- Inflammation surrounding the coronary arteries can be non-invasively assessed using pericoronary adipose tissue attenuation (PCAT). While PCAT holds promise for further risk stratification of patients with low coronary artery disease (CAD) prevalence, its value in higher risk populations remains unknown.
- Pericoronary adipose tissue (PCAT) attenuation is an indicator of active inflammation of perivascular adipose tissue, which is supposed to increase in diabetic patients. We aimed to investigate the PCAT attenuation values and high-risk plaque (HRP) features in diabetic and non-diabetic subjects with different stenotic extents.
- TOC SUMMARY: It is unclear how air pollution contributes to the development of cardiovascular disease. We investigated the change of coronary atherosclerosis using serial CCTAs in relation to the cumulative amount of PM2.5 exposure between the two CCTAs in 3,127 healthy adults. Coronary calcification progressed in 1,361 (43.5%) subjects with a positive relationship between the cumulative amount of PM2.5 exposure and CACS. The cumulative amount of PM2.5 exposure, rather than the average concentration of PM2.5, was independently associated with progression of coronary calcification and diffuse development of de novo calcified plaques, with its impact higher than any other traditional cardiovascular risk factors.
- Whether coronary plaque characteristics assessed in coronary computed tomography angiography (CCTA) in association with the coronary artery calcium score (CACS) have predictive value for coronary events is unclear. We aimed to examine the predictive value of the CACS and plaque characteristics for the occurrence of coronary events.
- The purpose of this study is to determine if a new score calculated with coronary artery calcium (CAC) density and volume is associated with total coronary artery plaque burden and composition on coronary CT angiography (CCTA) compared to the Agatston score (AS).
- The influence of computed tomography (CT) reconstruction algorithms on the performance of machine-learning-based CT-derived fractional flow reserve (CT-FFRML) has not been investigated. CT-FFRML values and processing time of two reconstruction algorithms were compared using an on-site workstation.