Shear Stress Estimated by Quantitative Coronary Angiography Predicts Plaques Prone to Progress and Cause Events

This study examined the value of endothelial shear stress (ESS) estimated in 3-dimensional quantitative coronary angiography (3D-QCA) models in detecting plaques that are likely to progress and cause events.

Cumulative evidence has shown that plaque characteristics and ESS derived from intravascular ultrasound (IVUS)-based reconstructions enable prediction of lesions that will cause cardiovascular events. However, the prognostic value of ESS estimated by 3D-QCA in nonflow limiting lesions is yet unclear.

This study analyzed baseline virtual histology (VH)-IVUS and angiographic data from 28 lipid-rich lesions (i.e., fibroatheromas) that caused major adverse cardiovascular events or required revascularization (MACE-R) at 5-year follow-up and 119 lipid-rich plaques from a control group that remained quiescent. The segments studied by VH-IVUS at baseline were reconstructed using 3D-QCA software. In the obtained geometries, blood flow simulation was performed, and the pressure gradient across the lipid-rich plaque and the mean ESS values in 3-mm segments were estimated. The additive value of these hemodynamic indexes in predicting MACE-R beyond plaque characteristics was examined.

MACE-R lesions were longer, had smaller minimum lumen area, increased plaque burden (PB), were exposed to higher ESS, and exhibited a higher pressure gradient. In multivariable analysis, PB (hazard ratio: 1.08; p = 0.004) and the maximum 3-mm ESS value (hazard ratio: 1.11; p = 0.001) were independent predictors of MACE-R. Lesions exposed to high ESS (>4.95 Pa) with a high-risk anatomy (minimal lumen area <4 mm² and PB >70%) had a higher MACE-R rate (53.8%) than those with a low-risk anatomy exposed to high ESS (31.6%) or those exposed to low ESS who had high- (20.0%) or low-risk anatomy (7.1%; p < 0.001).

In the present study, 3D-QCA-derived local hemodynamic variables provided useful prognostic information, and, in combination with lesion anatomy, enabled more accurate identification of MACE-R lesions.