Evidence map›Paper›PMID 40805076›Full record

ReviewAnimals : an open access journal from MDPI2025

Quantitative Variables Derived from the Electroencephalographic Signal to Assess Depth of Anaesthesia in Animals: A Narrative Review.

Susanne Figueroa, Olivier L Levionnois, Alessandro Mirra

Abstract readReview
In one paragraph

Review in Animals : an open access journal from MDPI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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.

Susanne FigueroaDivision of Anaesthesiology and Pain Therapy, Vetsuisse Faculty, University of Bern, 3012 Bern, Switzerland.
Olivier L LevionnoisDivision of Anaesthesiology and Pain Therapy, Vetsuisse Faculty, University of Bern, 3012 Bern, Switzerland.ORCID 0000-0003-0253-4682
Alessandro MirraDivision of Anaesthesiology and Pain Therapy, Vetsuisse Faculty, University of Bern, 3012 Bern, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurately assessing the depth of anaesthesia in animals remains a challenge, as traditional monitoring methods fail to capture subtle changes in brain activity. This review aimed to systematically map and critically evaluate the range of quantitative variables derived from electroencephalography (EEG) used to monitor sedation or anaesthesia in live animals, excluding laboratory rodents, over the past 35 years. Studies were identified through comprehensive searches in major biomedical databases (PubMed, Embase, CAB Abstract). To be included, studies had to report EEG use in relation to anaesthesia or sedation in living animals. A total of 169 studies were selected after screening and data extraction. Information was charted by animal species and reported EEG-derived variables. The most frequently reported variables were spectral edge frequencies, spectral power metrics, suppression ratio, and proprietary indices, such as the Bispectral Index. Methodological variability was high, and no consensus emerged on optimal EEG measures across species. While EEG-derived quantitative variables provide valuable insights, their interpretation remains highly context-dependent. Further research is necessary to refine these methods, explore variable combinations, and improve their clinical relevance in veterinary medicine.

Indexed as

animal anaesthesiadepth of anaesthesiaelectroencephalographynociceptionquantitative EEG variablesveterinary monitoring

Identifiers

PMID40805076
PMCPMC12345460

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.