Predictive Value of Pericoronary Adipose Tissue Attenuation Index and Left Main Coronary Artery Angle for High-Risk Plaques in Patients with Left-Dominant Coronary Artery Disease

Why It Matters

Identifying reliable, non‑invasive imaging biomarkers enables earlier intervention and personalized management of high‑risk coronary lesions, potentially reducing adverse cardiac events.

Key Takeaways

• FAI and LMCA angle independently predict high-risk plaques
• Combined model AUC 0.791 exceeds individual markers
• Study focused on left‑dominant coronary anatomy
• Age differences noted between plaque risk groups
• CCTA provides quantitative plaque risk assessment

Pulse Analysis

Coronary artery disease remains the leading cause of mortality worldwide, and left‑dominant anatomy—where the left coronary system supplies the majority of the myocardium—poses unique diagnostic challenges. Recent advances in coronary CT angiography (CCTA) have enabled quantitative assessment of pericoronary adipose tissue, captured as the fat attenuation index (FAI), which reflects local inflammation. Simultaneously, geometric parameters such as the left main coronary artery (LMCA) angle influence shear stress and plaque development, making them attractive imaging biomarkers for clinicians seeking to pinpoint vulnerable lesions without invasive procedures.

In a single‑center retrospective analysis of 106 patients, researchers demonstrated that both elevated FAI and wider LMCA angles were significantly associated with high‑risk plaques in the proximal left anterior descending artery. Multivariate logistic regression confirmed their independent predictive value, while receiver operating characteristic curves revealed that the combined FAI‑LMCA model achieved an area under the curve of 0.791—substantially higher than the 0.729 and 0.698 scores of each metric alone. These statistics underscore the synergistic benefit of merging tissue‑level inflammation data with coronary geometry to refine plaque risk stratification.

The implications extend beyond academic interest; integrating FAI and LMCA angle into routine CCTA protocols could transform clinical pathways by flagging patients who merit aggressive medical therapy or closer surveillance. As artificial intelligence tools mature, automated extraction of these parameters may become standard, facilitating large‑scale screening and personalized treatment plans. Nonetheless, prospective multicenter validation and cost‑effectiveness analyses are essential before widespread adoption, ensuring that the promise of enhanced diagnostic performance translates into tangible cardiovascular outcomes.