EEG as an Indicator of Cerebral Functioning in Postanoxic Coma

Juan, Elsa; Kaplan, Peter W.; Oddo, Mauro; Rossetti, Andrea O.

doi:10.1097/wnp.0000000000000199

Cited by 13 publications

(12 citation statements)

References 56 publications

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“…Specifically, we compared the accuracy of the Synek and standardized TTM-ACNS EEG classification systems; although EEG was not recorded primarily for prognostic purposes, we cannot discount an element of confirmation bias. None the less it would appear that when classified, the EEG phenotype does help stratify patients with both a good and a poor prognosis, as has been reported by others, and along with others we have called for its introduction into clinical practice [9,12,[34][35][36]. The disease-specific standardized TTM-ACNS system was more sensitive and specific in our series, suggesting that it is superior to the widely used Synek EEG grading.…”

Section: Discussionsupporting

confidence: 68%

“…Nonetheless, EEG is frequently recorded in comatose sedated ICU patients after OOHCA, as it enables the detection of myoclonic status epilepticus, non-convulsive status epilepticus, and subclinical seizures, which may account for nonawakening from coma. This real-world clinical experience is leading to a growing recognition that certain EEG patterns, referred to as phenotypes, can be used as biomarkers of both poor and good prognoses after HIE [12]. SSEPs already have an established role in identifying patients with a poor neurological prognosis, rather than in predicting good outcome, even those treated with sedatives and hypothermia, as demonstrated by two meta-analyses [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

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A Review of the Electrophysiological Neuroprognostications after Out of Hospital Cardiac Arrest

Skorko¹,

Pachucki²,

Taylor³

et al. 2021

ACR

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This personal opinion review of the potential role for EEG in the multimodal neuroprognostication of comatose cardiac arrest patients, after resuscitation and targeted temperature management, discusses recent findings along with our personal experience from a large single-center cohort of 220 consecutive patients investigated with electrophysiological tests (EEG and SSEP). Although EEG has its limitations, along with all modalities in the multimodal prognostic framework, when timed appropriately and interpreted in a standardized fashion, it can be probabilistic but not deterministic of an individual patient’s neurological prognosis. The EEG phenotype can indicate both good and poor prognoses for a comatose patient on the Intensive Care Unit, which is a distinct advantage of this widely available modality, whilst an SSEP can reliably predict a poor outcome if absent and may also help predict good outcome using amplitude analysis.

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Section: Discussionsupporting

confidence: 68%

Section: Introductionmentioning

confidence: 99%

A Review of the Electrophysiological Neuroprognostications after Out of Hospital Cardiac Arrest

Skorko¹,

Pachucki²,

Taylor³

et al. 2021

ACR

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“…Low voltage or generalized suppression patterns is often associated with extensive cortical and subcortical lesions such as anoxic encephalopathy or severe head trauma. This pattern was related to an unfavorable prognosis (score 4 qualitative classification of EEG alteration: Amodio et al, 1999) [40][41][42] or irreversible character as at ischemic encephalopathy 39,[42][43][44][45] with the exception of hypothyroid encephalopathy, which is reversible except for cretinism. 39,42,43,[45][46][47] , but in our series we found a good evolution in 60% of the patients.…”

Section: Discussionmentioning

confidence: 99%

“…This pattern was related to an unfavorable prognosis (score 4 qualitative classification of EEG alteration: Amodio et al, 1999) [40][41][42] or irreversible character as at ischemic encephalopathy 39,[42][43][44][45] with the exception of hypothyroid encephalopathy, which is reversible except for cretinism. 39,42,43,[45][46][47] , but in our series we found a good evolution in 60% of the patients. It has also been described in diffuse neuronal dysfunction like hypothermia, intoxication by central nervous system depressant drugs or anesthesia, 39,43,44 but we have ruled out all these causes in our patients.…”

Section: Discussionmentioning

confidence: 99%

Retrospective Analysis of EEG in Patients With COVID-19: EEG Recording in Acute and Follow-up Phases

et al. 2021

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Background. Interest in electroencephalographic (EEG) coronavirus disease 2019 (COVID-19) findings has been growing, especially in the search for a specific-features EEG of encephalopathy. Methods. We made a retrospective analysis of 29 EEGs recorded in 15 patients with COVID-19 and neurological symptoms. We classified the EEGs as “Acute EEG” and “follow-up EEG.” We did a statistical analysis between voltage and respiratory status of the patient, stay or not in the intensive care unit (ICU), days of stay in the ICU, sedative drugs, pharmacological treatment, type of symptoms predominating, and outcome. Results. We found EEG abnormalities in all patients studied. We observed the amplitude of background <20 µV at 93% of “acute EEG,” versus only 21.4% of “follow-up EEG.” The average voltage went from 12.33 ± 5.09 µV in the acute EEGs to 32.8 ± 20.13 µV in the follow-up EEGs. A total of 60% of acute EEGs showed an intermittent focal rhythmic. We have not found a statistically significant association between voltage of acute EEG and nonneurological clinical status (including respiratory) that may interfere with the EEG findings. Conclusions. Nonspecific diffuse slowing EEG pattern in COVID-19 is the most common finding reported, but we found in addition to that, as a distinctive finding, low voltage EEG, that could explain the low prevalence of epileptic activity published in these patients. A metabolic/hypoxic mechanism seems unlikely on the basis of our EEG findings. This pattern in other etiologies is reminiscent of severe encephalopathy states associated with poor prognosis. However, an unreactive low voltage pattern in COVID-19 patients is not necessarily related to poor prognosis.

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“…In Awal et al (2016) the authors use EEG features to predict neurodevelopmental outcomes for term infants with hypoxic ischaemic encephalopathy (HIE). The association between brain activity during coma and trauma outcomes was investigated in Malagurski et al (2017) and Juan et al (2015). Statistical analysis of EEG has been used to identify quasi-brain-death from coma (Li et al 2014) and to confirm brain death (Chen et al 2008).…”

Section: Introductionmentioning

confidence: 99%

Parameters of stochastic models for electroencephalogram data as biomarkers for child’s neurodevelopment after cerebral malaria

2018

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The objective of this study was to test statistical features from the electroencephalogram (EEG) recordings as predictors of neurodevelopment and cognition of Ugandan children after coma due to cerebral malaria. The increments of the frequency bands of EEG time series were modeled as Student processes; the parameters of these Student processes were estimated and used along with clinical and demographic data in a machine-learning algorithm for the prediction of children’s neurodevelopmental and cognitive scores 6 months after cerebral malaria illness. The key innovation of this work is in the identification of stochastic EEG features that can serve as language-independent markers of the impact of cerebral malaria on the developing brain. The results can enhance prognostic determination of which children are in most need of rehabilitative interventions, which is especially important in resource-constrained settings such as sub-Saharan Africa.

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EEG as an Indicator of Cerebral Functioning in Postanoxic Coma

Cited by 13 publications

References 56 publications

A Review of the Electrophysiological Neuroprognostications after Out of Hospital Cardiac Arrest

A Review of the Electrophysiological Neuroprognostications after Out of Hospital Cardiac Arrest

Retrospective Analysis of EEG in Patients With COVID-19: EEG Recording in Acute and Follow-up Phases

Parameters of stochastic models for electroencephalogram data as biomarkers for child’s neurodevelopment after cerebral malaria

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