Heart Rate Variability to Quantify General Anesthesia Depth

NCT04788732 | Unknown DMC

• 1 other identifier: CAAE: 33066620.1.0000.5440
• Study Type: observational
• Target: 200
• Locations: 1 country
• Sites: 1

Brief Summary

The shortage of anesthetic agents can lead to intraoperative awareness while overdosing can trigger severe intra and postoperative problems. Therefore, monitoring anesthesia's depth (DoA: Depth of Anesthesia) is a crucial but still challenging task. Although some commercial monitors are based on electroencephalogram (EEG), designed to quantify DoA, their use in clinical practice has limitations. On the other hand, heart rate variability (HRV) has valuable information about physiological states, both from the heart and the organism. Classical indices derived from HRV have been shown to be able to differentiate the different stages of anesthesia. In this study, it is proposed to create a model to monitor DoA combining several HRV indices. Patients will be divided into three groups, according to the type of anesthesia to which they will be submitted (inhalation, total or balanced intravenous) and will have the electrocardiogram recorded during the entire surgical procedure. Various HRV indices will be calculated, and machine learning techniques will be used to combine and identify the most relevant index to compose a score that reliably represents DoA. Several commercial devices have been developed to monitor the level of consciousness during anesthesia. Among the most popular tools are included: Narcotrend TM (MonitorTechnik, Bad Bramstedt, Germany); the M-Entropy TM (GE Healthcare, Helsinki, Finland); Nindex SA (Controls, Montevideo, Uruguay) and the Bi-Spectral Index (BIS, TM Medtronic-Covidien, Dublin, Ireland). In this study, BIS or Nindex will also be monitored during the entire period that the patients remain anesthetized and will later be used to compose the DoA score based on HRV. As a result, a computer program will be created to monitor DoA in real-time.

Conditions

Anesthesia Awareness

Keywords

general anesthesia; consciousness; heart rate variability; general anaesthesia monitors; computational analysis

Outcome Measures

Primary Outcomes (1)

• To create a new Depth of Anesthesia (DoA) Score based on Heart Rate Variability indices

  Define a score capable of reflecting DoA, combining different indices derived from ECG, such as HRV.

  12 months

Secondary Outcomes (10)

• Impact of the differnet anesthetic drugs on the HRV indices (derived from ECG)

  12 months

• Impact of the anesthesia adjuvant drugs on the HRV indices (derived from ECG)

  12 months

• Time to wake up

  12 months

• Time spent in PACU

  12 months

• Aldrete score

  12 months

Study Arms (3)

1-Inhalation Anesthesia

patients in this group will be anesthetized only with an inhaled anesthetic ( Sevoflurane ).

Device: ECG and EEG monitoring

2-Total Intravenous Anesthesia

the patients in this group will be anesthetized with only intravenous drugs such as benzodiazepícos (midazolam), opioids (alfentanil, fentanyl, sufentanil, remifentanil), hypnotics ( propofol and etomidate ), associated or not to relaxing neuromuscular (nondepolarizing/depolarizing), and adjuvant drugs such as dextrocetamina, dexmedetomidine, lidocaine, and magnesium sulfate.

Device: ECG and EEG monitoring

3-Balanced anesthesia

the patients in this group will be anesthetized with blends of anesthetic inhaled (Sevoflurane) and intravenous drugs such as benzodiazepine (midazolam), opioids (alfentanil, fentanyl, sufentanil, remifentanil), hypnotics ( propofol and etomidate ), associated or not with neuromuscular relaxants (nonpolarizing/depolarizing) and adjuvant drugs such as dextrocetamine, dexmedetomidine, lidocaine, and magnesium sulfate.

Device: ECG and EEG monitoring

Interventions

ECG and EEG monitoring DEVICE

the ECG and EEG will also be monitored during the entire period that the patients remain anesthetized with various anesthetics drugs and will later be used to compose the DoA score based on HRV.

1-Inhalation Anesthesia; 2-Total Intravenous Anesthesia; 3-Balanced anesthesia

Eligibility Criteria

• Age: 0 Months+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients ASA (classification by the American Society of Anesthesiology) 1-3, of all ages, undergoing general anesthesia will be considered and divided into three groups, according to the type of anesthesia used: inhaled, total intravenous, or balanced.

You may qualify if:

• ASA patients (classification by the American Society of Anesthesiology) 1-3, of all ages, scheduled to undergo procedures under general anesthesia.

You may not qualify if:

• Patients with craniofacial deformities in which it is not possible to place the EEG sensors.
• Patients with severe eczema, allergy, or skin atopy.
• Patients with a history of severe autonomic dysfunction.
• Need of autonomic cardiac blockers during the intraoperative period.
• Absence of Consent.

Sponsors & Collaborators

• University of Sao Paulo: lead

Study Sites (1)

Waynice N. Paula-Garcia

Ribeirão Preto, São Paulo, 14.048-900, Brazil

RECRUITING

Related Publications (7)

• Guignard B. Monitoring analgesia. Best Pract Res Clin Anaesthesiol. 2006 Mar;20(1):161-80. doi: 10.1016/j.bpa.2005.09.002.

  PMID: 16634423 BACKGROUND

• Fahy BG, Chau DF. The Technology of Processed Electroencephalogram Monitoring Devices for Assessment of Depth of Anesthesia. Anesth Analg. 2018 Jan;126(1):111-117. doi: 10.1213/ANE.0000000000002331.

  PMID: 28786839 BACKGROUND

• Smith A. Literature review: Awareness of anaesthesia. J Perioper Pract. 2017 Sep;27(9):191-195. doi: 10.1177/175045891702700903.

  PMID: 29328752 BACKGROUND

• Merry AF, Cooper JB, Soyannwo O, Wilson IH, Eichhorn JH. International Standards for a Safe Practice of Anesthesia 2010. Can J Anaesth. 2010 Nov;57(11):1027-34. doi: 10.1007/s12630-010-9381-6. Epub 2010 Sep 21. No abstract available.

  PMID: 20857254 BACKGROUND

• Shander A, Lobel GP, Mathews DM. Brain Monitoring and the Depth of Anesthesia: Another Goldilocks Dilemma. Anesth Analg. 2018 Feb;126(2):705-709. doi: 10.1213/ANE.0000000000002383. No abstract available.

  PMID: 28787338 BACKGROUND

• Hajat Z, Ahmad N, Andrzejowski J. The role and limitations of EEG-based depth of anaesthesia monitoring in theatres and intensive care. Anaesthesia. 2017 Jan;72 Suppl 1:38-47. doi: 10.1111/anae.13739.

  PMID: 28044337 BACKGROUND

• Kissin I. Depth of anesthesia and bispectral index monitoring. Anesth Analg. 2000 May;90(5):1114-7. doi: 10.1097/00000539-200005000-00021. No abstract available.

  PMID: 10781463 BACKGROUND

Biospecimen

Retention: SAMPLES WITH DNA

whole blood and plasma sample

MeSH Terms

Conditions

Intraoperative Awareness

Interventions

Electrocardiography

Condition Hierarchy (Ancestors)

Intraoperative Complications; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Intervention Hierarchy (Ancestors)

Heart Function Tests; Diagnostic Techniques, Cardiovascular; Diagnostic Techniques and Procedures; Diagnosis; Electrodiagnosis

Central Study Contacts

Waynice N Paula-Garcia

CONTACT

+5516981154121; wgarcia@fmrp.usp.br

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 1 Year
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Assistant Professor, M.D, Ph.D

Study Record Dates

• First Submitted: March 1, 2021
• First Posted: March 9, 2021
• Study Start: January 4, 2021
• Primary Completion: May 30, 2022
• Study Completion: August 30, 2022
• Last Updated: March 9, 2021

Record last verified: 2021-03

Locations

BR (1)