Assessment of coronary plaque progression using the plaque-specific pericoronary fat attenuation index and fractional flow reserve.

This study evaluated the plaque-specific fat attenuation index (FAI), a patient-level measure representing the weighted average FAI across all detected coronary plaques, and computed tomography fractional flow reserve (CT-FFR) for predicting coronary plaque progression (PP). This retrospective analysis included 1560 patients with suspected coronary artery disease who underwent baseline and follow-up coronary computed tomography angiography (CCTA) during January 2013-May 2023. Annual change in total plaque burden (TPB) was calculated using serial CCTA. PP was defined as ΔTPB/y greater than or equal to the median value. Univariate and multivariate logistic regression analyses identified independent PP predictors that were incorporated into an integrated prediction model. Prognostic performance of the plaque-specific FAI and CT-FFR was evaluated within this framework. In total, 380 patients [median age 61 (range: 54, 67) years; 253 males] were analyzed and equally assigned to progression and nonprogression groups. Independent predictors for PP included Δplaque-specific CT-FFR [odds ratio (95% confidence interval) = 0.879 (0.827-0.930), p < 0.001], vessel-specific CT-FFR [0.918 (0.881-0.955), p < 0.001], Δplaque-specific FAI [1.041 (1.009-1.073), p = 0.012], FAI-right coronary artery [1.033 (1.002-1.064), p = 0.034], statins [0.531 (0.331-0.852), p = 0.009], calcified volume [0.995 (0.991-0.998), p = 0.004], and plaque length [1.021 (1.009-1.033), p < 0.001]. The integrated model achieved an area under the receiver operating characteristics curve value of 0.792 (p < 0.001), significantly outperforming individual predictors (all p < 0.001, DeLong test). Overall, combining plaque-specific FAI, CT-FFR, and CCTA-derived quantitative plaque parameters improved risk stratification for PP.