Enhancing Coronary Revascularization Prediction: Insights From Fat Attenuation Index (FAI) of Pericoronary Adipose Tissue and CT-derived Fractional Flow Reserve (CT-FFR).

Abstract

Purpose: This study aimed to evaluate the clinical value of the fat attenuation index (FAI) of pericoronary adipose tissue (PCAT) and fractional flow reserve derived from coronary computed tomography angiography (CT-FFR) in predicting coronary revascularization.

Methods: Patients with known or suspected acute coronary syndrome (ACS) who underwent coronary computed tomography angiography (CCTA) and subsequent invasive coronary angiography (ICA) were screened. FAI, lesion-specific CT-FFR, and distal-tip CT-FFR were analyzed by core laboratories blinded to patient management. Per-vessel and per-patient logistic univariable and multivariable analyses were performed to predict revascularization. Three multivariable logistic regression models were compared, with ROC curves generated for each model and AUCs compared. Incremental predictive value between models 2 and 3 was also measured using continuous net reclassification improvement (NRI).

Results: A total of 94 patients who received CCTA followed by ICA were identified and analyzed; 282 vessels were included. Overall, 54 (57.4%) patients with 72 (25.5%) vessels underwent revascularization. Lesion-specific CT-FFR, FAI, and significant stenosis were significantly associated with revascularization in both univariable and multivariable analyses. Lesion-specific CT-FFR, FAI, and significant stenosis were independent predictors of coronary revascularization. In the per-vessel analysis, those with 2 or 3 risk factors had a markedly higher revascularization rate [50 of 69 (72.5%) vs. 22 of 213 (10.3%); P < 0.001]. In the per-patient analysis, those with 2 or 3 risk factors had a markedly higher revascularization rate [35 of 42 (83.3%) vs. 19 of 52 (36.5%); P < 0.001]. The continuous net reclassification improvement (NRI) for the addition of FAI and CT-FFR to standard CCTA analysis (model 3 over model 2) was 0.273 (95% CI, 0.166-0.379, P < 0.0001).

Conclusions: This study demonstrated the application value of CT-FFR and FAI in predicting coronary revascularization in patients with documented ACS. CT-FFR and FAI obtained from quantitative CCTA improved the prediction of future revascularization. These parameters can potentially identify patients likely to receive revascularization upon referral for cardiac catheterization. However, the clinical use of FAI may be limited by the lack of standardization in PCAT values and the absence of a clear established cutoff for clinical relevance.