Abstract
BACKGROUND: Noncalcified coronary plaque is prone to rupture and may lead to major adverse cardiovascular events (MACEs). The pericoronary fat attenuation index (FAI), derived from coronary computed tomographic angiography (CCTA), is an emerging marker of coronary inflammation. This study aimed to assess the predictive value of the FAI for vulnerable plaque and prognosis in patients with coronary heart disease (CHD). METHODS: We retrospectively enrolled 453 patients with CHD who underwent CCTA and were followed for 3 years. Patients were divided into a MACE group (n=103) and a control group (n=350) based on the occurrence of MACEs. The FAI was measured using artificial intelligence software at the site of the most severe coronary stenosis. Clinical characteristics, coronary plaque burden, and FAI were compared between groups. Multivariate logistic regression identified independent predictors of MACEs, and a nomogram prediction model was developed and validated. RESULTS: Patients with MACEs had significantly higher age, greater total and noncalcified plaque burden, lower left ventricular ejection fraction (LVEF), higher FAI, and a greater prevalence of the multivessel disease. Independent predictors of MACEs included age ≥80 years [relative risk (RR) 12.39, 95% confident interval (CI): 5.75-26.69], LVEF <50% (RR 8.73, 95% CI: 4.10-18.58), total coronary plaque burden >33.3% (RR 4.27, 95% CI: 2.23-8.18), increased FAI (RR 1.08, 95% CI: 1.05-1.11), and multivessel disease (RR 3.14, 95% CI: 1.67-5.90) with all P<0.001. The nomogram model demonstrated strong predictive performance, with area under the curve (AUC) values of 0.920 and 0.862 in the training and validation sets, respectively. FAI was significantly correlated with noncalcified plaque burden (r=0.234, P<0.001). CONCLUSIONS: FAI is associated with coronary noncalcified plaque burden and is an independent predictor of MACEs in patients with CHD. A prediction model incorporating the FAI demonstrated promising efficacy in identifying high-risk patients, supporting its potential role in personalized risk stratification.