Clinical characteristics of non‐convulsive status epilepticus diagnosed by simplified continuous electroencephalogram monitoring at an emergency intensive care unit

Egawa, Satoshi; Hifumi, Toru; Kawakita, Kenya; Manabe, Arisa; Nakashima, Ryuta; Matsumura, Hikari; Okazaki, Tomoya; Hamaya, Hideyuki; Shinohara, Naohide; Shishido, Hajime; Takano, Koshiro; Abe, Yuko; Hagiike, Masanobu; Kubota, Yuichi; Kuroda, Yasuhiro

doi:10.1002/ams2.221

Cited by 8 publications

(7 citation statements)

References 17 publications

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“…Stroke itself seems to be associated with a heightened risk of NCSE, with previous studies confirming stroke as the main cause of NCSE in their cohort (9). The overall prevalence of NCSE in our study population was 38.75%, which is in line with occurrence rates reported in previous studies that range from 14 to 45.3% (10)(11)(12)(13). We likewise found that there was a greater rate of NCSE in our cohort than in exclusively retrospective studies.…”

Section: Discussionsupporting

confidence: 91%

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Associated Factors and Prognostic Implications of Non-convulsive Status Epilepticus in Ischemic Stroke Patients With Impaired Consciousness

et al. 2022

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Background and Purpose: Non-convulsive status epilepticus (NCSE) is common in patients with disorders of consciousness and can cause secondary brain injury. Our study aimed to explore the determinants and prognostic significance of NCSE in stroke patients with impaired consciousness.Method: Consecutive ischemic stroke patients with impaired consciousness who were admitted to a neuro intensive care unit were enrolled for this study. Univariate and multivariable logistic regression were used to identify factors associated with NCSE and their correlation with prognosis.Results: Among the 80 patients studied, 20 (25%) died during hospitalization, and 51 (63.75%) had unfavorable outcomes at the 3-month follow-up. A total of 31 patients (38.75%) developed NCSE during 24-h electroencephalogram (EEG) monitoring. Logistic regression revealed that NCSE was significantly associated with an increased risk of death during hospital stay and adverse outcomes at the 3-month follow-up. Patients with stroke involving the cerebral cortex or those who had a severely depressed level of consciousness were more prone to epileptogenesis after stroke.Conclusion: Our results suggest that NCSE is a common complication of ischemic stroke, and is associated with both in-hospital mortality and dependency at the 3-month follow-up. Long-term video EEG monitoring of stroke patients is, therefore required, especially for those with severe consciousness disorders (stupor or coma) or cortical injury.

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Section: Discussionsupporting

confidence: 91%

“…without NCSE. The use of antiepileptic drugs may explain this discrepancy (12). On the other hand, some scholars also believe that the different inclusion criteria for patients might yield different results, and etiology is the most important factor influencing prognosis.…”

Section: Limitations and Future Perspectivesmentioning

confidence: 99%

Associated Factors and Prognostic Implications of Non-convulsive Status Epilepticus in Ischemic Stroke Patients With Impaired Consciousness

et al. 2022

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“…For these reasons, cEEG monitoring and assessment of NCSE during the induction, maintenance, and rewarming periods are indicated for all TTM patients (Figs. 4, 5) [86,91,92]. Seizures following cardiac arrest are associated with increased mortality [85,87,88].…”

Section: Detection Of Seizuresmentioning

confidence: 99%

Targeted temperature management for postcardiac arrest syndrome

Kuroda

Kawakita

2020

J Neurocrit Care

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Neurocritical care management to improve neurologic outcome for postcardiac arrest syndrome (PCAS) has focused considerably on targeted temperature management (TTM). TTM attenuates the destructive processes following ischemia/reperfusion in PCAS. The principal indication of TTM is a patient with sustained coma after return of spontaneous circulation (ROSC). TTM can be strongly recommended with a target temperature between 32°C and 36°C for patients with shockable rhythm and out-of-hospital cardiac arrest (OHCA) and weakly recommended for patients with initial asystole or pulseless electrical activity with OHCA and those with in-hospital cardiac arrest. TTM is induced and maintained using a cooling device with body temperature feedback under appropriate analgosedation. It requires the intensive management of various systemic respiratory, circulatory, and metabolic parameters that control shivering to prevent secondary brain damage. Considering the cerebral perfusion pressure, it is suggested that the mean arterial pressure should be particularly maintained over 80 mm Hg. Seizure management, including continuous electroencephalography monitoring, is also needed. Finally, we must continue the above mentioned care during and after the rewarming phase, because high fever and shivering may appear again during this period. Furthermore, neurological prognostication should be performed at least 72 hours after ROSC through clinical investigations and multimodal testing without sedation.

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“…CHB-MIT EEGs were collected from 22 patients (ages 1. [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. The waveforms were sampled at 256 Hz and included annotations of the seizure's start and end identified by neurophysiologists at CHB.…”

Section: Eeg Datamentioning

confidence: 99%

“…Non-convulsive seizures (NCS) are common in critically ill patients (8-50%) [1][2][3][4][5][6], and if untreated, are associated with high mortality (17-51%) [6][7][8]. Furthermore, effective treatment must be timely, as delayed treatment can lead to long term neurologic disability [9][10][11]. Many intensive care units (ICU) now perform continuous EEGs (cEEG) to monitor for NCS.…”

Section: Introductionmentioning

confidence: 99%

Automated spectrographic seizure detection using convolutional neural networks

Yan

Wang

Kwok

et al. 2019

Seizure

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Non-convulsive seizures are common in critically ill patients, and delays in diagnosis contribute to increased morbidity and mortality. Many intensive care units employ continuous EEG (cEEG) for seizure monitoring. Although cEEG is continuously recorded, it is often reviewed intermittently, which may delay seizure diagnosis and treatment. This may be mitigated with automated seizure detection. In this study, we develop and evaluate convolutional neural networks (CNN) to automate seizure detection on EEG spectrograms. Methods: Adult EEGs (12 patients, 12 EEGs, 33 seizures) from New-York Presbyterian Hospital (NYP) and pediatric EEGs (22 patients, 130 EEGs, 177 seizures) from Children's Hospital Boston (CHB) were converted into spectrograms. To simulate a telemetry display, seizure and non-seizure events on spectrograms were sequentially sampled as images across a detection window (26,380 total images). Four CNN models of increasing complexity (number of layers) were trained, cross-validated, and tested on CHB and NYP spectrographic images. All CNNs were based on the VGG-net architecture, with adjustments to alleviate overfitting. Results: For spectrographically visible seizures, two CNN models (containing 4 and 7 convolution layers) achieved > 90% seizure detection sensitivity and specificity on the CHB test set and > 90% sensitivity and 75-80% specificity on the NYP test set. The one CNN model (10 convolution layers) did not converge during training; while another CNN (2 convolution layers) performed poorly (60% sensitivity and 32% specificity) on the NYP test set. Conclusions: Seizure detection on EEG spectrograms with CNN models is feasible with sensitivity and specificity potentially suitable for clinical use.

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Clinical characteristics of non‐convulsive status epilepticus diagnosed by simplified continuous electroencephalogram monitoring at an emergency intensive care unit

Cited by 8 publications

References 17 publications

Associated Factors and Prognostic Implications of Non-convulsive Status Epilepticus in Ischemic Stroke Patients With Impaired Consciousness

Associated Factors and Prognostic Implications of Non-convulsive Status Epilepticus in Ischemic Stroke Patients With Impaired Consciousness

Targeted temperature management for postcardiac arrest syndrome

Automated spectrographic seizure detection using convolutional neural networks

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