Study on the Influencing Factors of Electroencephalogram Parameters Under Anesthesia

NCT ID: NCT04760028

Last Updated: 2024-06-25

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Clinical Phase

PHASE4

Total Enrollment

80 participants

Study Classification

INTERVENTIONAL

Study Start Date

2023-03-01

Study Completion Date

2024-03-30

Brief Summary

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The depth of anaesthesia is affected by the age factor in the interpretation of EEG information. Only by fully considering the influence of the age factor can we accurately grasp the depth of anaesthesia.

Detailed Description

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In the process of general anesthesia, the reliable judgment of the depth of anesthesia has always been the focus of anesthesiologists.In recent decades, researchers have discovered that it is possible to monitor the effects of anesthesia using electroencephalography (EEG), so the information has come to be used to guide anesthesia management.The indicators derived from electroencephalogram (EEG) that can be used to judge the depth of anesthesia during general anesthesia, such as spectral edge frequency, burst inhibition rate, median power frequency, etc., were successively born. Thereafter, commercial instruments for monitoring the depth of anesthesia, such as EEG dual-frequency index, entropy module, Narcotrend index, etc., were also widely used in clinical practice.Although the anesthesia measurements or instrument has many advantages, but the use of these indicators are not considering the factors of age, and have been well documented for general anesthesia electroencephalography (eeg) of different ages is different, this means that in different age groups using the same monitoring indicators will lead to the judgment of anesthesia depth is larger error.Therefore, age should be considered in the process of monitoring the depth of clinical anesthesia, and appropriate indexes should be selected according to different ages to monitor the depth of anesthesia.However, it has not been thoroughly studied which indexes can be used to monitor the depth of anesthesia in each age group and how to conduct reasonable monitoring.Therefore, it is necessary to explore the monitoring indexes of depth of anesthesia in different age groups and the most appropriate monitoring program.

The purpose of this study was to validate EEG indicators that can monitor the depth of anesthesia at different ages, including basic EEG power spectrum parameters and processed parameters, and to explore whether there is an optimal protocol for monitoring the depth of anesthesia at different ages.In order to facilitate the clinical selection of patients of different age groups to monitor the depth of anesthesia, the factor of age is considered so as to take individual monitoring.Finally, it can accurately judge the depth of anesthesia and accurately use the anesthetic drugs.

Conditions

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EEC

Study Design

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Allocation Method

NA

Intervention Model

SINGLE_GROUP

Primary Study Purpose

OTHER

Blinding Strategy

NONE

Study Groups

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sevoflurane

Adjust sevoflurane concentration as required

Group Type OTHER

Adjusting the depth of anesthesia

Intervention Type PROCEDURE

After the main procedure, the depth of anesthesia can be adjusted as needed

Interventions

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Adjusting the depth of anesthesia

After the main procedure, the depth of anesthesia can be adjusted as needed

Intervention Type PROCEDURE

Eligibility Criteria

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Inclusion Criteria

1. ASA Ⅰ \~ Ⅲ level
2. 18 to 100 years
3. BMI 19 \~ 30 kg/m squared
4. Patients without intracranial surgery

Exclusion Criteria

1. nervous system diseases, abnormal EEG, old cerebral infarction, the use of drugs affecting the nervous system;
2. coronary heart disease, hypertension without regular treatment or poor control;
3. The oxygen partial pressure and calcium ion levels were abnormal before anesthesia induction;
4. History of alcohol abuse.
Minimum Eligible Age

18 Years

Maximum Eligible Age

100 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Tianjin Medical University Second Hospital

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Locations

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The Second Hospital of Tianjin Medical University

Tianjin, , China

Site Status

Countries

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China

References

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Monk TG, Saini V, Weldon BC, Sigl JC. Anesthetic management and one-year mortality after noncardiac surgery. Anesth Analg. 2005 Jan;100(1):4-10. doi: 10.1213/01.ANE.0000147519.82841.5E.

Reference Type BACKGROUND
PMID: 15616043 (View on PubMed)

Brown EN, Purdon PL. The aging brain and anesthesia. Curr Opin Anaesthesiol. 2013 Aug;26(4):414-9. doi: 10.1097/ACO.0b013e328362d183.

Reference Type BACKGROUND
PMID: 23820102 (View on PubMed)

Bennett C, Voss LJ, Barnard JP, Sleigh JW. Practical use of the raw electroencephalogram waveform during general anesthesia: the art and science. Anesth Analg. 2009 Aug;109(2):539-50. doi: 10.1213/ane.0b013e3181a9fc38.

Reference Type BACKGROUND
PMID: 19608830 (View on PubMed)

Otto KA, Cebotari S, Hoffler HK, Tudorache I. Electroencephalographic Narcotrend index, spectral edge frequency and median power frequency as guide to anaesthetic depth for cardiac surgery in laboratory sheep. Vet J. 2012 Mar;191(3):354-9. doi: 10.1016/j.tvjl.2011.02.023. Epub 2011 Mar 30.

Reference Type BACKGROUND
PMID: 21454112 (View on PubMed)

Schultz A, Siedenberg M, Grouven U, Kneif T, Schultz B. Comparison of Narcotrend Index, Bispectral Index, spectral and entropy parameters during induction of propofol-remifentanil anaesthesia. J Clin Monit Comput. 2008 Apr;22(2):103-11. doi: 10.1007/s10877-008-9111-6. Epub 2008 Feb 21.

Reference Type BACKGROUND
PMID: 18288579 (View on PubMed)

Avidan MS, Zhang L, Burnside BA, Finkel KJ, Searleman AC, Selvidge JA, Saager L, Turner MS, Rao S, Bottros M, Hantler C, Jacobsohn E, Evers AS. Anesthesia awareness and the bispectral index. N Engl J Med. 2008 Mar 13;358(11):1097-108. doi: 10.1056/NEJMoa0707361.

Reference Type BACKGROUND
PMID: 18337600 (View on PubMed)

Hayashi K, Indo K, Sawa T. Anaesthesia-dependent oscillatory EEG features in the super-elderly. Clin Neurophysiol. 2020 Sep;131(9):2150-2157. doi: 10.1016/j.clinph.2020.05.027. Epub 2020 Jun 23.

Reference Type BACKGROUND
PMID: 32682243 (View on PubMed)

Kreuzer M, Stern MA, Hight D, Berger S, Schneider G, Sleigh JW, Garcia PS. Spectral and Entropic Features Are Altered by Age in the Electroencephalogram in Patients under Sevoflurane Anesthesia. Anesthesiology. 2020 May;132(5):1003-1016. doi: 10.1097/ALN.0000000000003182.

Reference Type BACKGROUND
PMID: 32108685 (View on PubMed)

Aranake A, Mashour GA, Avidan MS. Minimum alveolar concentration: ongoing relevance and clinical utility. Anaesthesia. 2013 May;68(5):512-22. doi: 10.1111/anae.12168. Epub 2013 Feb 16.

Reference Type BACKGROUND
PMID: 23414556 (View on PubMed)

Blain-Moraes S, Tarnal V, Vanini G, Alexander A, Rosen D, Shortal B, Janke E, Mashour GA. Neurophysiological correlates of sevoflurane-induced unconsciousness. Anesthesiology. 2015 Feb;122(2):307-16. doi: 10.1097/ALN.0000000000000482.

Reference Type BACKGROUND
PMID: 25296108 (View on PubMed)

Smith WD, Dutton RC, Smith NT. Measuring the performance of anesthetic depth indicators. Anesthesiology. 1996 Jan;84(1):38-51. doi: 10.1097/00000542-199601000-00005.

Reference Type BACKGROUND
PMID: 8572353 (View on PubMed)

Li X, Li D, Liang Z, Voss LJ, Sleigh JW. Analysis of depth of anesthesia with Hilbert-Huang spectral entropy. Clin Neurophysiol. 2008 Nov;119(11):2465-75. doi: 10.1016/j.clinph.2008.08.006. Epub 2008 Sep 21.

Reference Type BACKGROUND
PMID: 18812265 (View on PubMed)

Other Identifiers

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EEG

Identifier Type: -

Identifier Source: org_study_id

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