Abstract Following technological developments and new landmark trials, the diagnostic work-up of symptomatic chronic coronary artery disease (CAD) has evolved. Clinical guidelines now favor noninvasive anatomical assessments by coronary CT angiography (CCTA) as the first-line modality to evaluate CAD in the majority of patients with chest pain. This shift from ischemia testing to stenosis and plaque characterization has resulted in the development of new imaging biomarkers reflecting a variety of coronary plaque features, many of which have proven to be important clinical risk markers. Consequently, there has been a transition from qualitative to semi-quantitative and fully quantitative plaque acquisitions over the entire coronary tree. With the integration of artificial intelligence, novel software enables rapid quantitative acquisitions of plaque components, making them feasible for use in clinical practice. CCTA has also enabled identification of precursor features associated with plaque development such as peri-coronary artery adipose tissue attenuation and epicardial adipose tissue volume. This review provides an overview of CCTA derived plaque features in CAD and associated imaging biomarkers of risk to highlight their potential applications in precision phenotyping and individualized management decisions. It further outlines anticipated future developments that may enable widespread clinical adoption of these novel imaging biomarkers.
J. Lenell, K. Grodecki, J. Kwieciński et al.· BJR|Open· 0 citations
Background and Aims: Epicardial adipose tissue (EAT) has emerged as an important cardiovascular biomarker that reflects both inflammatory and cardiometabolic risk. EAT volume and density vary significantly across populations, yet there is a lack of multicenter studies investigating the predictive value of population-specific EAT percentiles. Methods: In this multicenter study, we retrospectively analyzed low-dose computed tomography correction scans from 42,842 patients undergoing myocardial perfusion imaging. A derivation cohort of 15,082 patients was used to establish sex- and age-specific nomograms for EAT density and EAT volume indexed to body surface area. Percentile-based thresholds were tested for outcome prediction in a validation cohort of 27,760 patients. For clinical implementation, we developed an online EAT percentile calculator. Results: Percentile curves demonstrated increased BSA-indexed EAT volume and decreasing EAT density with age. Over a median follow-up of 3.6 years (IQR: 1.83 - 5.14), 4,956 patients experienced a nonfatal myocardial infarction or death. In multivariable Cox models, patients above the 95th sex- and age-specific percentile had significantly worse outcomes for BSA- indexed EAT volume [adjusted hazard ratio 1.30, 95% CI: 1.14 - 1.49, p < 0.001] and EAT density [adjusted hazard ratio 1.7, 95% CI: 1.51 - 1.92, p<0.001] when compared to patients below the 50th percentile (p<0.001). Conclusion: Age- and sex-specific EAT percentiles provide a clinically interpretable framework for contextualizing automated EAT measurements and identifying patients at increased cardiovascular risk. EAT density was a stronger prognostic marker and identified elevated risk even among patients with normal BMI, supporting its potential to provide information beyond conventional anthropometric assessment.
A. Kamagate, A. Shanbhag, M. Buchwald et al.· medRxiv· 0 citations
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