Background: Pericoronary adipose tissue attenuation expressed by fat attenuation index (FAI) on coronary CT angiography (CCTA) reflects pericoronary inflammation and is associated with cardiac mortality.
Objective: The aim of this study was to define the sub-phenotypes of coronary CCTA-defined plaque and whole vessel quantification by unsupervised machine learning (ML) and its prognostic impact when combined with pericoronary inflammation.
Methods: A total of 220 left anterior descending arteries (LAD) with intermediate stenosis who underwent fractional flow reserve (FFR) measurement and CCTA were studied. After removal of outcome and FAI data, the phenotype heterogeneity of CCTA-defined plaque and whole vessel quantification was investigated by unsupervised hierarchical clustering analysis based on Ward's method. Detailed features of CCTA findings were assessed according to the clusters (CS1 and CS2). Major adverse cardiac events (MACE)-free survivals were assessed according to the stratifications by FAI and the clusters.
Results: Compared with CS2 (n = 119), CS1 (n = 101) were characterized by greater vessel size, increased plaque volume, and high-risk plaque features. FAI was significantly higher in CS1. ROC analyses revealed that best cut-off value of FAI to predict MACE was -73.1. Kaplan-Meier analysis revealed that lesions with FAI ≥ -73.1 had a significantly higher risk of MACE. Multivariate Cox proportional hazards regression analysis revealed that age, FAI ≥ -73.1, and the clusters were independent predictors of MACE.
Conclusion: Unsupervised hierarchical clustering analysis revealed two distinct CCTA-defined subgroups and discriminated by high-risk plaque features and increased FAI. The risk of MACE differs significantly according to the increased FAI and ML-defined clusters.
Keywords: Coronary CT angiography; Coronary artery disease; Fractional flow reserve; Inflammation; Machine learning.
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