Coronary artery disease (CAD) is the leading global cause of mortality, accounting for approximately 30% of all deaths. It is primarily characterized by the accumulation of atherosclerotic plaques within the coronary arteries, leading to reduced blood flow to the heart muscle. Early detection of atherosclerotic plaques is crucial to prevent major adverse cardiac events. Notably, recent studies have shown that 15% of myocardial infarctions occur in patients with non-obstructive CAD, underscoring the importance of comprehensive plaque assessment beyond merely identifying obstructive lesions. Cardiac Computed Tomography Angiography (CCTA) has emerged as a cost-effective and efficient technique for excluding obstructive CAD, particularly in patients with a low-to-intermediate clinical likelihood of the disease. Recent advancements in CCTA technology, such as improved resolution and reduced scan times, have mitigated many technical challenges, allowing for precise quantification and characterization of both calcified and non-calcified atherosclerotic plaques. This review focuses on two critical physiological aspects of atherosclerotic plaques: the burden of calcifications, assessed via the coronary artery calcium score (CACs), and perivascular fat attenuation index (pFAI), an emerging marker of vascular inflammation. The CACs, obtained through non-contrast CT scans, quantifies calcified plaque burden and is widely used to stratify cardiovascular risk, particularly in asymptomatic patients. Despite its prognostic value, the CACs does not provide information on non-calcified plaques or inflammatory status. In contrast, the pFAI, derived from CCTA, serves as an indirect marker of coronary inflammation and has shown potential in predicting adverse cardiac events. Combining both CACs and pFAI assessment could offer a comprehensive risk stratification approach, integrating the established calcification burden with novel inflammatory markers to enhance CAD prevention and management strategies.

Cardiac computed tomography angiography (CCTA) has emerged as a cost-effective and time -saving technique for excluding coronary artery disease. One valuable tool obtained by CCTA is the coronary artery calcium (CAC) score. The use of CAC scoring has shown promise in the risk assessment and stratification of cardiovascular disease. CAC scores can be complemented by plaque analysis to assess vulnerable plaque characteristics and further refine risk assessment. This paper aims to provide a comprehensive understanding of the value of the CAC as a prognostic tool and its implications for patient risk assessment, treatment strategies and outcomes. CAC scoring has demonstrated superior ability in stratifying patients, especially asymptomatic individuals, compared to traditional risk factors and scoring systems. The main evidence suggests that individuals with CAC score of 0 have a good long-term prognosis, while an elevated CAC score is associated with increased cardiovascular risk. Finally, the clinical power of CAC scoring and the development of new models for risk stratification could be enhanced by machine learning algorithms.

Mitral annulus calcification (MAC) is no longer seen as merely age-related. Recent evidence links MAC to calcific aortic stenosis (AS), but its prognostic value in AS patients undergoing aortic valve replacement (AVR) remains unclear. This study aims to assess the impact of cardiac CT-derived MAC on cardiovascular outcomes in this population. Pre-procedural contrast-enhanced CCT was performed, and Guerrero CCT-derived MAC score was used to quantify the MAC severity. Major adverse cardiovascular events (MACE − cardiovascular mortality, significant arrhythmias [sustained ventricular tachycardia, asystole, complete atrioventricular block], stroke, hospitalizations due to heart failure) and all-cause and non-cardiovascular mortality were assessed during the 60 months of follow-up after AVR. Multivariate Cox models evaluated the predictive value of severe MAC alongside other significant variables. A total of 313 patients with severe AS and subsequent AVR were included in the study. Among these, 34.6 % of patients had no criteria for MAC, while 65.4 % exhibited MAC (54.1 % presented mild MAC, 33.6 % moderate MAC, and 12.1 % severe MAC). In multivariable Cox models, severe MAC, moderate-to-severe mitral regurgitation, and Society of Thoracic Surgeons (STS) score > 2.5 % remained independent predictors of MACE (all p < 0.05). Also, severe MAC and STS scores > 2.5 % remained independent predictors of all-cause mortality (all p < 0.05) in multivariable analysis. The present study demonstrated that severe MAC independently predicted worse outcomes in patients undergoing AVR, suggesting that incorporating it into the clinical evaluation could ensure a critical change in patient care.