Correlation between pre-stroke cognitive impairment and MRI markers in patients with minor acute ischemic stroke
10.3760/cma.j.issn.1673-4165.2022.04.005
- VernacularTitle:轻型急性缺血性卒中患者卒中前认知损害与MRI征象的相关性
- Author:
Xia CHEN
1
;
Yingge WANG
;
Tieyu TANG
;
Zhaocai JIANG
;
Xinjiang ZHANG
Author Information
1. 扬州大学附属医院神经内科，扬州 225003
- Keywords:
Stroke;
Brain ischemia;
Cognition disorders;
Magnetic resonance imaging;
Cerebral small vessel diseases;
Risk factors
- From:
International Journal of Cerebrovascular Diseases
2022;30(4):268-274
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the correlation between MRI markers of neurodegenerative diseases and vascular diseases and pre-stroke cognitive impairment (PSCI).Methods:Patients with minor acute ischemic stroke at first onset and aged ≥60 years admitted to the Department of Neurology, the Affiliated Hospital of Yangzhou University and the Department of Neurology, Linyi Jinluo Hospital from March 2019 to December 2021 were retrospectively enrolled. The imaging markers of cerebral small vessel disease and neurodegeneration were analyzed by dichotomy visual score. The former included cerebral white matter hyperintensities, vasogenic lacunar lesions, cerebral microbleeds, and enlarged perivascular space, and the latter included global cortical atrophy and medial temporal lobe atrophy. According to the score of Information Questionnaire on Cognitive Decline in the Elderly (IQCODE), the patients were divided into PSCI group (≥3.31 points) and non-PSCI group (<3.31 points). The clinical baseline data and MRI markers of both groups were compared. Multivariate logistic regression model was used to analyze the correlation between MRI markers and PSCI, and receiver operator characteristic (ROC) curve was used to analyze the predictive value of MRI markers to PSCI. Results:A total of 221 patients were enrolled in the study, including 77 patients (34.8%) in the PSCI group and 144 (65.2%) in the non-PSCI group. Univariate analysis showed that there were significant differences in age, years of education, pathological white matter hyperintensities, medial temporal lobe atrophy, and the proportion of patients with ≥1 abnormal MRI markers between the two groups (all P<0.05). Multivariate logistic regression analysis showed that older age (odds ratio [ OR] 1.089, 95% confidence interval [ CI] 1.034-1.146; P=0.001), years of education <6 years ( OR 3.134, 95% CI 1.534-6.401; P=0.002), medial temporal lobe atrophy ( OR 2.911, 95% CI 1.385-6.121; P=0.005), and presence of ≥1 abnormal MRI markers ( OR 2.823, 95% CI 1.305-5.938; P=0.007) were the independent risk factors for PSCI. ROC curve analysis showed that the area under the curve of medial temporal lobe atrophy and the presence of ≥1 abnormal MRI markers for predicting PSCI were both smaller (0.595 and 0.584 respectively), but the area under the curve was the largest when the two and years of education were combined (0.818, 95% CI 0.756-0.880; P<0.001), and its sensitivity and specificity for predicting PSCI were 79.9% and 71.4% respectively. Conclusions:The incidence of PSCI is high. Medial temporal lobe atrophy combined with other abnormal MRI markers has a certain predictive value for PSCI.
