.067, p = 0.049), and low-attenuation plaque (HR 3.82, p = 0.036) were independent predictors for MACE in men, whereas only low-attenuation plaque (HR 2.42, p = 0.041) showed predictive value for events in women. Conclusion: Women demonstrated less overall plaque burden, fewer adverse plaque characteristics, and a smaller EAT volume compared to men. However, low-attenuation plaque is a predictor for MACE in both genders. Thus, a differentiated plaque analysis is warranted to understand gender differences of atherosclerosis to guide medical therapy and prevention strategies.

Association of epicardial adipose tissue with coronary CT angiography plaque parameters on cardiovascular outcome in patients with and without diabetes mellitus

BACKGROUND AND AIMS

We aimed to evaluate the association of epicardial adipose tissue (EAT) with coronary CT angiography (CCTA) plaque parameters on cardiovascular outcome in patients with and without diabetes mellitus.

METHODS

Data of 353 patients (62.9 ± 10.4 years, 62% male), who underwent CCTA as part of their clinical workup for the evaluation of suspected or known CAD, were retrospectively analyzed. EAT volume and plaque parameters from CCTA were compared in patients with diabetes (n = 63) and without diabetes (n = 290). Follow-up was performed to record adverse cardiovascular events. The predictive value to detect adverse cardiovascular events was assessed using concordance indices (CIs) and multivariable Cox proportional hazards analysis.

RESULTS

In total, 33 events occurred after a median follow-up of 5.1 years. In patients with diabetes, EAT volume and plaque parameters were significantly higher than in patients without diabetes (all p < 0.05). A multivariable model demonstrated an incrementally improved C-index of 0.84 (95%CI 0.80-0.88) over the Framingham risk score and single measures alone. In multivariable Cox regression analysis EAT volume (Hazard ratio[HR] 1.21, p = 0.022), obstructive CAD (HR 1.18, p = 0.042), and ≥2 high-risk plaque features (HR 2.13, p = 0.031) were associated with events in patients with diabetes and obstructive CAD (HR 1.88, p = 0.017), and Agatston calcium score (HR 1.009, p = 0.039) in patients without diabetes.

CONCLUSIONS

EAT, as a biomarker of inflammation, and plaque parameters, as an extent of atherosclerotic CAD, are higher in patients with diabetes and are associated with increased adverse cardiovascular outcomes. These parameters may help identify patients at high risk with need for more aggressive therapeutic and preventive care.

Automated Identification of Coronary Arteries in Assisting Inexperienced Readers: Comparison between Two Commercial Vendors

Background: to assess the performance and speed of two commercially available advanced cardiac software packages in the automated identification of coronary vessels as an aiding tool for inexperienced readers. Methods: Hundred and sixty patients undergoing coronary CT angiography (CCTA) were prospectively enrolled from February until September 2021 and randomized in two groups, each one composed by 80 patients. Patients in group 1 were scanned on Revolution EVO CT Scanner (GE Healthcare), while patients in group 2 had the CCTA performed on Brilliance iCT (Philips Healthcare); each examination was evaluated on the respective vendor proprietary advanced cardiac software (software 1 and 2, respectively). Two inexperienced readers in cardiac imaging verified the software performance in the automated identification of the three major coronary vessels: (RCA, LCx, and LAD) and in the number of identified coronary segments. Time of analysis was also recorded. Results: software 1 correctly and automatically nominated 202/240 (84.2%) of the three main coronary vessels, while software 2 correctly identified 191/240 (79.6%) (p = 0.191). Software 1 achieved greater performances in recognizing the LCx (81.2% versus 67.5%; p = 0.048), while no differences have been reported in detecting the RCA (p = 0.679), and the LAD (p = 0.618). On a per-segment analysis, software 1 outperformed software 2, automatically detecting 942/1062 (88.7%) coronary segments, while software 2 detected 797/1078 (73.9%) (p < 0.001). Average reconstruction and detection time was of 13.8 s for software 1 and 21.9 s for software 2 (p < 0.001). Conclusions: automated cardiac software packages are a reliable and time-saving tool for inexperienced reader. Software 1 outperforms software 2 and might therefore better assist inexperienced CCTA readers in automated identification of the three main vessels and coronaries segments, with a consistent time saving of the reading session.

Additive value of epicardial adipose tissue quantification to coronary CT angiography-derived plaque characterization and CT fractional flow reserve for the prediction of lesion-specific ischemia

OBJECTIVES

Epicardial adipose tissue (EAT) from coronary CT angiography (CCTA) is strongly associated with coronary artery disease (CAD). We investigated the additive value of EAT volume to coronary plaque quantification and CT-derived fractional flow reserve (CT-FFR) to predict lesion-specific ischemia.

METHODS

Patients (n = 128, 60.6 ± 10.5 years, 61% male) with suspected CAD who had undergone invasive coronary angiography (ICA) and CCTA were retrospectively analyzed. EAT volume and plaque measures were derived from CCTA using a semi-automatic software approach, while CT-FFR was calculated using a machine learning algorithm. The predictive value and discriminatory power of EAT volume, plaque measures, and CT-FFR to identify ischemic CAD were assessed using invasive FFR as the reference standard.

RESULTS

Fifty-five of 152 lesions showed ischemic CAD by invasive FFR. EAT volume, CCTA ≥ 50% stenosis, and CT-FFR were significantly different in lesions with and without hemodynamic significance (all p < 0.05). Multivariate analysis revealed predictive value for lesion-specific ischemia of these parameters: EAT volume (OR 2.93, p = 0.021), CCTA ≥ 50% (OR 4.56, p = 0.002), and CT-FFR (OR 6.74, p < 0.001). ROC analysis demonstrated incremental discriminatory value with the addition of EAT volume to plaque measures alone (AUC 0.84 vs. 0.62, p < 0.05). CT-FFR (AUC 0.89) showed slightly superior performance over EAT volume with plaque measures (AUC 0.84), however without significant difference (p > 0.05).

CONCLUSIONS

EAT volume is significantly associated with ischemic CAD. The combination of EAT volume with plaque quantification demonstrates a predictive value for lesion-specific ischemia similar to that of CT-FFR. Thus, EAT may aid in the identification of hemodynamically significant coronary stenosis.

KEY POINTS

• CT-derived EAT volume quantification demonstrates high discriminatory power to identify lesion-specific ischemia. • EAT volume shows incremental diagnostic performance over CCTA-derived plaque measures in detecting lesion-specific ischemia. • A combination of plaque measures with EAT volume provides a similar discriminatory value for detecting lesion-specific ischemia compared to CT-FFR.

Improved long-term prognostic value of coronary CT angiography-derived plaque measures and clinical parameters on adverse cardiac outcome using machine learning

OBJECTIVES

To evaluate the long-term prognostic value of coronary CT angiography (cCTA)-derived plaque measures and clinical parameters on major adverse cardiac events (MACE) using machine learning (ML).

METHODS

Datasets of 361 patients (61.9 ± 10.3 years, 65% male) with suspected coronary artery disease (CAD) who underwent cCTA were retrospectively analyzed. MACE was recorded. cCTA-derived adverse plaque features and conventional CT risk scores together with cardiovascular risk factors were provided to a ML model to predict MACE. A boosted ensemble algorithm (RUSBoost) utilizing decision trees as weak learners with repeated nested cross-validation to train and validate the model was used. Performance of the ML model was calculated using the area under the curve (AUC).

RESULTS

MACE was observed in 31 patients (8.6%) after a median follow-up of 5.4 years. Discriminatory power was significantly higher for the ML model (AUC 0.96 [95%CI 0.93-0.98]) compared with conventional CT risk scores including Agatston calcium score (AUC 0.84 [95%CI 0.80-0.87]), segment involvement score (AUC 0.88 [95%CI 0.84-0.91]), and segment stenosis score (AUC 0.89 [95%CI 0.86-0.92], all p < 0.05). Similar results were shown for adverse plaque measures (AUCs 0.72-0.82, all p < 0.05) and clinical parameters including the Framingham risk score (AUCs 0.71-0.76, all p < 0.05). The ML model yielded significantly higher diagnostic performance compared with logistic regression analysis (AUC 0.96 vs. 0.92, p = 0.024).

CONCLUSION

Integration of a ML model improves the long-term prediction of MACE when compared with conventional CT risk scores, adverse plaque measures, and clinical information. ML algorithms may improve the integration of patient's information to enhance risk stratification.

KEY POINTS

• A machine learning (ML) model portends high discriminatory power to predict major adverse cardiac events (MACE). • ML-based risk stratification shows superior diagnostic performance for MACE prediction over coronary CT angiography (cCTA)-derived risk scores or clinical parameters alone. • A ML model outperforms conventional logistic regression analysis for the prediction of MACE.

Relationships between myocardial perfusion abnormalities and integrated indices of atherosclerotic burden: clinical impact of combined anatomic-functional evaluation

Aim to evaluate the relationships between functional and anatomical information obtained by myocardial perfusion imaging (MPI) and coronary computed tomography angiography (CCTA) in a series of consecutive patients at intermediate probability of coronary artery disease (CAD). Material and Methods — The study group comprised 139 patients (83 men, age of 61.6±7.5 years) who underwent CCTA and single-photon emission computed tomography myocardial perfusion imaging (SPECT-MPI). Based on CCTA results patients were divided into three groups: 1) with the absence of coronary atherosclerosis on CCTA; 2) with non-obstructive CAD (<50%); 3) with obstructive (≥50%) CAD. The Segment Involvement Score, Segment Stenosis Score (SSS) and CTA Risk Score were calculated as measures of global atherosclerosis burden. MPI studies were considered abnormal in the presence of SSS≥4. Results — Abnormal myocardial perfusion was detected in 60% of cases in group 1 and 2; in 75% of cases in group 3. The overall frequencies of normal and abnormal MPI studies differed significantly only in obstructive CAD patients and did not differ in group 1 and 2. There were no significant correlations between calcium score, atherosclerotic lesion length, positive remodelling index and MPI results in patients with non-obstructive as well as in patients with obstructive CAD. In group of patients with obstructive CAD Segment Stenosis Score correlated wekly with SSS (r=0.39, p=0.001) and SDS (r=0.28; p=0.012); the CTA Risk Score showed correlationes with SSS (r=0.38, p=0.002) and SDS (r=0.30, p=0.020). Conclusion — Myocardial perfusion abnormalities may develop even in the absence of critical coronary artery lesions. The extent of myocardial ischemia correlates with measures of global CAD burden only in patients with obstructive CAD.

Impact of Coronary Computerized Tomography Angiography-Derived Plaque Quantification and Machine-Learning Computerized Tomography Fractional Flow Reserve on Adverse Cardiac Outcome

This study investigated the impact of coronary CT angiography (cCTA)-derived plaque markers and machine-learning-based CT-derived fractional flow reserve (CT-FFR) to identify adverse cardiac outcome. Data of 82 patients (60 ± 11 years, 62% men) who underwent cCTA and invasive coronary angiography (ICA) were analyzed in this single-center retrospective, institutional review board-approved, HIPAA-compliant study. Follow-up was performed to record major adverse cardiac events (MACE). Plaque quantification of lesions responsible for MACE and control lesions was retrospectively performed semiautomatically from cCTA together with machine-learning based CT-FFR. The discriminatory value of plaque markers and CT-FFR to predict MACE was evaluated. After a median follow-up of 18.5 months (interquartile range 11.5 to 26.6 months), MACE was observed in 18 patients (21%). In a multivariate analysis the following markers were predictors of MACE (odds ratio [OR]): lesion length (OR 1.16, p = 0.018), low-attenuation plaque (<30 HU) (OR 4.59, p = 0.003), Napkin ring sign (OR 2.71, p = 0.034), stenosis ≥50% (OR 3.83, p 0.042), and CT-FFR ≤0.80 (OR 7.78, p = 0.001). Receiver operating characteristics analysis including stenosis ≥50%, plaque markers and CT-FFR ≤0.80 (Area under the curve 0.94) showed incremental discriminatory power over stenosis ≥50% alone (Area under the curve 0.60, p <0.0001) for the prediction of MACE. cCTA-derived plaque markers and machine-learning CT-FFR demonstrate predictive value to identify MACE. In conclusion, combining plaque markers with machine-learning CT-FFR shows incremental discriminatory power over cCTA stenosis grading alone.

Coronary CT angiography-derived plaque quantification with artificial intelligence CT fractional flow reserve for the identification of lesion-specific ischemia

OBJECTIVES

We sought to investigate the diagnostic performance of coronary CT angiography (cCTA)-derived plaque markers combined with deep machine learning-based fractional flow reserve (CT-FFR) to identify lesion-specific ischemia using invasive FFR as the reference standard.

METHODS

Eighty-four patients (61 ± 10 years, 65% male) who had undergone cCTA followed by invasive FFR were included in this single-center retrospective, IRB-approved, HIPAA-compliant study. Various plaque markers were derived from cCTA using a semi-automatic software prototype and deep machine learning-based CT-FFR. The discriminatory value of plaque markers and CT-FFR to identify lesion-specific ischemia on a per-vessel basis was evaluated using invasive FFR as the reference standard.

RESULTS

One hundred three lesion-containing vessels were investigated. 32/103 lesions were hemodynamically significant by invasive FFR. In a multivariate analysis (adjusted for Framingham risk score), the following markers showed predictive value for lesion-specific ischemia (odds ratio [OR]): lesion length (OR 1.15, p = 0.037), non-calcified plaque volume (OR 1.02, p = 0.007), napkin-ring sign (OR 5.97, p = 0.014), and CT-FFR (OR 0.81, p < 0.0001). A receiver operating characteristics analysis showed the benefit of identifying plaque markers over cCTA stenosis grading alone, with AUCs increasing from 0.61 with ≥ 50% stenosis to 0.83 with addition of plaque markers to detect lesion-specific ischemia. Further incremental benefit was realized with the addition of CT-FFR (AUC 0.93).

CONCLUSION

Coronary CTA-derived plaque markers portend predictive value to identify lesion-specific ischemia when compared to cCTA stenosis grading alone. The addition of CT-FFR to plaque markers shows incremental discriminatory power.

KEY POINTS

• Coronary CT angiography (cCTA)-derived quantitative plaque markers of atherosclerosis portend high discriminatory power to identify lesion-specific ischemia. • Coronary CT angiography-derived fractional flow reserve (CT-FFR) shows superior diagnostic performance over cCTA alone in detecting lesion-specific ischemia. • A combination of plaque markers with CT-FFR provides incremental discriminatory value for detecting flow-limiting stenosis.

Relationship between patient presentation and morphology of coronary atherosclerosis by quantitative multidetector computed tomography

Aims

Quantitative computed tomography (QCT) allows assessment of morphological features of coronary atherosclerosis. We aimed to test the hypothesis that clinical patient presentation is associated with distinct morphological features of coronary atherosclerosis.

Methods and results

A total of 1652 participants, representing a spectrum of clinical risk profiles [787 asymptomatic individuals from the general population, 468 patients with acute chest pain without acute coronary syndrome (ACS), and 397 patients with acute chest pain and ACS], underwent multidetector computed tomography. Of these, 274 asymptomatic individuals, 254 patients with acute chest pain without ACS, and 327 patients with acute chest pain and ACS underwent QCT to assess coronary plaque volumes and proportions of dense calcium (DC), fibrous, fibro fatty (FF), and necrotic core (NC) tissue. Furthermore, the presence of vulnerable plaques, defined by plaque volume and tissue composition, was examined. Coronary plaque volume increased significantly with worsening clinical risk profile [geometric mean (95% confidence interval): 148 (129–166) mm3, 257 (224–295) mm3, and 407 (363–457) mm3, respectively, P < 0.001]. Plaque composition differed significantly across cohorts, P < 0.0001. The proportion of DC decreased, whereas FF and NC increased with worsening clinical risk profile (mean proportions DC: 33%, 23%, 23%; FF: 50%, 61%, 57%; and NC: 17%, 17%, 20%, respectively). Significant differences in plaque composition persisted after multivariable adjustment for age, gender, body surface area, hypertension, statin use at baseline, diabetes, smoking, family history of ischaemic heart disease, total plaque volume, and tube voltage, P < 0.01.

Conclusion

Coronary atherosclerotic plaque volume and composition are strongly associated to clinical presentation.

Dual-Energy Computed Tomography in Cardiothoracic Vascular Imaging

Dual energy computed tomography is becoming increasingly widespread in clinical practice. It can expand on the traditional density-based data achievable with single energy computed tomography by adding novel applications to help reach a more accurate diagnosis. The implementation of this technology in cardiothoracic vascular imaging allows for improved image contrast, metal artifact reduction, generation of virtual unenhanced images, virtual calcium subtraction techniques, cardiac and pulmonary perfusion evaluation, and plaque characterization. The improved diagnostic performance afforded by dual energy computed tomography is not associated with an increased radiation dose. This review provides an overview of dual energy computed tomography cardiothoracic vascular applications.

The evolving view of coronary artery calcium and cardiovascular disease risk

Calcification of the coronary artery is a complex pathophysiologic process that is intimately associated with atherosclerosis. Extensive investigation has demonstrated the value of identifying and quantifying coronary artery calcium (CAC) in atherosclerotic cardiovascular disease (CVD) prognostication. However, over the last several years, an increasing body of evidence has suggested that CAC has underappreciated aspects that modulate, and at times attenuate, future CVD risk. The most commonly used measure of CAC, the Agatston unit, effectively models both higher density and higher area of CAC as risk factors for future CVD events. Recent findings from the Multi-Ethnic Study of Atherosclerosis (MESA) have challenged this assumption, demonstrating that higher density of CAC is protective for coronary heart disease and CVD events. Statins may be associated with an increase in CAC, an unexpected finding given their clear benefits in the prevention and treatment of CVD. Studies utilizing intracoronary ultrasound and coronary computed tomography angiography have demonstrated that calcified atherosclerotic plaque-as compared with noncalcified or sparsely calcified plaque-is associated with fewer CVD events. These studies lend support to the often-asserted (but as yet unvalidated) view that calcification may play a role in plaque stabilization. Furthermore, vascular calcification, though a surrogate for atherosclerotic plaque burden, may also possess identifiable aspects that can refine CVD risk assessment.