Résumé
Objective:To investigate the clinical significance of fluid-attenuated inversion recovery (FLAIR) vascular hyperintensities (FVHs) in patients with chronic atherosclerotic middle cerebral artery occlusion.Methods:From July 2016 to November 2020, patients admitted to the Department of Neurology, Jiangdu People's Hospital of Yangzhou and first found chronic atherosclerotic middle cerebral artery occlusion were enrolled. The demographic, clinical and MRI imaging data were collected. According to the presence or absence of acute cerebral infarction, they were divided into the non-acute cerebral infarction group and the acute cerebral infarction group. According to the modified Rankin Scale score at 3 months after the onset, patients with acute cerebral infarction were further divided into the good outcome group (≤2) and the poor outcome group (>2). A multivariate logistic regression model was used to analyze the independent correlation between FVHs and the risk of cerebral infarction in patients with chronic atherosclerotic middle cerebral artery occlusion and the outcome in patients with cerebral infarction. Results:A total of 94 patients with chronic atherosclerotic middle cerebral artery occlusion were enrolled, including 38 with non-acute cerebral infarction (40.4%) and 56 with acute cerebral infarction (59.6%). In patients with acute cerebral infarction, 13 (23.2%) had a poor outcome, and 43 (76.8%) had a good outcome. The fibrinogen level, the proportion of patients with FVHs and the FVHs score in the cerebral infarction group were significantly higher than those in the non-cerebral infarction group (all P<0.05). Multivariate logistic regression analysis showed that the FVHs score was significantly, independently, and positively correlated with the risk of acute cerebral infarction (odds ratio 2.524, 95% confidence interval 1.400-4.552; P=0.002). The National Institutes of Health Stroke Scale score at admission, the modified Rankin Scale score at admission, and FVHs score in the poor outcome group were significantly higher than those in the good outcome group (all P<0.05). Multivariate logistic regression analysis showed that there was a significant independent negative correlation between the FVHs score and the outcome of patients with cerebral infarction (odds ratio 0.144, 95% confidence interval 0.045-0.459; P=0.001). Conclusions:FVHs suggest that the blood supply is in a state of decompensation. When FVHs are present, the risk of cerebral infarction in patients with chronic middle cerebral artery occlusion is significantly increased; the wider the range of FVHs, the higher the risk of poor outcome after cerebral infarction.
Résumé
As a common disease in nervous system, epilepsy is possessed of characteristics of high incidence, suddenness and recurrent seizures. Timely prediction with corresponding rescues and treatments can be regarded as effective countermeasure to epilepsy emergencies, while most accidental injuries can thus be avoided. Currently, how to use electroencephalogram (EEG) signals to predict seizure is becoming a highlight topic in epilepsy researches. In spite of significant progress that made, more efforts are still to be made before clinical applications. This paper reviews past epilepsy studies, including research records and critical technologies. Contributions of machine learning (ML) and deep learning (DL) on seizure predictions have been emphasized. Since feature selection and model generalization limit prediction ratings of conventional ML measures, DL based seizure predictions predominate future epilepsy studies. Consequently, more exploration may be vitally important for promoting clinical applications of epileptic seizure prediction.