Multimodal Risk Stratification Clue of Carotid Bifurcation Plaques: A Cross-Sectional Observational Study of Vector Flow Imaging, Plaque-RADS, and Clinical Factors.

This single-center cross-sectional study quantified segmental hemodynamic parameters of carotid bifurcation plaques by integrating high-frame-rate Vector Flow (V-Flow) imaging, Plaque-Reporting and Data System (Plaque-RADS) grading and clinical factors, to identify determinants of symptomatic status and enhance risk stratification of carotid atherosclerosis. From Feb-Aug 2025, 160 consecutive patients with carotid bifurcation plaques were enrolled. Symptomatic patients had ipsilateral transient ischemic attack/ischemic stroke within 180 days. B-mode ultrasound and V-Flow quantified wall shear stress (WSS), oscillatory shear index (OSI), and time-averaged turbulence intensity (TATur) at proximal/middle/distal plaque segments; plaques were Plaque-RADS-graded. Associations with symptomatic status were analyzed via multivariable logistic regression. Model performance was evaluated using ROC curve analysis and decision curve analysis; interobserver reproducibility was assessed using Bland-Altman analysis. Compared with the asymptomatic group patients (n = 98), symptomatic patients (n = 62) were older and had higher prevalence of hypertension, coronary heart disease, elevated triglycerides, and greater plaque length/thickness. They exhibited more Plaque-RADS grades 3-4, higher OSI/TATur at middle segments, and increased WSSmax/WSSmean, OSI, and TATur at distal segments. Multivariable analysis identified triglycerides, plaque length, TATur-Mid, WSSmax-Distal, and Plaque-RADS as independent predictors. The combined model (clinical + Plaque-RADS + V-Flow) achieved the highest discrimination (AUC = 0.826) and outperformed the base model. V-Flow-derived hemodynamic metrics, particularly OSI and TATur in middle/distal plaque segments, are strongly associated with symptomatic carotid disease. Combined with Plaque-RADS and clinical factors, these parameters enhance prediction of high-risk plaques, support individualized risk stratification, and may help identify patients who could benefit from closer surveillance.