Different Electroencephalographic Profiles of Remimazolam and Propofol after Anesthesia
Poster No:
1663
Submission Type:
Abstract Submission
Authors:
Yeji Lee1, Youngjai Park1, Hyoungkyu Kim1, Sujung Park2, Bon-Nyeo Koo2, Joon-Young Moon1
Institutions:
1Sungkyunkwan University/Institute for Basic Science, Suwon, Republic of Korea, 2Department of Anesthesiology and Pain Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
First Author:
Co-Author(s):
Sujung Park
Department of Anesthesiology and Pain Medicine, Yonsei University College of Medicine
Seoul, Republic of Korea
Department of Anesthesiology and Pain Medicine, Yonsei University College of Medicine
Seoul, Republic of Korea
Bon-Nyeo Koo
Department of Anesthesiology and Pain Medicine, Yonsei University College of Medicine
Seoul, Republic of Korea
Department of Anesthesiology and Pain Medicine, Yonsei University College of Medicine
Seoul, Republic of Korea
Introduction:
Understanding the dynamic changes of EEG characteristics after emergence from anesthesia (AE) is crucial for distinguishing the risk of postoperative neurocognitive disorder. Previous research has found that changes of alpha-oscillatory activity (8-12 Hz) is a decisive factor for detecting such disorder at risk [1]. Remimazolam is a newly introduced ultra-short-acting benzodiazepine anesthetic, and few studies have compared its changes of EEG patterns with Propofol after AE. Recent studies of clinical trials have reported delayed emergence in Remimazoalm versus Propofol [2], but the underlying mechanism remains elusive. Hence, we compare the characteristics of EEG patterns between Remimazolam and Propofol after AE. By applying various phase measures, we aim to understand the underlying mechanism of differing EEG characteristics between the two anesthetic drugs. Ultimately, we aim to suggest these measures as alternative indicators for assessing the anesthetic depth, the quality of recovery, and the disorder at risk.
Methods:
Various phase measures were applied to four-channel electroencephalogram (EEG) data (sampling rate 178 Hz), which were recorded from Sedline device at Yonsei Severance Hospital. The data includes 50 patients undergoing laparoscopic cholecystectomy who were randomly assigned to the remimazolam group (n = 25) and the propofol group (n = 25). We upsampled the data to 200 Hz. Specifically, we applied phase lag entropy (PLE), coherence, phase coherence, and the absolute value of relative phase, as described in the Measurements section below, at specific frequency bands (i.e., delta: 0.5-4, theta: 4-8, alpha: 8-12, beta: 12-20, gamma: 20-40, and broad: 6-25 Hz). We considered a broad frequency band in our analysis following the evidence from the literature that rhythmic activity induced by propofol starts in the broad band [3].
Preprocessing
As EEG characteristics are distinctly exhibited in different anesthetic states, the data were divided into 6 states: (1) baseline eyes closed (BEC), defined as 3 minutes after eyes closed; (2) baseline eyes open (BEO), defined as 3 minutes after eyes open; (3) after intubation (AI), defined as 15 minutes after the intubation point; (4) recovery of consciousness (ROC), defined as 15 minutes before the extubation point; (5) eyes closed (EC), defined as 3 minutes after eyes closed; (6) eyes open (EO), defined as 3 minutes after eyes open.
Preprocessing
As EEG characteristics are distinctly exhibited in different anesthetic states, the data were divided into 6 states: (1) baseline eyes closed (BEC), defined as 3 minutes after eyes closed; (2) baseline eyes open (BEO), defined as 3 minutes after eyes open; (3) after intubation (AI), defined as 15 minutes after the intubation point; (4) recovery of consciousness (ROC), defined as 15 minutes before the extubation point; (5) eyes closed (EC), defined as 3 minutes after eyes closed; (6) eyes open (EO), defined as 3 minutes after eyes open.
Results:
(a) PLE measures the pattern diversity of phase lag difference between two channels. 0 indicates simplicity of patterns, thus lead/lag relationship is predictable; 1 indicates diversity of patterns, thus lead/lag relationship is unpredictable [3]. (b) Coherence measures the coherency of phase magnitude and the degree of synchronization between two channels. 0 indicates an absence of coherence; 1 indicates the presence of coherence [4]. (c) Phase coherence measures the degree of phase locking between the two channels. 0 indicates an absence of phase locking; 1 indicates complete phase locking [5]. (d) |relative phase| measures the magnitude of phase relationship between two channels. 0 indicates the absence of lead/lag relationship between the channels; 1 indicates the presence of lead/lag relationship [6]. In our analysis, we observed a significant group difference after AE from the four selected phase measures at the broad frequency band (Fig. 1). From all four measures, we found that the Propofol group recovered up to the baseline states faster than the Remimazolam group. Especially, the group difference was observed in PLE, where the phase relationship patterns were highly unpredictable in the Propofol group than the Remimazolam group after AE.
Conclusions:
These findings were in accordance with the previous study [2], indicating delayed emergence after general anesthesia with Remimazolam. Our observation suggests that this delayed emergence is linked with the residual effects observed after recovery from Remimazolam anesthesia.
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis 1
Novel Imaging Acquisition Methods:
EEG 2
Perception, Attention and Motor Behavior:
Consciousness and Awareness
Keywords:
Consciousness
Electroencephaolography (EEG)
1|2Indicates the priority used for review
Provide references using author date format
[2] Takemori, T. et al. (2022), ‘Long-term delayed emergence after remimazolam-based general anesthesia: a case report’, JA Clinical Reports, vol. 8, no. 86.
[3] Lee, H. et al. (2017), ‘Diversity of functional connectivity patterns is reduced in propofol-induced unconsciousness’, Human Brain Mapping, vol. 38, no. 10, pp. 4980-4995.
[4] Nunez, P.L. (2006), ‘Electric fields of the brain: the neurophysics of EEG’, Oxford: Oxford University Press.
[5] Mormann, F. et al. (2000), ‘Mean phase coherence as a measure for phase synchronization and its application to the EEG of epilepsy patients’, Physica D: Nonlinear Phenomena, vol. 144, no. 3-4, pp. 358-369.
[6] Moon, J.Y. et al. (2017), ‘Structure shapes dynamics and directionality in diverse brain networks: mathematical principles and empirical confirmation in three species’, Science Reports, vol. 7, 46606.