Evidence map›Paper›PMID 35986096›Full record

ReviewJournal of neurology2022

Recent applications of quantitative electroencephalography in adult intensive care units: a comprehensive review.

Jaeho Hwang, Sung-Min Cho, Eva K Ritzl

Open access · greenAbstract readReview
In one paragraph

Review in Journal of neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
2.6field-weighted citation impact, top 10% 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

11 citing papers in PubMed, 22 citations in OpenAlex.

  1. Article
  2. Article
  3. ICA-SFrontiers in neuroscience · 2026
    Article
  4. Review
  5. Review
  6. Using artificial intelligence to optimize anti-seizure treatment and EEG-guided decisions in severe brain injury.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2025
    Review
  7. Article
  8. Discovery of VU6024578/BI02982816: An mGluJournal of medicinal chemistry · 2024
    Article
  9. Review
  10. Article
  11. Article
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

3 authors at 1 institution in 1 country.

Jaeho HwangDepartment of Neurology, Johns Hopkins Hospital, Baltimore, MD, USA. jh@jhmi.edu.ORCID http://orcid.org/0000-0002-1803-2524
Sung-Min Cho *Department of Neurology, Johns Hopkins Hospital, Baltimore, MD, USA.
Eva K Ritzl *Department of Neurology, Johns Hopkins Hospital, Baltimore, MD, USA.
Johns Hopkins Hospital · US

Funding

CLots and Oxygen in Va-ExtracorpoReal membrane oxygenation (CLOVER) studyK23HL157610 · NHLBI · JOHNS HOPKINS UNIVERSITY · PI Sung-Min Cho · 2022 to 2026
$797k
NHLBI NIH HHS K23 HL157610
6 · The paper itself

Abstract

Quantitative electroencephalography (qEEG) refers to the numerical analysis and/or visual transformations of raw electroencephalography (EEG) signals. Evaluation of qEEG in intensive care units (ICU) faces unique challenges that warrant investigation separate from those conducted in other settings. Additionally, the pathophysiology, management, and EEG patterns of critically ill conditions often significantly differ between adults and children. Thus, it is important to distinguish the literature on qEEGs specifically performed in adult ICUs. The aim of this review is to summarize the studies using qEEG for clinical evaluation of patients in adult ICUs performed over the past decade (since 2010), and to present the state of the art of these techniques. Overall, these studies have reported that qEEG can reveal important information faster than typically possible with traditional methods of reviewing the raw EEG only, with reasonable accuracy. However, it is crucial to emphasize that qEEG must be reviewed in conjunction with raw EEG and in context of understanding the patients' clinical status. Because each qEEG panel only focuses on a few aspects of the entire EEG, different combinations of qEEG panels may be required for optimal analyses of each medical condition and individual patient. Currently in practical terms, qEEG can serve as a complementary, valuable tool for portions of the EEG that require more detailed review. Further multi-center collaborative studies are needed to ultimately develop standardized methods of employing qEEG that are generalizable across institutions. As qEEG techniques continue to advance, including those involving machine learning, qEEG will further benefit from algorithms specifically suited for ICUs.

Indexed as

ElectroencephalographyIntensive Care UnitsAdultAlgorithmsChildHumansAdult intensive careContinuous electroencephalographyQuantitative electroencephalography

Identifiers

PMID35986096
PMCPMC9633458
OpenAlexW4292366572

What OpenQuestion holds

Textmetadata
LicenceTDM
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.