The aim was to determine the prevalence and risk factors for electrographic seizures and other electroencephalographic (EEG) patterns in patients with Coronavirus disease 2019 (COVID-19) undergoing clinically indicated continuous electroencephalogram (cEEG) monitoring and to assess whether EEG findings are associated with outcomes. Methods: We identified 197 patients with COVID-19 referred for cEEG at 9 participating centers. Medical records and EEG reports were reviewed retrospectively to determine the incidence of and clinical risk factors for seizures and other epileptiform patterns. Multivariate Cox proportional hazards analysis assessed the relationship between EEG patterns and clinical outcomes. Results: Electrographic seizures were detected in 19 (9.6%) patients, including nonconvulsive status epilepticus (NCSE) in 11 (5.6%). Epileptiform abnormalities (either ictal or interictal) were present in 96 (48.7%). Preceding clinical seizures during hospitalization were associated with both electrographic seizures (36.4% in those with vs 8.1% in those without prior clinical seizures, odds ratio [OR] 6.51, p = 0.01) and NCSE (27.3% vs 4.3%, OR 8.34, p = 0.01). A pre-existing intracranial lesion on neuroimaging was associated with NCSE (14.3% vs 3.7%; OR 4.33, p = 0.02). In multivariate analysis of outcomes, electrographic seizures were an independent predictor of in-hospital mortality (hazard ratio ], p < 0.01). In competing risks analysis, hospital length of stay increased in the presence of NCSE (30 day proportion discharged with vs without NCSE: HR 0.21 [0.03-0.33] vs 0.43 [0.36-0.49]). Interpretation: This multicenter retrospective cohort study demonstrates that seizures and other epileptiform abnormalities are common in patients with COVID-19 undergoing clinically indicated cEEG and are associated with adverse clinical outcomes.

New neurons are incorporated throughout life into the brains of many vertebrate and non-vertebrate species. This process of adult neurogenesis is regulated by a variety of external and endogenous factors, including environmental enrichment, which increases the production of neurons in juvenile mice and crayfish. The primary goal of the present study was to exploit the spatial separation of the neuronal precursor cell lineage in crayfish to determine which generation(s) of precursors is altered by environmental conditions. Further, in crayfish, an intimate relationship between the 1st generation neuronal precursors (stem cells) and cells circulating in the hemolymph has been proposed (Zhang et al., 2009). Therefore, a second goal was to assess whether environmental enrichment alters the numbers or types of cells circulating in the hemolymph. We find that neurogenesis in the brains of sexually differentiated procambarid crayfish is enhanced by environmental enrichment as previously demonstrated by Sandeman and Sandeman (2000) in young, sexually undifferentiated Cherax destructor. We also show that environmental enrichment increases the cell cycle rate of neuronal stem cells. While there was no effect of environment on the overall numbers of cells circulating in the hemolymph, enrichment resulted in increased expression of glutamine synthetase, a marker of the neuronal stem cells, in a small percentage of circulating cells; there was little or no expression of this enzyme in hemolymph cells extracted from deprived animals. Thus, environmental enrichment influences the rate of neuronal stem cell division in adult crayfish, as well as the composition of cells circulating in the hemolymph.

Objective: To evaluate the accuracy of ICD-10-CM claims-based definitions for epilepsy and classifying seizure types in the outpatient setting. Methods:We reviewed electronic health records (EHR) for a cohort of adults aged 18+ years seen by six neurologists who had an outpatient visit at a level 4 epilepsy center between 01/2019-09/2019. The neurologists used a standardized documentation template to capture the diagnosis of epilepsy (yes/no/unsure), seizure type (focal/generalized/unknown), and seizure frequency in the EHR. Using linked ICD-10-CM codes assigned by the provider, we assessed the accuracy of claims-based definitions for epilepsy, focal seizure type, and generalized seizure type against the reference-standard EHR documentation by estimating sensitivity (Sn), specificity (Sp), positive predictive value (PPV), and negative predictive value (NPV).