How and Whom to Monitor for Seizures in an ICU: A Systematic Review and Meta-Analysis

Limotai, Chusak; Ingsathit, Atiporn; Thadanipon, Kunlawat; McEvoy, Mark; Attia, John; Thakkinstian, Ammarin

doi:10.1097/ccm.0000000000003641

Cited by 41 publications

(39 citation statements)

References 55 publications

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“…In case of known seizures, however, the response to anti-seizure medication is the main prognostic feature, and not the other ACNS criteria. As previously published [32], about 10% of patients in the present cohort (4.4% in those undergoing routine EEG, 15.7% in the group with continuous EEG) had ictal or interictal epileptiform activity during at least one EEG recording; while these patients were not excluded from the analysis, these numbers correspond to a recent meta-analysis of available cohort studies [53] and thus in our view reinforce generalizability of our findings. Also, we did not account for potential preexisting disability, which could affect the best CPC.…”

Section: Strengths and Limitationssupporting

confidence: 90%

Standardized visual EEG features predict outcome in patients with acute consciousness impairment of various etiologies

et al. 2020

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Background Early prognostication in patients with acute consciousness impairment is a challenging but essential task. Current prognostic guidelines vary with the underlying etiology. In particular, electroencephalography (EEG) is the most important paraclinical examination tool in patients with hypoxic ischemic encephalopathy (HIE), whereas it is not routinely used for outcome prediction in patients with traumatic brain injury (TBI). Method Data from 364 critically ill patients with acute consciousness impairment (GCS ≤ 11 or FOUR ≤ 12) of various etiologies and without recent signs of seizures from a prospective randomized trial were retrospectively analyzed. Random forest classifiers were trained using 8 visual EEG features—first alone, then in combination with clinical features—to predict survival at 6 months or favorable functional outcome (defined as cerebral performance category 1–2). Results The area under the ROC curve was 0.812 for predicting survival and 0.790 for predicting favorable outcome using EEG features. Adding clinical features did not improve the overall performance of the classifier (for survival: AUC = 0.806, p = 0.926; for favorable outcome: AUC = 0.777, p = 0.844). Survival could be predicted in all etiology groups: the AUC was 0.958 for patients with HIE, 0.955 for patients with TBI and other neurosurgical diagnoses, 0.697 for patients with metabolic, inflammatory or infectious causes for consciousness impairment and 0.695 for patients with stroke. Training the classifier separately on subgroups of patients with a given etiology (and thus using less training data) leads to poorer classification performance. Conclusions While prognostication was best for patients with HIE and TBI, our study demonstrates that similar EEG criteria can be used in patients with various causes of consciousness impairment, and that the size of the training set is more important than homogeneity of ACI etiology.

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Section: Strengths and Limitationssupporting

confidence: 90%

Standardized visual EEG features predict outcome in patients with acute consciousness impairment of various etiologies

et al. 2020

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“…NCS are found in critically ill patients regardless of underlying brain injury 11 . In fact, NCS were less common in our study population (2%) than what is reported in association with severe sepsis (seen in ~11% of patients), or patients admitted to an ICU for intracranial hemorrhage, subarachnoid hemorrhage, subdural hematoma, or traumatic brain injury (seen in ~10%‐13% of patients) 12,34 . Our findings indicate that epileptiform findings on EEG are rarely found in patients with COVID‐19 in the absence of clinically determinable risks (notably the presence of clinical seizures or history of epilepsy), and in patients without these risks, are rare in the absence of acute imaging findings.…”

Section: Discussionmentioning

confidence: 47%

“…11 In fact, NCS were less common in our study population (2%) than what is reported in association with severe sepsis (seen in ~11% of patients), or patients admitted to an ICU for intracranial hemorrhage, subarachnoid hemorrhage, subdural hematoma, or traumatic brain injury (seen in ~10%-13% of patients). 12,34 Our findings indicate that epileptiform findings on EEG are rarely found in patients with COVID-19 in the absence of clinically determinable risks (notably the presence of clinical seizures or history of epilepsy), and in patients without these risks, are rare in the absence of acute imaging findings. This is important to keep in mind when weighing the potential benefits of EEG testing against the risks of spreading infection due to obtaining EEG recordings in this population, as discussed in a recent review on the topic.…”

Section: Discussionmentioning

confidence: 71%

Continuous EEG findings in patients with COVID‐19 infection admitted to a New York academic hospital system

et al. 2020

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Objective: There is evidence for central nervous system complications of coronavirus disease 2019 (COVID-19) infection, including encephalopathy. Encephalopathy caused by or arising from seizures, especially nonconvulsive seizures (NCS), often requires electroencephalography (EEG) monitoring for diagnosis. The prevalence of seizures and other EEG abnormalities among COVID-19-infected patients is unknown. Methods: Medical records and EEG studies of patients hospitalized with confirmed COVID-19 infections over a 2-month period at a single US academic health system (four hospitals) were reviewed to describe the distribution of EEG findings including epileptiform abnormalities (seizures, periodic discharges, or nonperiodic epileptiform discharges). Factors including demographics, remote and acute brain injury, prior history of epilepsy, preceding seizures, critical illness severity scores, and interleukin 6 (IL-6) levels were compared to EEG findings to identify predictors of epileptiform EEG abnormalities. Results: Of 111 patients monitored, most were male (71%), middle-aged or older (median age 64 years), admitted to an intensive care unit (ICU; 77%), and comatose (70%). Excluding 11 patients monitored after cardiac arrest, the most frequent EEG finding was moderate generalized slowing (57%), but epileptiform findings were observed in 30% and seizures in 7% (4% with NCS). Three patients with EEG seizures did not have epilepsy or evidence of acute or remote brain injury, although all had clinical seizures prior to EEG. Only having epilepsy (odds ratio [OR] 5.4, 95% confidence interval [CI] 1.4-21) or seizure(s) prior to EEG (OR 4.8, 95% CI 1.7-13) was independently associated with epileptiform EEG findings. Significance: Our study supports growing evidence that COVID-19 can affect the central nervous system, although seizures are unlikely a common cause of encephalopathy. Seizures and epileptiform activity on EEG occurred infrequently, and having 2098 |

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“…In practice, electroencephalography (EEG) recording is required to help in the diagnosis of non-convulsive seizure (NCS)/NCSE; otherwise, it may be under-recognised and left untreated 3. Our recent meta-analysis revealed that continuous EEG (cEEG) is significantly better than routine EEG (rEEG) in helping detect NCS/NCSE 4. The overall prevalence of NCS/NCSE is 15.6% in critically ill patients, but higher in patients with postconvulsive SE (32.9%), central nervous system (CNS) infection (23.9%) and postcardiac arrest (22.6%) 4.…”

Section: Introductionmentioning

confidence: 99%

“…Our recent meta-analysis revealed that continuous EEG (cEEG) is significantly better than routine EEG (rEEG) in helping detect NCS/NCSE 4. The overall prevalence of NCS/NCSE is 15.6% in critically ill patients, but higher in patients with postconvulsive SE (32.9%), central nervous system (CNS) infection (23.9%) and postcardiac arrest (22.6%) 4. Evidence of systemic complications and neurological consequences has been clearly demonstrated in CSE,5 but remains unclear for NCS/NCSE.…”

Section: Introductionmentioning

confidence: 99%

Efficacy and economic evaluation of delivery of care with tele-continuous EEG in critically ill patients: a multicentre, randomised controlled trial (Tele-cRCT) study protocol

et al. 2020

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IntroductionSome critically ill patients are confirmed by continuous electroencephalography (cEEG) monitoring that non-convulsive seizure (NCS) and/or non-convulsive status epilepticus (NCSE) are causes of their depressed level of consciousness. Shortage of epilepsy specialists, especially in developing countries, is a major limiting factor in implementing cEEG in general practice. Delivery of care with tele-continous EEG (tele-cEEG) may be a potential solution as this allows specialists from a central facility to remotely assist local neurologists from distant areas in interpreting EEG findings and suggest proper treatment. No tele-cEEG programme has been implemented to help improve quality of care. Therefore, this study is conducted to assess the efficacy and cost utility of implementing tele-cEEG in critical care.Methods and analysisThe Tele-cRCT study is a 3-year prospective, randomised, controlled, parallel, multicentre, superiority trial comparing delivery of care through ‘Tele-cEEG’ intervention with ‘Tele-routine EEG (Tele-rEEG)’ in patients with clinical suspicion of NCS/NCSE. A group of EEG specialists and a tele-EEG system were set up to remotely interpret EEG findings in six regional government hospitals across Thailand. The primary outcomes are functional neurological outcome (modified Rankin Scale, mRS), mortality rate and incidence of seizures. The secondary outcomes are cost utility, length of stay, emergency visit/readmission, impact on changing medical decisions and health professionals’ perceptions about tele-cEEG implementation. Functional outcome (mRS) will be assessed at 3 and 7 days after recruitment, and again at time of hospital discharge, and at 90 days, 6 months, 9 months and 1 year. Costs and health-related quality of life will be assessed using the Thai version of the EuroQol-five dimensions-five levels (EQ-5D-5L) at hospital discharge, and at 90 days, 6 months, 9 months and 1 year.Ethics and disseminationThis study has been approved by the ethics committees of the Faculty of Medicine, Chulalongkorn University, and of Ramathibodi Hospital, Mahidol University, and registered on Thai Clinical Trials Registry. The results will be disseminated in a peer-reviewed journal.Trial registration numberTCTR20181022002; preresults.

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How and Whom to Monitor for Seizures in an ICU: A Systematic Review and Meta-Analysis

Cited by 41 publications

References 55 publications

Standardized visual EEG features predict outcome in patients with acute consciousness impairment of various etiologies

Standardized visual EEG features predict outcome in patients with acute consciousness impairment of various etiologies

Continuous EEG findings in patients with COVID‐19 infection admitted to a New York academic hospital system

Efficacy and economic evaluation of delivery of care with tele-continuous EEG in critically ill patients: a multicentre, randomised controlled trial (Tele-cRCT) study protocol

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