Refractory Status Epilepticus in a Patient With Aducanumab-Induced Amyloid-Related Imaging Abnormalities.

OBJECTIVE: To report a case of fatal super-refractory status epilepticus associated with amyloid-related imaging abnormalities (ARIA). METHODS: We describe the history, neuroimaging, EEG, and brain pathology findings of a 75-year-old patient with mild cognitive impairment due to Alzheimer disease (homozygous ε4 apolipoprotein status) and a remote history of 3 asymptomatic ARIA episodes, who developed super-refractory status epilepticus related to severe ARIA. RESULTS: The patient was participating in an extended open-label trial of aducanumab when she was admitted to hospital for focal seizures and ARIA in 2 noncontiguous regions of the left frontal and occipital lobes. Despite aggressive treatment with high-dose corticosteroids, sedation, and antiseizure medications, she died from refractory focal status epilepticus. In retrospect, routine surveillance brain magnetic resonance imaging performed 11 weeks before hospitalization had signs of ARIA, which had not been identified. DISCUSSION: Clinicians should be aware that anti-amyloid therapies may cause rare serious adverse events. A high degree of vigilance is required in the interpretation of surveillance imaging for ARIA. Longitudinal studies are justified to further characterize the safety profile of anti-amyloid antibody therapies and identify participants at high risk of serious adverse events.

Computer-assisted analysis of routine EEG to identify hidden biomarkers of epilepsy: A systematic review.

Background: Computational analysis of routine electroencephalogram (rEEG) could improve the accuracy of epilepsy diagnosis. We aim to systematically assess the diagnostic performances of computed biomarkers for epilepsy in individuals undergoing rEEG. Methods: We searched MEDLINE, EMBASE, EBM reviews, IEEE Explore and the grey literature for studies published between January 1961 and December 2022. We included studies reporting a computational method to diagnose epilepsy based on rEEG without relying on the identification of interictal epileptiform discharges or seizures. Diagnosis of epilepsy as per a treating physician was the reference standard. We assessed the risk of bias using an adapted QUADAS-2 tool. Results: We screened 10 166 studies, and 37 were included. The sample size ranged from 8 to 192 (mean=54). The computed biomarkers were based on linear (43%), non-linear (27%), connectivity (38%), and convolutional neural networks (10%) models. The risk of bias was high or unclear in all studies, more commonly from spectrum effect and data leakage. Diagnostic accuracy ranged between 64% and 100%. We observed high methodological heterogeneity, preventing pooling of accuracy measures. Conclusion: The current literature provides insufficient evidence to reliably assess the diagnostic yield of computational analysis of rEEG. Significance: We provide guidelines regarding patient selection, reference standard, algorithms, and performance validation.