Coronary computed tomography angiography in patients with stable coronary artery disease

Recent advances in cardiovascular risk assessment: The added value of non-invasive anatomic imaging

In 2022, multiple original research studies were conducted highlighting the utility of coronary artery calcium (CAC) imaging in young individuals and provided further evidence for the role of CAC to improve atherosclerotic cardiovascular disease (ASCVD) risk assessment. Mean calcium density was shown to be a more reliable predictor than peak density in risk assessment. Additionally, in light of the ACC/AHA/Multispecialty Chest Pain Guideline's recent elevation of coronary computed tomography angiography (CCTA) to a Class I (level of evidence A) recommendation as an index diagnostic test for acute or stable chest pain, several studies support the utility of CCTA and guided future directions. This review summarizes recent studies that highlight the role of non-invasive imaging in enhancing ASCVD risk assessment across different populations.

Diagnostic accuracy of noninvasive fractional flow reserve derived from computed tomography angiography in ischemia-specific coronary artery stenosis and indeterminate lesions: results from a multicenter study in China

Background

To determine the diagnostic performance of a novel computational fluid dynamics (CFD)-based algorithm for in situ CT-FFR in patients with ischemia-induced coronary artery stenosis. Additionally, we investigated whether the diagnostic accuracy of CT-FFR differs significantly across the spectrum of disease and analyzed the influencing factors that contribute to misdiagnosis.

Methods

Coronary computed tomography angiography (CCTA), invasive coronary angiography (ICA), and FFR were performed on 324 vessels from 301 patients from six clinical medical centers. Local investigators used CCTA and ICA to conduct assessments of stenosis, and CT-FFR calculations were performed in the core laboratory. For CCTA and ICA, CT-FFR ≤ 0.8 and a stenosis diameter ≥ 50% were identified as lesion-specific ischemia. Univariate logistic regression models were used to assess the effect of features on discordant lesions (false negative and false positive) in different CT-FFR categories. The diagnostic performance of CT-FFR was analyzed using an invasive FFR ≤ 0.8 as the gold standard.

Results

The Youden index indicated an optimal threshold of 0.80 for CT-FFR to identify functionally ischemic lesions. On a per-patient basis, the diagnostic sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) for CT-FFR were 96% (91%-98%), 92% (87%-96%), 94% (90%-96%), 91% (85%-95%), and 96% (92%-99%), respectively. The diagnostic efficacy of CT-FFR was higher than that of CCTA without the influence of calcification. Closer to the cut point, there was less certainty, with the agreement between the invasive FFR and the CT-FFR being at its lowest in the CT-FFR range of 0.7-0.8. In all lesions, luminal stenosis ≥ 50% significantly affected the risk of reduced false negatives (FN) and false positives (FP) results by CT-FFR, irrespective of the association with calcified plaque.

Conclusions

In summary, CT-FFR based on the new parameter-optimized CFD model has a better diagnostic performance than CTA for lesion-specific ischemia. The presence of calcified plaque has no significant effect on the diagnostic performance of CT-FFR and is independent of the degree of calcification. Given the range of applicability of our software, its use at a CT-FFR of 0.7-0.8 requires caution and must be considered in the context of multiple factors.

Multimodality Imaging in Ischemic Chronic Cardiomyopathy

Ischemic chronic cardiomyopathy (ICC) is still one of the most common cardiac diseases leading to the development of myocardial ischemia, infarction, or heart failure. The application of several imaging modalities can provide information regarding coronary anatomy, coronary artery disease, myocardial ischemia and tissue characterization. In particular, coronary computed tomography angiography (CCTA) can provide information regarding coronary plaque stenosis, its composition, and the possible evaluation of myocardial ischemia using fractional flow reserve CT or CT perfusion. Cardiac magnetic resonance (CMR) can be used to evaluate cardiac function as well as the presence of ischemia. In addition, CMR can be used to characterize the myocardial tissue of hibernated or infarcted myocardium. Echocardiography is the most widely used technique to achieve information regarding function and myocardial wall motion abnormalities during myocardial ischemia. Nuclear medicine can be used to evaluate perfusion in both qualitative and quantitative assessment. In this review we aim to provide an overview regarding the different noninvasive imaging techniques for the evaluation of ICC, providing information ranging from the anatomical assessment of coronary artery arteries to the assessment of ischemic myocardium and myocardial infarction. In particular this review is going to show the different noninvasive approaches based on the specific clinical history of patients with ICC.

The impact of lipid-lowering medications on coronary artery plaque characteristics

Atherosclerosis is the predominant cause of coronary artery disease. The last several decades have witnessed significant advances in lipid-lowering therapies, which comprise a central component of atherosclerotic cardiovascular disease prevention. In addition to cardiovascular risk reduction with dyslipidemia management, some lipid-based therapies show promise at the level of the atherosclerotic plaque itself through mechanisms governing lipid accumulation, plaque stability, local inflammation, endothelial dysfunction, and thrombogenicity. The capacity of lipid-lowering therapies to modify atherosclerotic plaque burden, size, composition, and vulnerability should correlate with their ability to reduce disease progression. This review discusses plaque characteristics, diagnostic modalities to evaluate these characteristics, and how they are altered by current and emerging lipid-lowering therapies, all in human coronary artery disease.

Diagnostic Improvements of Deep Learning-Based Image Reconstruction for Assessing Calcification-Related Obstructive Coronary Artery Disease

Objectives: The objective of this study was to explore the diagnostic value of deep learning-based image reconstruction (DLR) and hybrid iterative reconstruction (HIR) for calcification-related obstructive coronary artery disease (CAD) evaluation by using coronary CT angiography (CCTA) images and subtraction CCTA images.

Methods: Forty-two consecutive patients with known or suspected coronary artery disease who underwent coronary CTA on a 320-row CT scanner and subsequent invasive coronary angiography (ICA), which was used as the reference standard, were enrolled. The DLR and HIR images were reconstructed as CTA_DLR and CTA_HIR, and, based on which, the corresponding subtraction CCTA images were established as CTA_sDLR and CTA_sHIR, respectively. Qualitative images quality comparison was performed by using a Likert 4 stage score, and quantitative images quality parameters, including image noise, signal-to-noise ratio, and contrast-to-noise ratio were calculated. Diagnostic performance on the lesion level was assessed and compared among the four CCTA approaches (CTA_DLR, CTA_HIR, CTA_sDLR, and CTA_sHIR).

Results: There were 166 lesions of 86 vessels in 42 patients (32 men and 10 women; 62.9 ± 9.3 years) finally enrolled for analysis. The qualitative and quantitative image qualities of CTA_sDLR and CTA_DLR were superior to those of CTA_sHIR and CTA_HIR, respectively. The diagnostic accuracies of CTA_sDLR, CTA_DLR, CTA_sHIR, and CTA_HIR to identify calcification-related obstructive diameter stenosis were 83.73%, 69.28%, 75.30%, and 65.66%, respectively. The false-positive rates of CTA_sDLR, CTA_DLR, CTA_sHIR, and CTA_HIR for luminal diameter stenosis ≥50% were 15%, 31%, 24%, and 34%, respectively. The sensitivity and the specificity to identify ≥50% luminal diameter stenosis was 90.91% and 83.23% for CTA_sDLR.

Conclusion: Our study showed that deep learning–based image reconstruction could improve the image quality of CCTA images and diagnostic performance for calcification-related obstructive CAD, especially when combined with subtraction technique.