Detection of Asymptomatic Carotid Artery Stenosis in High-Risk Individuals of Stroke Using a Machine-Learning Algorithm.

Objective Asymptomatic carotid stenosis (ACS) is closely associated to the incidence of severe cerebrovascular diseases. Early identifying the individuals with ACS and its associated risk factors could be beneficial for primary prevention of stroke. This study aimed to investigate a machine-learning algorithm for the detection of ACS among high-risk population of stroke based on the associated risk factors.Methods A novel model of machine learning was utilized to screen the associated predictors of ACS based on 30 potential risk factors. The algorithm of this model adopted a random forest pattern based on the training data and then was verified using the testing data. All of the original data were retrieved from the China National Stroke Screening and Prevention Project (CNSSPP), including demographic, clinical and laboratory characteristics. The individuals with high risk of stroke were enrolled and randomly divided into a training group and a testing group at a ratio of 4:1. The identification of carotid stenosis by carotid artery duplex scans was set as the golden standard. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) was used to evaluate the efficacy of the model in detecting ACS.Results Of 2841 high risk individual of stroke enrolled, 326 (11.6%) were diagnosed as ACS by ultrasonography. The top five risk factors contributing to ACS in this model were identified as family history of dyslipidemia, high level of low-density lipoprotein cholesterol (LDL-c), low level of high-density lipoprotein cholesterol (HDL-c), aging, and low body mass index (BMI). Their weights were 11.8%, 7.6%, 7.1%, 6.1%, and 6.1%, respectively. The total weight of the top 15 risk factors was 85.5%. The AUC values of the model for detecting ACS with training dataset and testing dataset were 0.927 and 0.888, respectively.Conclusions This study demonstrated that the machine-learning algorithm could be used to identify the risk factors for ACS among high risk population of stroke. Family history of dyslipidemia may be the most important risk factor for ACS. This model could be a suitable tool to optimize the clinical approach for the primary prevention of stroke.

Brain astrocytoma misdiagnosed as anti-NMDAR encephalitis: a case report.

BACKGROUND: Anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis, which is the most common type of autoimmune encephalitis, is caused by the production of autoantibodies against NMDA receptor. Anti-NMDAR encephalitis patients present with various non-specific symptoms, such as abnormal psychiatric or behaviour, speech dysfunction, cognitive dysfunction, seizures, movement disorders, decreased level of consciousness, and central hypoventilation or autonomic dysfunction. CASE PRESENTATION: A 67-year-old man presented with new-onset focal seizures. The brain magnetic resonance imaging (MRI) plain scan and enhanced scan showed abnormal signal on the proximal midline frontoparietal junction region. Anti-NMDAR antibody was detected in cerebrospinal fluid (CSF) and serum using a commercial kit (Euroimmune, Germany) by indirect immunofluorescence testing (IIFT) according to the manufacturer's instructions for twice. Both of the test results were positive in CSF and serum. The patient was diagnosed as anti-NMDAR encephalitis and then was treated repeatedly with large dose of intravenous corticosteroids and gamma globulin. Accordingly, the refractory nature of seizures in this case may be attributed to NMDAR autoantibodies. When the patient presented at the hospital for the third time, the brain MRI revealed an increase in the size of the frontal parietal lesion and one new lesion in the left basal ganglia. The patient underwent a surgical biopsy and astrocytoma was confirmed by histopathology. CONCLUSIONS: Although the sensitivity and specificity of anti-NMDAR-IgG antibodies in CSF to diagnose anti-NMDAR encephalitis are close to 100%, it is not absolute. Anti-NMDAR antibodies were positive, which might make the diagnosis more complex. The diagnosis of atypical presentation of anti-NMDAR encephalitis requires reasonable exclusion of other disorders.