Characteristics of carotid plaques combined with total cerebral small vessel disease burden for predicting recurrence of Ischemic stroke

Objective To investigate the predictive value of carotid plaque characteristics combined with total cerebral small vessel disease (CSVD) burden for recurrent ischemic stroke. Methods This retrospective study included 337 patients with ischemic stroke and carotid plaques who were admitted between January 2021 and May 2025. Corresponding to the prevalence of ischemic stroke recurrence within one year of follow-up, stroke cases were categorized into recurrence (n = 135) and non-recurrence (n = 202) groups. Multivariate analysis performed with regression was used to recognize factors correlated with recurrent ischemic stroke. A prediction model using a nomogram was subsequently created on the basis of identified factors associated with risk, and its predictive performance was evaluated. Results The analysis based on multivariate logistic regression demonstrated that higher age, hypertension, elevated LDL-C, increased plaque thickness, irregular plaque surface, low-/mixed-echogenic plaques, and a higher total CSVD burden were independent factors associated with recurrent ischemic stroke (all P < 0.05). A nomogram model was constructed based on these predictors. The areas under the receiver operating characteristic curves (AUCs) were 0.918 (95% CI: 0.882–0.955) and 0.861 (95% CI: 0.790–0.932) in the training and validation cohorts, respectively. Calibration curves confirmed notable concurrence relating observed and predicted probabilities, while decision curve analysis suggested a potential net clinical benefit across a range of threshold probabilities. Conclusion The nomogram model incorporating age, hypertension, LDL-C, maximum plaque thickness, plaque surface, plaque echogenicity, and total CSVD burden showed promising discrimination for recurrent ischemic stroke, but limited sensitivity in the internal validation cohort at the selected cutoff. External validation and evaluation of clinically appropriate thresholds are needed before clinical implementation.