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