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Objective Find the optimal continuous electroencephalographic (CEEG) monitoring duration for seizure detection in critically ill patients. Methods We analyzed prospective data from 665 consecutive CEEGs, including clinical factors and time-to-event emergence of electroencephalographic (EEG) findings over 72 hours. Clinical factors were selected using logistic regression. EEG risk factors were selected a priori. Clinical factors were used for baseline (pre-EEG) risk. EEG findings were used for the creation of a multistate survival model with 3 states (entry, EEG risk, and seizure). EEG risk state is defined by emergence of epileptiform patterns. Results The clinical variables of greatest predictive value were coma (31% had seizures; odds ratio [OR] = 1.8, p<0.01) and history of seizures, either remotely or related to acute illness (34% had seizures; OR = 3.0, p<0.001). If there were no epileptiform findings on EEG, the risk of seizures within 72 hours was between 9% (no clinical risk factors) and 36% (coma and history of seizures). If epileptiform findings developed, the seizure incidence was between 18% (no clinical risk factors) and 64% (coma and history of seizures). In the absence of epileptiform EEG abnormalities, the duration of monitoring needed for seizure risk of <5% was between 0.4 hours (for patients who are not comatose and had no prior seizure) and 16.4 hours (comatose and prior seizure). Interpretation The initial risk of seizures on CEEG is dependent on history of prior seizures and presence of coma. The risk of developing seizures on CEEG decays to <5% by 24 hours if no epileptiform EEG abnormalities emerge, independent of initial clinical risk factors.

Objective Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has a myriad of neurological manifestations and its effects on the nervous system are increasingly recognized. Seizures and status epilepticus (SE) are reported in the novel coronavirus disease (COVID-19), both new onset and worsening of existing epilepsy, however the exact prevalence is still unknown. The primary aim of this study was to correlate the presence of seizures, status epilepticus, and specific critical care EEG patterns with patient functional outcomes in those with COVID-19. Methods This is a retrospective, multicenter cohort of COVID-19 positive patients in Southeast Michigan who underwent electroencephalography (EEG) from March 12 th through May 15 th , 2020. All patients had confirmed nasopharyngeal PCR for COVID-19. EEG patterns were characterized per 2012 ACNS critical care EEG terminology. Clinical and demographic variables were collected by medical chart review. Outcomes were divided into recovered, recovered with disability, or deceased. Results Out of the total of 4100 patients hospitalized with COVID-19, 110 patients (2.68%) had EEG during their hospitalization; 64% were male, 67% were African American with mean age of 63 years (range 20-87). The majority (70%) had severe COVID-19 were intubated or had multiorgan failure. The median length of hospitalization was 26.5 days (IQR=15 to 44 days). During hospitalization, of the patients who had EEG, 21.8% had new onset seizure including 7% with status epilepticus, majority (87.5%) with no prior epilepsy. Forty-nine (45%) patients died in the hospital, 46 (42%) recovered but maintained a disability and 15 (14%) recovered without a disability. The EEG findings associated with outcomes were background slowing/attenuation (recovered 60% vs recovered/disabled 96% vs died 96%, p<0.001) and normal (recovered 27% vs recovered/disabled 0% vs died 1%, p<0.001). However, these findings were no longer significant after adjusting for severity of COVID-19. Conclusion In this large multi-center study from Southeast Michigan, one of the early COVID-19 epicenters in the US, none of the EEG findings were significantly correlated with outcomes in critically ill COVID-19 patients. Although seizures and status epilepticus could be encountered in COVID-19, the occurrence did not correlate with the patients’ functional outcome.

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BIPDs are uncommon but their identification in critically ill patients has potential important implications, both in terms of clinical management and prognostication.

BackgroundPost-traumatic epilepsy (PTE) is a severe complication of traumatic brain injury (TBI). Electroencephalography aids early post-traumatic seizure diagnosis, but its optimal utility for PTE prediction remains unknown. We aim to evaluate the contribution of quantitative electroencephalograms to predict first-year PTE (PTE1).MethodsWe performed a multicentre, retrospective case–control study of patients with TBI. 63 PTE1patients were matched with 63 non-PTE1patients by admission Glasgow Coma Scale score, age and sex. We evaluated the association of quantitative electroencephalography features with PTE1using logistic regressions and examined their predictive value relative to TBI mechanism and CT abnormalities.ResultsIn the matched cohort (n=126), greater epileptiform burden, suppression burden and beta variability were associated with 4.6 times higher PTE1risk based on multivariable logistic regression analysis (area under the receiver operating characteristic curve, AUC (95% CI) 0.69 (0.60 to 0.78)). Among 116 (92%) patients with available CT reports, adding quantitative electroencephalography features to a combined mechanism and CT model improved performance (AUC (95% CI), 0.71 (0.61 to 0.80) vs 0.61 (0.51 to 0.72)).ConclusionsEpileptiform and spectral characteristics enhance covariates identified on TBI admission and CT abnormalities in PTE1prediction. Future trials should incorporate quantitative electroencephalography features to validate this enhancement of PTE risk stratification models.

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There is accumulating evidence on the prognostic implications of various rhythmic and periodic patterns in the critically ill population. These patterns are not only associated with increased seizure risk but have also been associated with worse outcome and increased long-term risk of epilepsy in recent studies. There is emerging evidence suggesting that certain EEG features as well as ancillary studies including serum, neuroimaging, and invasive multimodality monitory can assist in the risk stratification of neuronal injury associated with these patterns, allowing for a targeted approach to these patterns. We present a case illustrating the clinical nuances of these patterns. We propose an algorithm for a personalized and targeted approach to ictal interictal patterns based on risk stratification according to clinical, EEG, imaging, and invasive monitoring markers.