Several cases of myelin oligodendrocyte glycoprotein (MOG) antibody-associated encephalitis have been reported after coronavirus disease 2019 . In this case, the patient presented with focal status epilepticus with impaired awareness, auditory hallucinations, and incoherent speech after COVID-19. Brain magnetic resonance imaging revealed no specific findings. Cerebrospinal fluid results showed pleocytosis and MOG antibody testing confirmed anti-MOG antibody with live cell-based fluorescence-activated cell sorting assay. The patient was diagnosed with MOG antibody-associated autoimmune encephalitis and treated with intravenous immunoglobulin, rituximab, and tocilizumab. This case occurred presumably due to auto-antibody production following COVID-19.
When a patient with encephalopathy has an organic brain lesion, his symptom is easily and often mistakenly attributed to that brain lesion. However, a combination of different conditions is also possible. We present a case of autoimmune limbic encephalitis combined with leptomeningeal carcinomatosis. A 57-year-old female patient was transferred to our institute with a 1-month history of seizure and aggressive behavior. Subacute onset of psychosis with multifocal T2 high signal lesions suggested autoimmune encephalitis, and high-dose steroid pulse and immunoglobulin therapy were started. However, a cerebrospinal fluid study revealed metastatic adenocarcinoma of non-small cell lung cancer, of which she was in complete remission state. Osimertinib, a third-generation epidermal growth factor receptor tyrosine kinase inhibitor, was started targeting leptomeningeal metastases while maintaining immunotherapy of rituximab and tocilizumab. Her neurological symptoms showed improvement in response to immunotherapy which lasted approximately 1 month and then deteriorated again. We concluded that her symptoms were more attributable to autoimmune encephalitis than leptomeningeal carcinomatosis, and discontinued osimertinib.
Introduction Idiopathic Rapid eye movement (REM) sleep behavior disorder is a condition that can be an early sign of alpha-synuclein-mediated neurodegenerative diseases, and the course of the disease can vary greatly from patient to patient. It is important to identify patients who are at risk of developing neurodegenerative diseases in the future for the purpose of future clinical trials and for patients to plan their lives accordingly. Previous research has identified various risk factors for phenoconversion in RBD patients, but these studies are not practical for use in clinical settings due to resource availability or the rarity of certain features. Additionally, most of these studies have been conducted on non-Asian populations, which may have different genetic backgrounds than Asian populations. This study aimed to develop a machine learning model to predict survival in RBD patients using clinical features commonly available in routine clinical settings. Methods This study recruited patients diagnosed with RBD based on polysomnography results and collected 34 features for each patient. Missing data were imputed and various models were applied to the data to improve performance. The model's predictive performance was evaluated using an integrated Brier score and the concordance index. Mean performance indicators were calculated from 5-fold cross-validation results. A web application hosting the final prediction model was developed and deployed on a server for use by physicians or patients. Results 173 patients were included in the study. We used the likelihood ratio test to calculate the p-values of all variables and selected the following 8 variables with p-values less than 0.1: UPDRS part III, age, history of antidepressant use, history of alcohol use, MoCA (Montreal Cognitive Assessment), PSQI-TST (Pittsburgh Sleep Quality Index - total sleep time), AHI-REM (apnea-hypopnea index - REM sleep), and education level. The random survival forest model had the best mean IBS of 0.07 and the best C-index of 0.93 Conclusion We showed that it is possible for a machine learning model to predict phenoconversion in patients with RBD using features that are commonly available in routine clinical settings Support (if any)
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