Noninvasive Quantitative Plaque Analysis Identifies Hemodynamically Significant Coronary Arteries Disease

Abstract

Objective: To evaluate the diagnostic performance of automated quantitative analysis by coronary computed tomography angiography (CCTA) in identifying lesion-specific hemodynamic abnormality.

Methods: A total of 132 patients (mean age, 61 y; 86 men) with 169 vessels (with 30% to 90% diameter stenosis), who successively underwent invasive coronary angiography with evaluation of fractional flow reserve (values ≤0.8 were defined as lesion-specific hemodynamic abnormalities), were analyzed by CCTA. CCTA images were quantitatively analyzed using automated software to obtain the following index: maximum diameter stenosis (MDS%); maximum area stenosis (MAS%); lesion length (LL); volume and burden (plaque volume×100 per vessel volume) of total plaque (total plaque volume [TPV], total plaque burden [TPB]), calcified plaque (calcified plaque volume [CPV], calcified plaque volume burden [CPB]), noncalcified plaque (noncalcified plaque volume [NCPV], noncalcified plaque volume burden [NCPB]), lipid plaque (lipid plaque volume [LPV], lipid plaque burden [LPB]), and fibrous plaque (fibrotic plaque volume [FPV], fibrotic plaque burden [FPB]); napkin-ring sign (NRS); remodeling index (RI); and eccentric index (EI). Logistic regression and area under the receiver operating characteristics (AUC) were used for statistical analysis.

Results: Fractional flow reserve ≤0.80 was found in 57 (33.73%) of the 169 vessels. Vessels with hemodynamic significance had greater MDS% (64.43%±8.69% vs. 57.33%±9.95%, P<0.001), MAS% (73.18%±8.56% vs. 64.66%±8.95%, P<0.001), and lipid plaque burden (12.75% [9.73%, 19.56%] vs. 9.41% [4.10%, 15.70%], P=0.01) compared with vessels with normal hemodynamics. In multivariable logistic regression analysis, MAS% >68% (odds ratio: 7.20, 95% confidence interval [CI]=2.89-17.91, P<0.001) and LPB >10.03% (odds ratio=4.32, 95% CI=1.36-13.66, P=0.01) were significant predictors of hemodynamic abnormalities. In predicting lesion-specific hemodynamic abnormalities, the AUC was 0.77 (95% CI=0.70-0.85) for MAS% versus 0.71 (95% CI=0.63-0.79) for MDS% (P<0.05), 0.66 (95% CI=0.58-0.74) for LPV (P<0.05), 0.66 (95% CI=0.58-0.74) for LPB (P<0.05), and 0.63 (95% CI=0.54-0.71) for TPB (P<0.05). The AUC of MAS%+LPB (0.83, 95% CI=0.76-0.89) was significantly improved compared with that of MAS% (0.77, 95% CI=0.70-0.85, P<0.05).

Conclusions: Compared with MDS% and the volume burdens of plaque compositions, MAS% has a higher diagnostic accuracy for coronary hemodynamic abnormalities in the precise quantitative analysis of coronary plaques on the basis of CT. Furthermore, MAS%+LPB might improve the diagnostic accuracy beyond MAS% alone.