Carotid Plaque Neovascularization and EASIX Value Effects on Symptom Development in Patients with Carotid Artery Disease

Background: Carotid artery stenosis (CAS) is currently one of the leading causes of ischemic stroke. In these patients, the primary factor leading to stroke is the presence of vulnerable atherothrombotic plaques rather than the degree of stenosis itself. The assessment of intraplaque neovascularization (IPN) emerges as a significant parameter for stroke risk. Also, certain biomarkers have been investigated as predictors of unstable atheromatous plaque structures. Objective: Our current study aimed to investigate the role of IPN and the Endothelial Activation and Stress Index (EASIX) parameters in the development of symptoms among patients with CAS of 50% or greater. Subjects and Methods: Patients with unilateral internal carotid artery (ICA) stenosis of 50% or greater who underwent carotid artery Doppler ultrasonography (DUSG) at our clinic, between 20 January 2026 and 20 September 2026, were consecutively enrolled in this prospective descriptive study. Subsequently, the patients were divided into two groups: asymptomatic (Group 1) and symptomatic (Group 2). Following this classification, an analysis of potential risk factors influencing the development of symptoms was conducted. Results: A total of 394 patients were included in the study. Group 1 consisted of 309 patients with a mean age of 61.6 ± 10.9, while Group 2 consisted of 85 patients with a mean age of 62 ± 10 (p = 0.278). Glycated hemoglobin and EASIX values were statistically significantly higher in Group 2 compared to Group 1 (p = 0.022 and p =0.001, respectively). There was no statistically significant difference between the groups regarding the side of the lesion and the grading of stenosis. There was a significant difference between the groups in terms of the Gray-Weale–Geroulakos classification and IPN assessments (p < 0001). Conclusions: In this study, we determined that SMI—a novel DUSG technique—could predict high-risk plaque structures. We demonstrated that incorporating SMI assessment into the evaluation of plaque characteristics yields a more predictive model.