Quantitative plaque characterization, pericoronary fat attenuation index, and fractional flow reserve: a novel method for differentiating between stable and unstable angina pectoris in a case-control study.

Background: Accurate diagnosis of coronary artery disease is essential for preventing serious cardiovascular events. Although coronary computed tomography angiography (CCTA) is widely used in the clinic, it is limited because it only provides anatomical information, which makes differentiating in-depth between subtypes of noncalcified plaques and assessing the inflammatory state of coronary vessels difficult. Fractional flow reserve with computed tomography (FFR-CT) can be combined with CCTA to form a hybrid anatomic-physiologic diagnostic strategy. This study aimed to improve the recognition of stable and unstable angina with quantitative plaque characteristics, fat attenuation index (FAI), and fractional flow reserve with FFR-CT using a coronary artificial intelligence (AI)-assisted diagnostic system. Methods: In this retrospective case-control study, 215 and 202 patients with stable and unstable angina pectoris, respectively, who were treated at our hospital between January 2015 and August 2023, were enrolled. Propensity score matching was used to reduce clinical baseline data bias. Binary logistic regression was used to determine the risk factors for unstable angina pectoris. The diagnostic efficacy of quantitative plaque characteristics, pericoronary FAI, FFR-CT, and their combined models in differentiating stable and unstable angina pectoris was determined using the area under the receiver operating characteristic (ROC) curve. Results: This study included 168 pairs of patients with stable or unstable angina. Patients with unstable angina had a significantly greater pericoronary FAI volume and percentage of, lipid, and fibrolipid components within the total plaque (all P<0.001) and a significantly smaller percentage of calcification components (P<0.001), FFR-CT (P=0.003), and lumen area at the narrowest point of the stenosis(P=0.003) than those with stable angina. Independent risk factors for unstable angina were FAI >-82 Hounsfield units (HU) and total intraplaque lipid component percentage >1.2% (P=0.003 and 0.009, respectively). The area under the curve (AUC) of the ROC regarding pericoronary FAI differentiating between stable and unstable angina was 0.631 (P<0.001). In contrast, the AUC of the combined model of FFR-CT, plaque characteristics, and pericoronary FAI was 0.698 (P<0.001). The AUC value of the combined model was significantly higher than that of the diagnostic model using a single index (all, P<0.001). Conclusions: AI-assisted diagnostic systems could provide new methods to differentiate between stable and unstable angina. Patients with FAI >-82 HU and total intraplaque lipid component percentage >1.2% had a significantly increased risk of unstable angina, a finding that may be informative for clinical decision-making.