Prediction of ischemic stroke in elderly hypertensive patients using carotid plaque superb microvascular imaging characteristics: a lasso-logistic regression model.

Abstract

Purpose: This study aims to assess the effectiveness of Superb Microvascular Imaging (SMI) in evaluating intraplaque neovascularization (IPN) in carotid plaques and its association with ischemic stroke in elderly hypertensive patients, and to develop and validate a prediction model for ischemic stroke based on SMI characteristics.

Methods: This retrospective study included 314 elderly hypertensive patients with carotid plaques, divided into a training cohort (235 cases) and a validation cohort (79 cases). Patients were categorized into stroke and non-stroke groups. SMI characteristics of carotid plaques and baseline variables were analyzed using univariate logistic regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression to develop a multivariate logistic regression model. The model was then validated.

Results: In the training cohort, 79 patients (33.6%) experienced ischemic stroke. Significant predictors included BMI, hypertension grade, IPN grade, and stenosis percentage. These factors were incorporated into a logistic regression model, which was validated with an area under the curve (AUC) of 0.79, 69.6% accuracy, 60.8% sensitivity, and 85.7% specificity.

Conclusion: BMI, hypertension grade, IPN grade, and carotid plaque stenosis are associated with ischemic stroke in elderly hypertensive patients. The developed logistic regression model based on these indicators can improve the prediction of ischemic stroke in this population.