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  1. Abstract Background

    Four severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants predominated in the United States since 2021. Understanding disease severity related to different SARS-CoV-2 variants remains limited.

    Method

    Viral genome analysis was performed on SARS-CoV-2 clinical isolates circulating March 2021 through March 2022 in Cleveland, Ohio. Major variants were correlated with disease severity and patient outcomes.

    Results

    In total 2779 patients identified with either Alpha (n = 1153), Gamma (n = 122), Delta (n = 808), or Omicron variants (n = 696) were selected for analysis. No difference in frequency of hospitalization, intensive care unit (ICU) admission, and death were found among Alpha, Gamma, and Delta variants. However, patients with Omicron infection were significantly less likely to be admitted to the hospital, require oxygen, or admission to the ICU (χ2 = 12.8, P < .001; χ2 = 21.6, P < .002; χ2 = 9.6, P = .01, respectively). In patients whose vaccination status was known, a substantial number had breakthrough infections with Delta or Omicron variants (218/808 [26.9%] and 513/696 [73.7%], respectively). In breakthrough infections, hospitalization rate was similar regardless of variant by multivariate analysis. No difference in disease severity was identified between Omicron subvariants BA.1 and BA.2.

    Conclusions

    Disease severity associated with Alpha, Gamma, and Delta variants is comparable while Omicron infections are significantly less severe. Breakthrough disease is significantly more common in patients with Omicron infection.

     
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  3. Abstract Objective

    Anterior temporal lobectomy (ATL) is a widely performed and successful intervention for drug‐resistant temporal lobe epilepsy (TLE). However, up to one third of patients experience seizure recurrence within 1 year after ATL. Despite the extensive literature on presurgical electroencephalography (EEG) and magnetic resonance imaging (MRI) abnormalities to prognosticate seizure freedom following ATL, the value of quantitative analysis of visually reviewed normal interictal EEG in such prognostication remains unclear. In this retrospective multicenter study, we investigate whether machine learning analysis of normal interictal scalp EEG studies can inform the prediction of postoperative seizure freedom outcomes in patients who have undergone ATL.

    Methods

    We analyzed normal presurgical scalp EEG recordings from 41 Mayo Clinic (MC) and 23 Cleveland Clinic (CC) patients. We used an unbiased automated algorithm to extract eyes closed awake epochs from scalp EEG studies that were free of any epileptiform activity and then extracted spectral EEG features representing (a) spectral power and (b) interhemispheric spectral coherence in frequencies between 1 and 25 Hz across several brain regions. We analyzed the differences between the seizure‐free and non–seizure‐free patients and employed a Naïve Bayes classifier using multiple spectral features to predict surgery outcomes. We trained the classifier using a leave‐one‐patient‐out cross‐validation scheme within the MC data set and then tested using the out‐of‐sample CC data set. Finally, we compared the predictive performance of normal scalp EEG‐derived features against MRI abnormalities.

    Results

    We found that several spectral power and coherence features showed significant differences correlated with surgical outcomes and that they were most pronounced in the 10–25 Hz range. The Naïve Bayes classification based on those features predicted 1‐year seizure freedom following ATL with area under the curve (AUC) values of 0.78 and 0.76 for the MC and CC data sets, respectively. Subsequent analyses revealed that (a) interhemispheric spectral coherence features in the 10–25 Hz range provided better predictability than other combinations and (b) normal scalp EEG‐derived features provided superior and potentially distinct predictive value when compared with MRI abnormalities (>10% higher F1 score).

    Significance

    These results support that quantitative analysis of even a normal presurgical scalp EEG may help prognosticate seizure freedom following ATL in patients with drug‐resistant TLE. Although the mechanism for this result is not known, the scalp EEG spectral and coherence properties predicting seizure freedom may represent activity arising from the neocortex or the networks responsible for temporal lobe seizure generation within vs outside the margins of an ATL.

     
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  4. Abstract

    Copy number variants (CNV) are established risk factors for neurodevelopmental disorders with seizures or epilepsy. With the hypothesis that seizure disorders share genetic risk factors, we pooled CNV data from 10,590 individuals with seizure disorders, 16,109 individuals with clinically validated epilepsy, and 492,324 population controls and identified 25 genome-wide significant loci, 22 of which are novel for seizure disorders, such as deletions at 1p36.33, 1q44, 2p21-p16.3, 3q29, 8p23.3-p23.2, 9p24.3, 10q26.3, 15q11.2, 15q12-q13.1, 16p12.2, 17q21.31, duplications at 2q13, 9q34.3, 16p13.3, 17q12, 19p13.3, 20q13.33, and reciprocal CNVs at 16p11.2, and 22q11.21. Using genetic data from additional 248,751 individuals with 23 neuropsychiatric phenotypes, we explored the pleiotropy of these 25 loci. Finally, in a subset of individuals with epilepsy and detailed clinical data available, we performed phenome-wide association analyses between individual CNVs and clinical annotations categorized through the Human Phenotype Ontology (HPO). For six CNVs, we identified 19 significant associations with specific HPO terms and generated, for all CNVs, phenotype signatures across 17 clinical categories relevant for epileptologists. This is the most comprehensive investigation of CNVs in epilepsy and related seizure disorders, with potential implications for clinical practice.

     
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