Frontiers in Neurology (Sep 2023)
Predicting acute ischemic stroke using the revised Framingham stroke risk profile and multimodal magnetic resonance imaging
Abstract
Background and purposePatients with transient ischemic attacks (TIA) have a significant risk of developing acute ischemic strokes (AIS), emphasizing the critical need for hierarchical management. This study aims to develop a clinical-imaging model utilizing multimodal magnetic resonance imaging (mMRI) and the revised Framingham Stroke Risk Profile (FSRP) to predict AIS and achieve early secondary prevention.MethodsmMRI scans were conducted on patients with symptomatic intracranial atherosclerotic disease (ICAD) to assess vascular wall features and cerebral perfusion parameters. Based on diffusion-weighted imaging (DWI), patients were divided into two groups: TIA and AIS. Clinical data were evaluated to calculate the FSRP score. Differences in clinical and imaging characteristics between the groups were analyzed, and a predictive model for AIS probability in patients with ICAD was established.ResultsA total of 112 TIA and AIS patients were included in the study. The results showed that the AIS group had higher proportions of FSRP-high risk, hyperhomocysteinemia, and higher value of low-density lipoprotein (LDL), standardized plaque index (SQI), and enhancement rate (ER) compared to the TIA group (p < 0.05). Mean transit time (MTT) and time to peak (TTP) in the lesion area were significantly longer in the AIS group (p < 0.05). Multivariate analysis identified FSRP-high risk (p = 0.027) and high ER (p = 0.046) as independent risk factors for AIS. The combined clinical and mMRI model produced an area under the curve (AUC) of 0.791 in receiver operating characteristic (ROC) analysis. The constructed nomogram model combining clinical and mMRI features demonstrated favorable clinical net benefits.ConclusionFSRP-high risk and high ER were confirmed as independent risk factors for AIS. The combined prediction model utilizing clinical and imaging markers effectively predicts stroke risk in symptomatic ICAD patients.
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