Rapid Response Electroencephalography for Urgent Evaluation of Patients in Community Hospital Intensive Care Practice

Yazbeck, Moussa; Sra, Parveen; Parvizi, Josef

doi:10.1097/jnn.0000000000000476

Cited by 27 publications

(33 citation statements)

References 16 publications

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“…Limitations of our study include the small sample size, as discussed previously. While 8ch‐EEGs provide a rapid and easy method of EEG monitoring, 9,10 the electrode coverage includes 8 bipolar channels (frontal, temporal, occipital), limiting the capacity to fully localize and characterize certain waveforms. In our study, two patients with EDs detected by the 8ch‐EEG also had routine and/or cEEG studies that confirmed the presence of EDs.…”

Section: Discussionmentioning

confidence: 99%

“…Systematic testing for COVID‐19 has been has been performed beginning in early March 2020 when the first cases were recognized in New York. To minimize healthcare personnel's exposure to high risk for COVID‐19 transmission patients, we have utilized an 8‐channel headband EEG system (8ch‐EEG), Ceribell rapid response EEG that can be quickly applied by personnel without having prior training as EEG technologists 9,10 . We report our first findings from medically indicated EEG studies, performed predominantly using 8ch‐EEG, on admitted acutely ill COVID‐19 PUIs.…”

Section: Introductionmentioning

confidence: 99%

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EEG findings in acutely ill patients investigated for SARS‐CoV‐2/COVID‐19: A small case series preliminary report

et al. 2020

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Objective: Acute encephalopathy may occur in COVID-19-infected patients. We investigated whether medically indicated EEGs performed in acutely ill patients under investigation (PUIs) for COVID-19 report epileptiform abnormalities and whether these are more prevalent in COVID-19 positive than negative patients. Methods: In this retrospective case series, adult COVID-19 inpatient PUIs underwent EEGs for acute encephalopathy and/or seizure-like events. PUIs had 8-channel headband EEGs (Ceribell; 20 COVID-19 positive, 6 COVID-19 negative); 2 more COVID-19 patients had routine EEGs. Overall, 26 Ceribell EEGs, 4 routine and 7 continuous EEG studies were reviewed. EEGs were interpreted by board-certified clinical neurophysiologists (n = 16). EEG findings were correlated with demographic data, clinical presentation and history, and medication usage. Fisher's exact test was used. Results: We included 28 COVID-19 PUIs (30-83 years old), of whom 22 tested positive (63.6% males) and 6 tested negative (33.3% male). The most common indications for EEG, among COVID-19-positive vs COVID-19-negative patients, respectively, were new onset encephalopathy (68.2% vs 33.3%) and seizure-like events (14/22, 63.6%; 2/6, 33.3%), even among patients without prior history of seizures (11/17, 64.7%; 2/6, 33.3%). Sporadic epileptiform discharges (EDs) were present in 40.9% of COVID-19-positive and 16.7% of COVID-19-negative patients; frontal sharp waves were reported in 8/9 (88.9%) of COVID-19-positive patients with EDs and in 1/1 of COVID-19-negative patient with EDs. No electrographic seizures were captured, but 19/22 COVID-19-positive and 6/6 COVID-19-negative patients were given antiseizure medications and/or sedatives before the EEG. | 315GALANOPOULOU et AL. | METHODS | Study design, inclusion and exclusion criteria

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

EEG findings in acutely ill patients investigated for SARS‐CoV‐2/COVID‐19: A small case series preliminary report

et al. 2020

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“…When available, some institutions opt for a rapid response 8-channel EEG instead of routing or continuous EEG to reduce the time of patient contact [73]. As one considers anti-epileptic therapy in these patients, special consideration must be given to concomitant circumstances that may affect antiseizure medication pharmacokinetics, such as renal or hepatic failure or extra corporeal membrane oxygenation [67].…”

Section: Status Epilepticusmentioning

confidence: 99%

Severe Neurologic Complications of SARS-CoV-2

Sisniega¹,

Reynolds

2021

Curr Treat Options Neurol

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Purpose of Review This review presents an overview of the known neurocritical care complications of severe acute respiratory virus 2 (SARS-CoV-2). We present readers with a review of the literature of severe neurologic complications of SARS-CoV-2 and cases from our institution to illustrate these conditions. Recent Findings Neurologic manifestations are being increasingly recognized in the literature. Some patients can have severe neurologic manifestations, though the true prevalence is unknown. Summary Severe neurologic complications of COVID-19 include large vessel occlusion ischemic stroke, intracranial hemorrhage, encephalitis, myelitis, Guillain-Barre syndrome, status epilepticus, posterior reversible encephalopathy syndrome, and hypoxic-ischemic encephalopathy. These conditions can manifest in COVID-19 patients even in the absence of risk factors and must be promptly identified as they can have a high mortality if left untreated.

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“…When actionable EEG interpretations are not immediately available, treatment decisions are made on the basis of clinical suspicion alone, which results in the potential for missing or undertreating some patients with nonconvulsive seizures and overtreating a significantly larger number of patients without seizure activity who may not need aggressive antiseizure medications [13,14]. To provide timely access to EEG, novel rapid EEG systems can be used to enable physicians and allied health professionals to acquire EEG within minutes and stream the data in real time to the cloud, where a machine learning-derived classification algorithm can be applied [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Monitoring the Burden of Seizures and Highly Epileptiform Patterns in Critical Care with a Novel Machine Learning Method

Kamousi¹,

Karunakaran²,

Gururangan

et al. 2020

Neurocrit Care

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Introduction Current electroencephalography (EEG) practice relies on interpretation by expert neurologists, which introduces diagnostic and therapeutic delays that can impact patients’ clinical outcomes. As EEG practice expands, these experts are becoming increasingly limited resources. A highly sensitive and specific automated seizure detection system would streamline practice and expedite appropriate management for patients with possible nonconvulsive seizures. We aimed to test the performance of a recently FDA-cleared machine learning method (Claritγ, Ceribell Inc.) that measures the burden of seizure activity in real time and generates bedside alerts for possible status epilepticus (SE). Methods We retrospectively identified adult patients (n = 353) who underwent evaluation of possible seizures with Rapid Response EEG system (Rapid-EEG, Ceribell Inc.). Automated detection of seizure activity and seizure burden throughout a recording (calculated as the percentage of ten-second epochs with seizure activity in any 5-min EEG segment) was performed with Claritγ, and various thresholds of seizure burden were tested (≥ 10% indicating ≥ 30 s of seizure activity in the last 5 min, ≥ 50% indicating ≥ 2.5 min of seizure activity, and ≥ 90% indicating ≥ 4.5 min of seizure activity and triggering a SE alert). The sensitivity and specificity of Claritγ’s real-time seizure burden measurements and SE alerts were compared to the majority consensus of at least two expert neurologists. Results Majority consensus of neurologists labeled the 353 EEGs as normal or slow activity (n = 249), highly epileptiform patterns (HEP, n = 87), or seizures [n = 17, nine longer than 5 min (e.g., SE), and eight shorter than 5 min]. The algorithm generated a SE alert (≥ 90% seizure burden) with 100% sensitivity and 93% specificity. The sensitivity and specificity of various thresholds for seizure burden during EEG recordings for detecting patients with seizures were 100% and 82% for ≥ 50% seizure burden and 88% and 60% for ≥ 10% seizure burden. Of the 179 EEG recordings in which the algorithm detected no seizures, seizures were identified by the expert reviewers in only two cases, indicating a negative predictive value of 99%. Discussion Claritγ detected SE events with high sensitivity and specificity, and it demonstrated a high negative predictive value for distinguishing nonepileptiform activity from seizure and highly epileptiform activity. Conclusions Ruling out seizures accurately in a large proportion of cases can help prevent unnecessary or aggressive over-treatment in critical care settings, where empiric treatment with antiseizure medications is currently prevalent. Claritγ’s high sensitivity for SE and high negative predictive value for cases without epileptiform activity make it a useful tool for triaging treatment and the need for urgent neurological consultation.

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Rapid Response Electroencephalography for Urgent Evaluation of Patients in Community Hospital Intensive Care Practice

Cited by 27 publications

References 16 publications

EEG findings in acutely ill patients investigated for SARS‐CoV‐2/COVID‐19: A small case series preliminary report

EEG findings in acutely ill patients investigated for SARS‐CoV‐2/COVID‐19: A small case series preliminary report

Severe Neurologic Complications of SARS-CoV-2

Monitoring the Burden of Seizures and Highly Epileptiform Patterns in Critical Care with a Novel Machine Learning Method

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