Risk factors for acute ischemic stroke of small vessel occlusion in elderly patients with cerebral small vessel disease and establishment of a prediction model

Abstract

Cerebral small vessel disease (CSVD) accounts for 25% of ischemic stroke causes, yet risk factors for acute ischemic stroke (AIS) in elderly CSVD patients remain unclear. This study aimed to identify these risk factors and establish a prediction model. This single-center retrospective study enrolled 452 elderly CSVD patients (training cohort) and 59 patients (validation cohort). The total CSVD burden score was calculated based on MRI features. Logistic regression was used to identify independent risk factors and develop prediction models. Among 452 patients, 52 (11.5%) developed AIS. The total CSVD burden score (OR = 3.718, 95% CI: 1.971–7.011) was an independent risk factor. Carotid plaque thickness was significantly greater in AIS patients (1.879 ± 1.543 mm vs. 1.237 ± 1.246 mm, P = 0.006). In the training set, the parsimonious Model 2 achieved an apparent AUC of 0.875 (95% CI: 0.812 –0.938 ), with a bootstrap-corrected AUC of 0.867, sensitivity of 92.7%, and specificity of 84.2%. In the validation set, Model 2 exhibited excellent performance with an AUC of 0.969 (95% CI: 0.917–1.000), sensitivity of 100%, and specificity of 85.7%. The total CSVD burden score, smoking and carotid plaque thickness are independent predictors of AIS in elderly CSVD patients. Integrating imaging burden with modifiable risk factors provides excellent predictive accuracy for early identification of high-risk individuals. Graphical Abstract Schematic summary of the study design, key predictors, model performance, and clinical implications. The parsimonious model comprising CSVD burden score, smoking, and carotid plaque thickness demonstrated excellent predictive accuracy with minimal overfitting (bootstrap-corrected AUC = 0.867) and achieved an AUC of 0.969 in the validation set, supporting its potential utility for early risk stratification in elderly CSVD patients.