Assessing the Prognostic Significance of Ultrasound - Detected Carotid Plaque Morphology in Predicting Ischemic...

This study aimed to evaluate the predictive value of carotid plaque characteristics for ischemic stroke risk in postmenopausal women, and to develop a predictive model based on these features.

A retrospective analysis of clinical and carotid ultrasound data was performed on 145 postmenopausal women admitted to our hospital between January and December 2023. Patients were divided into stroke (n = 23) and non-stroke (n = 122) groups based on ischemic stroke occurrence during follow-up. Carotid plaque characteristics (location, number, echogenicity, surface, internal structure, and calcification) were assessed using color Doppler ultrasound. Cox regression analysis was used to examine plaque morphology's association with ischemic stroke risk. A prediction model incorporating plaque characteristics and traditional risk factors (age, hypertension, diabetes, smoking, dyslipidemia) was developed and evaluated using receiver operating characteristic curve analysis.

Cox regression analysis revealed several plaque characteristics as independent risk factors for ischemic stroke. These included hypoechoic plaques (hazard ratio (HR) = 2.16), irregular surfaces (HR = 1.84), ulcerated plaques (HR = 3.25), and heterogeneous internal structures (HR = 1.92). Additionally, plaques located in the internal carotid artery (HR = 2.31) and the presence of multiple plaques (≥3) (HR = 1.86) were significant stroke risk factors. The predictive model combining these plaque features with traditional risk factors demonstrated superior accuracy (area under the curve (AUC) = 0.87) compared to models based solely on traditional risk factors (AUC = 0.73, P = 0.008). Stratification using the prediction model identified low, moderate, and high-risk groups, with stroke incidence highest in the high-risk group (35.9%) compared to moderate (12.1%) and low-risk (4.2%) groups.

Carotid plaque morphology is a significant predictor of ischemic stroke in postmenopausal women. Including plaque characteristics in risk assessments improves predictive accuracy, aiding in early identification and personalized prevention strategies.