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Clinical Trial Details — Status: Recruiting

Administrative data

NCT number NCT04788732
Other study ID # CAAE: 33066620.1.0000.5440
Secondary ID
Status Recruiting
Phase
First received
Last updated
Start date January 4, 2021
Est. completion date August 30, 2022

Study information

Verified date March 2021
Source University of Sao Paulo
Contact Waynice N Paula-Garcia
Phone +5516981154121
Email wgarcia@fmrp.usp.br
Is FDA regulated No
Health authority
Study type Observational [Patient Registry]

Clinical Trial 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.


Description:

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Study Design


Related Conditions & MeSH terms


Intervention

Device:
ECG and EEG monitoring
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.

Locations

Country Name City State
Brazil Waynice N. Paula-Garcia Ribeirão Preto Sao Paulo

Sponsors (1)

Lead Sponsor Collaborator
University of Sao Paulo

Country where clinical trial is conducted

Brazil, 

References & Publications (7)

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. Review. — View Citation

Guignard B. Monitoring analgesia. Best Pract Res Clin Anaesthesiol. 2006 Mar;20(1):161-80. Review. — View Citation

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. Review. — View Citation

Kissin I. Depth of anesthesia and bispectral index monitoring. Anesth Analg. 2000 May;90(5):1114-7. — View Citation

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. — View Citation

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. Review. — View Citation

Smith A. Literature review: Awareness of anaesthesia. J Perioper Pract. 2017 Sep;27(9):191-195. Review. — View Citation

Outcome

Type Measure Description Time frame Safety issue
Primary 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 Impact of the differnet anesthetic drugs on the HRV indices (derived from ECG) Correlate HRV indices (derived from ECG) with the concentration and final consumption of anesthetic drugs throughout the anesthetic-surgical procedure. 12 months
Secondary Impact of the anesthesia adjuvant drugs on the HRV indices (derived from ECG) Correlate HRV indices (derived from ECG) with the concentration and final consumption of adjuvant drugs (for example: lidocaine, dexmedetomedine, magnesium sulfate) throughout the anesthetic-surgical procedure. 12 months
Secondary Time to wake up Correlate HRV indices (derived from ECG) with the time to wake up 12 months
Secondary Time spent in PACU Correlate HRV indices (derived from ECG) with the time spent in PACU 12 months
Secondary Aldrete score Correlate HRV indices (derived from ECG) with the Aldrete score 12 months
Secondary Incidence of nausea and vomiting Correlate HRV indices (derived from ECG) with the incidence of postoperative nausea and vomiting, . 12 months
Secondary Incidence of delirium Correlate HRV indices (derived from ECG) with the incidence of postoperative delirium 12 months
Secondary Incidence of intraoperative memory. Correlate HRV indices (derived from ECG) with the incidence of intraoperative memory. 12 months
Secondary Impact of frailty on the HRV indexes (derived from ECG) The impact of pre-anesthetic frailty (evaluated by the Clinical Frailty Scale) on ECG and EEG variables in the intraoperative period. 12 months
Secondary Impact of the basal cognitive status on the HRV indexes (derived from ECG) The impact of the basal cognitive status on ECG and EEG variables in the intraoperative period. 12 months
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