A model based on high-resolution magnetic resonance vessel wall imaging for predicting stroke recurrence in patients...

Due to its high sensitivity and specificity, high-resolution magnetic resonance vessel wall imaging (HRMR-VWI) and other adjunct tools with conventional angiography have been widely applied in the diagnosis of ischemic cerebrovascular disease. The purpose of this study was to construct a model based on HRMR-VWI indicators for predicting ischemic stroke recurrence in patients with symptomatic intracranial atherosclerosis (sICAS).

The data in this study were partly from patients with cerebral infarction or transient ischemic attack attending 18 hospitals (from January 2017 to December 2019) in the ICASMAP study (A Study on the Etiology of Intracranial Artery Stenosis and Atherosclerotic Plaque Prediction Based on High Resolution MRI Technology). The remaining data were from patients with cerebral infarction or transient ischemic attack who were hospitalized in the Hebei Medical University Third Hospital (from January 2020 to December 2023). A total of 540 patients with sICAS were enrolled, all of whom were ≥18 years old, had large arterial atherosclerotic lesions (30-99% stenotic lesions in at least one intracranial artery as confirmed by HRMR-VWI examination, with the stenotic lesions being responsible for the stroke), and experienced ischemic stroke within 2 weeks. The follow-up endpoint event was the recurrence of stroke. Follow-up was completed in 493 of the patients (493/540, 91.30%), the average follow-up time was 43 months, and 65 patients experienced ischemic stroke recurrence after treatment. The primary clinical features and HRMR-VWI parameters of patients with recurrence and no-recurrence were compared, and Cox regression analysis was used to explore the risk factors for stroke recurrence in patients with sICAS. R v. 4.0.3 statistical software was used to randomly select 246 cases for the training set based on the relevant risk factors, and the remaining 247 cases were used as the validation set to predict the outcome of stroke recurrence.

The risk factors for stroke recurrence in patients with sICAS were diabetes mellitus [relative risk (RR): 2.427; 95% confidence interval (CI): 1.402-4.199; P=0.002], poor medication compliance (RR: 3.403; 95% CI: 1.886-6.138; P<0.001), intraplaque bleeding (RR: 2.618; 95% CI: 1.493-4.589; P=0.001), and standardized wall index >0.555 (RR: 3.421; 95% CI: 1.986-5.894; P<0.001). The prediction model based on the relevant risk factors had good efficacy and reliability, with an area under the curve (AUC) of 0.846 (95% CI: 0.788-0.904) in the training set and an AUC of 0.702 (95% CI: 0.604-0.801) in the validation set.

HRMR-VWI is valuable for the prediction of ischemic stroke recurrence in patients with sICAS. The HRMR-VWI-based model for the prediction of stroke recurrence in patients with sICAS can facilitate the early diagnosis and treatment of stroke recurrence.