Clinical value of perivascular fat attenuation index and computed tomography derived fractional flow reserve in identification of culprit lesion of subsequent acute coronary syndrome

• Published in: Frontiers in cardiovascular medicine Vol. 10; p. 1090397
• Main Authors: Huang, Minggang, Han, Tingting, Nie, Xuan, Zhu, Shunming, Yang, Di, Mu, Yue, Zhang, Yan
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 02.06.2023
• Subjects: acute coronary syndrome; Cardiovascular Medicine; coronary computed tomography angiography; fat attenuation index; fraction flow reserve; plaque characteristics; acute coronary syndrome; plaque characteristics; coronary computed tomography angiography; fraction flow reserve; fat attenuation index
• ISSN: 2297-055X; 2297-055X
• DOI: 10.3389/fcvm.2023.1090397

To explore the potential of perivascular fat attenuation index (FAI) and coronary computed tomography angiography (CCTA) derived fractional flow reserve (CT-FFR) in the identification of culprit lesion leading to subsequent acute coronary syndrome (ACS). Thirty patients with documented ACS event who underwent invasive coronary angiography (ICA) from February 2019 to February 2021 and had received CCTA in the previous 6 months were collected retrospectively. 40 patients with stable angina pectoris (SAP) were matched as control group according to sex, age and risk factors. The study population has a mean age of 59.3 ± 12.3 years, with a male prevalence of 81.4%. The plaque characteristics, perivascular fat attenuation index (FAI), and coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) of 32 culprit lesions and 30 non-culprit lesions in ACS patients and 40 highest-grade stenosis lesions in SAP patients were statistically analyzed. FAI around culprit lesions was increased significantly (-72.4 ± 3.2 HU vs. -79.0 ± 7.7 HU, vs. -80.4 ± 7.0HU, all  < 0.001) and CT-FFR was decreased for culprit lesions of ACS patients [0.7(0.1) vs. 0.8(0.1), vs.0.8(0.1), < 0.001] compared to other lesions. According to multivariate analysis, diameter stenosis (DS), FAI, and CT-FFR were significant predictors for identification of the culprit lesion. The integration model of DS, FAI, and CT-FFR showed the significantly highest area under the curve (AUC) of 0.917, compared with other single predictors (all  < 0.05). This study proposes a novel integrated prediction model of DS, FAI, and CT-FFR that enhances the diagnostic accuracy of traditional CCTA for identifying culprit lesions that trigger ACS. Furthermore, this model also provides improved risk stratification for patients and offers valuable insights for predicting future cardiovascular events.