Identification of patients with acute coronary syndrome and representation of their degree of inflammation: application of pericoronary adipose tissue within different radial distances of the proximal coronary arteries

BACKGROUND: Pericoronary adipose tissue (PCAT) around the proximal right coronary artery (RCA) is considered a marker of coronary inflammation. We aimed to explore the segments of PCAT that represent coronary inflammation in patients with acute coronary syndrome (ACS) and to identify patients with ACS and stable coronary artery disease (CAD) prior to intervention. METHODS: We retrospectively enrolled consecutive patients with ACS and stable CAD who underwent invasive coronary angiography (ICA) after coronary computed tomography angiography (CCTA) from November 2020 to October 2021 at the Fourth Affiliated Hospital of Harbin Medical University. The fat attenuation index (FAI) was obtained using PCAT quantitative measurement software, and the coronary Gensini score was also calculated to indicate the severity of CAD. The differences and correlations between FAI within different radial distances of proximal coronary arteries were evaluated, and the recognition ability of FAI for patients with ACS and stable CAD was evaluated by establishing receiver operator characteristic (ROC) curves. RESULTS: A total of 267 patients were included in the cross-sectional study, including 173 patients with ACS. With the increase of radial distance from the outer wall of proximal coronary vessels, the FAI decreased (P<0.001). The FAI around the proximal left anterior descending artery (LAD) within the reference diameter from the outer wall of the vessel (LAD(ref)) had the highest correlation with the FAI around culprit lesions [r=0.587; 95% confidence interval (CI): 0.489–0.671; P<0.001]. The model based on clinical features, Gensini score, and LAD(ref) had the highest recognition performance for patients with ACS and stable CAD [area under the curve (AUC): 0.663; 95% CI: 0.540–0.785]. CONCLUSIONS: LAD(ref) is most correlated with FAI around culprit lesions in patients with ACS and has higher value in the preintervention differentiation of patients with ACS and stable CAD compared to the use of clinical features alone.