Evidence map›Paper›PMID 29923167›Full record

Observational studyNeurocritical care2018

Rapid Bedside Evaluation of Seizures in the ICU by Listening to the Sound of Brainwaves: A Prospective Observational Clinical Trial of Ceribell's Brain Stethoscope Function.

Kyle Hobbs, Prashanth Krishnamohan, Catherine Legault, Steve Goodman, Josef Parvizi, Kapil Gururangan, Michael Mlynash

Abstract readObservational Study
PubMed Publisher
In one paragraph

Observational study in Neurocritical care, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 1 pooled it
2.8field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed, 1 synthesis or guideline pooled it, 43 citations in OpenAlex.

  1. Pooled it
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  6. Observational
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  11. Review
  12. Big data and predictive analytics in neurocritical care.Current neurology and neuroscience reports · 2022
    Review
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  16. Observational
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 1 institution in 1 country.

Kyle HobbsDepartment of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA. khobbs@wakehealth.edu.
Prashanth KrishnamohanDepartment of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA.
Catherine LegaultDepartment of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA.
Steve GoodmanDepartment of Health Research and Policy, Stanford University School of Medicine, Stanford, CA, USA.
Josef ParviziDepartment of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA.
Kapil GururanganDepartment of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA.
Michael MlynashDepartment of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA.
Stanford University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPatients suffering from non-convulsive seizures experience delays in diagnosis and treatment due to limitations in acquiring and interpreting electroencephalography (EEG) data. The Ceribell EEG System offers rapid EEG acquisition and conversion of EEG signals to sound (sonification) using a proprietary algorithm. This study was designed to test the performance of this EEG system in an intensive care unit (ICU) setting and measure its impact on clinician treatment decision.

methodsEncephalopathic ICU patients at Stanford University Hospital were enrolled if clinical suspicion for seizures warranted EEG monitoring. Treating physicians rated suspicion for seizure and decided if the patient needed antiepileptic drug (AED) treatment at the time of bedside evaluation. After listening to 30 s of EEG from each hemisphere in each patient, they reevaluated their suspicion for seizure and decision for additional treatment. The EEG waveforms recorded with Ceribell EEG were subsequently analyzed by three blinded epileptologists to assess the presence or absence of seizures within and outside the sonification window. Study outcomes were EEG set up time, ease of use of the device, change in clinician seizure suspicion, and change in decision to treat with AED before and after sonification.

resultsThirty-five cases of EEG sonification were performed. Mean EEG setup time was 6 ± 3 min, and time to obtain sonified EEG was significantly faster than conventional EEG (p < 0.001). One patient had non-convulsive seizure during sonification and another had rhythmic activity that was followed by seizure shortly after sonification. Change in treatment decision after sonification occurred in approximately 40% of patients and resulted in a significant net reduction in unnecessary additional treatments (p = 0.01). Ceribell EEG System was consistently rated easy to use.

conclusionThe Ceribell EEG System enabled rapid acquisition of EEG in patients at risk for non-convulsive seizures and aided clinicians in their evaluation of encephalopathic ICU patients. The ease of use and speed of EEG acquisition and interpretation by EEG-untrained individuals has the potential to improve emergent clinical decision making by quickly detecting non-convulsive seizures in the ICU.

Indexed as

Brain WavesElectroencephalographyNeurophysiological MonitoringSeizuresAdultAgedAged, 80 and overCritical CareFemaleHumansIntensive Care UnitsMaleMiddle AgedPoint-of-Care TestingProspective StudiesYoung AdultClinical trialElectroencephalographySonificationStatus epilepticus

Identifiers

PMID29923167
OpenAlexW2809199446

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.