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Obert DP, Killing D, Happe T, Tamas P, Altunkaya A, Dragovic SZ, Kreuzer M, Schneider G, Fenzl T. Substance specific EEG patterns in mice undergoing slow anesthesia induction. BMC Anesthesiol 2024; 24:167. [PMID: 38702608 PMCID: PMC11067159 DOI: 10.1186/s12871-024-02552-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Accepted: 04/26/2024] [Indexed: 05/06/2024] Open
Abstract
The exact mechanisms and the neural circuits involved in anesthesia induced unconsciousness are still not fully understood. To elucidate them valid animal models are necessary. Since the most commonly used species in neuroscience are mice, we established a murine model for commonly used anesthetics/sedatives and evaluated the epidural electroencephalographic (EEG) patterns during slow anesthesia induction and emergence. Forty-four mice underwent surgery in which we inserted a central venous catheter and implanted nine intracranial electrodes above the prefrontal, motor, sensory, and visual cortex. After at least one week of recovery, mice were anesthetized either by inhalational sevoflurane or intravenous propofol, ketamine, or dexmedetomidine. We evaluated the loss and return of righting reflex (LORR/RORR) and recorded the electrocorticogram. For spectral analysis we focused on the prefrontal and visual cortex. In addition to analyzing the power spectral density at specific time points we evaluated the changes in the spectral power distribution longitudinally. The median time to LORR after start anesthesia ranged from 1080 [1st quartile: 960; 3rd quartile: 1080]s under sevoflurane anesthesia to 1541 [1455; 1890]s with ketamine. Around LORR sevoflurane as well as propofol induced a decrease in the theta/alpha band and an increase in the beta/gamma band. Dexmedetomidine infusion resulted in a shift towards lower frequencies with an increase in the delta range. Ketamine induced stronger activity in the higher frequencies. Our results showed substance-specific changes in EEG patterns during slow anesthesia induction. These patterns were partially identical to previous observations in humans, but also included significant differences, especially in the low frequencies. Our study emphasizes strengths and limitations of murine models in neuroscience and provides an important basis for future studies investigating complex neurophysiological mechanisms.
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Affiliation(s)
- David P Obert
- School of Medicine and Health, Department of Anesthesiology and Intensive Care, Technical University of Munich, 81675, Munich, Germany
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts's General Hospital, Boston, MA, 02114, USA
- Harvard Medical School, Boston, MA, 02115, USA
| | - David Killing
- School of Medicine and Health, Department of Anesthesiology and Intensive Care, Technical University of Munich, 81675, Munich, Germany
| | - Tom Happe
- School of Medicine and Health, Department of Anesthesiology and Intensive Care, Technical University of Munich, 81675, Munich, Germany
| | - Philipp Tamas
- School of Medicine and Health, Department of Anesthesiology and Intensive Care, Technical University of Munich, 81675, Munich, Germany
| | - Alp Altunkaya
- School of Medicine and Health, Department of Anesthesiology and Intensive Care, Technical University of Munich, 81675, Munich, Germany
| | - Srdjan Z Dragovic
- School of Medicine and Health, Department of Anesthesiology and Intensive Care, Technical University of Munich, 81675, Munich, Germany
| | - Matthias Kreuzer
- School of Medicine and Health, Department of Anesthesiology and Intensive Care, Technical University of Munich, 81675, Munich, Germany
| | - Gerhard Schneider
- School of Medicine and Health, Department of Anesthesiology and Intensive Care, Technical University of Munich, 81675, Munich, Germany
| | - Thomas Fenzl
- School of Medicine and Health, Department of Anesthesiology and Intensive Care, Technical University of Munich, 81675, Munich, Germany.
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2
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Liu J, Zhang W, Hu S, Wu C, Dong K, Wei Q, Wang G, Fang J, Zhang D, Lan M, Zhang F, Sun H. Analysis of Amplitude Modulation of EEG Based on Holo-Hilbert Spectrum Analysis During General Anesthesia. IEEE Trans Biomed Eng 2024; 71:1607-1616. [PMID: 38285584 DOI: 10.1109/tbme.2023.3345942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2024]
Abstract
OBJECTIVE The study aims to investigate the relationship between amplitude modulation (AM) of EEG and anesthesia depth during general anesthesia. METHODS In this study, Holo-Hilbert spectrum analysis (HHSA) was used to decompose the multichannel EEG signals of 15 patients to obtain the spatial distribution of AM in the brain. Subsequently, HHSA was applied to the prefrontal EEG (Fp1) obtained during general anesthesia surgery in 15 and 34 patients, and the α-θ and α-δ regions of feature (ROFs) were defined in Holo-Hilbert spectrum (HHS) and three features were derived to quantify AM in ROFs. RESULTS During anesthetized phase, an anteriorization of the spatial distribution of AMs of α-carrier in brain was observed, as well as AMs of α-θ and α-δ in the EEG of Fp1. The total power ([Formula: see text]), mean carrier frequency ([Formula: see text]) and mean amplitude frequency ([Formula: see text]) of AMs changed during different anesthesia states. CONCLUSION HHSA can effectively analyze the cross-frequency coupling of EEG during anesthesia and the AM features may be applied to anesthesia monitoring. SIGNIFICANCE The study provides a new perspective for the characterization of brain states during general anesthesia, which is of great significance for exploring new features of anesthesia monitoring.
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Loison V, Voskobiynyk Y, Lindquist B, Necula D, Longrois D, Paz J, Holcman D. Mapping general anesthesia states based on electro-encephalogram transition phases. Neuroimage 2024; 285:120498. [PMID: 38135170 PMCID: PMC10792552 DOI: 10.1016/j.neuroimage.2023.120498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 12/08/2023] [Accepted: 12/11/2023] [Indexed: 12/24/2023] Open
Abstract
Cortical electro-encephalography (EEG) served as the clinical reference for monitoring unconsciousness during general anesthesia. The existing EEG-based monitors classified general anesthesia states as underdosed, adequate, or overdosed, lacking predictive power due to the absence of transition phases among these states. In response to this limitation, we undertook an analysis of the EEG signal during isoflurane-induced general anesthesia in mice. Adopting a data-driven approach, we applied signal processing techniques to track θ- and δ-band dynamics, along with iso-electric suppressions. Combining this approach with machine learning, we successfully developed an automated algorithm. The findings of our study revealed that the dampening of the δ-band occurred several minutes before the onset of significant iso-electric suppression episodes. Furthermore, a distinct γ-frequency oscillation was observed, persisting for several minutes during the recovery phase subsequent to isoflurane-induced overdose. As a result of our research, we generated a map summarizing multiple brain states and their transitions, offering a tool for predicting and preventing overdose during general anesthesia. The transition phases identified, along with the developed algorithm, have the potential to be generalized, enabling clinicians to prevent inadequate anesthesia and, consequently, tailor anesthetic regimens to individual patients.
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Affiliation(s)
- V Loison
- Group of Data Modeling and Computational Biology, Institut de Biologie (IBENS), École Normale Supérieure CNRS, Université PSL Paris, France
| | - Y Voskobiynyk
- Gladstone Institutes, USA; Gladstone Institute of Neurological Disease, University of California, San Francisco, USA
| | - B Lindquist
- Gladstone Institutes, USA; Gladstone Institute of Neurological Disease, University of California, San Francisco, USA
| | - D Necula
- Gladstone Institutes, USA; Gladstone Institute of Neurological Disease, University of California, San Francisco, USA
| | - D Longrois
- Département d'Anesthésie-Réanimation, Hôpital Bichat-Claude Bernard, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - J Paz
- Gladstone Institutes, USA; Gladstone Institute of Neurological Disease, University of California, San Francisco, USA
| | - D Holcman
- Group of Data Modeling and Computational Biology, Institut de Biologie (IBENS), École Normale Supérieure CNRS, Université PSL Paris, France; DAMPT, University of Cambridge and Churchill College, CB30DS, Cambridge, UK.
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4
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Arina P, Kaczorek MR, Hofmaenner DA, Pisciotta W, Refinetti P, Singer M, Mazomenos EB, Whittle J. Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools. Anesthesiology 2024; 140:85-101. [PMID: 37944114 DOI: 10.1097/aln.0000000000004764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2023]
Abstract
BACKGROUND The utilization of artificial intelligence and machine learning as diagnostic and predictive tools in perioperative medicine holds great promise. Indeed, many studies have been performed in recent years to explore the potential. The purpose of this systematic review is to assess the current state of machine learning in perioperative medicine, its utility in prediction of complications and prognostication, and limitations related to bias and validation. METHODS A multidisciplinary team of clinicians and engineers conducted a systematic review using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) protocol. Multiple databases were searched, including Scopus, Cumulative Index to Nursing and Allied Health Literature (CINAHL), the Cochrane Library, PubMed, Medline, Embase, and Web of Science. The systematic review focused on study design, type of machine learning model used, validation techniques applied, and reported model performance on prediction of complications and prognostication. This review further classified outcomes and machine learning applications using an ad hoc classification system. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was used to assess risk of bias and applicability of the studies. RESULTS A total of 103 studies were identified. The models reported in the literature were primarily based on single-center validations (75%), with only 13% being externally validated across multiple centers. Most of the mortality models demonstrated a limited ability to discriminate and classify effectively. The PROBAST assessment indicated a high risk of systematic errors in predicted outcomes and artificial intelligence or machine learning applications. CONCLUSIONS The findings indicate that the development of this field is still in its early stages. This systematic review indicates that application of machine learning in perioperative medicine is still at an early stage. While many studies suggest potential utility, several key challenges must be first overcome before their introduction into clinical practice. EDITOR’S PERSPECTIVE
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Affiliation(s)
- Pietro Arina
- Bloomsbury Institute of Intensive Care Medicine and Human Physiology and Performance Laboratory, Centre for Perioperative Medicine, Department of Targeted Intervention, University College London, London, United Kingdom
| | - Maciej R Kaczorek
- Wellcome/EPSRC Centre of Interventional and Surgical Sciences and Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Daniel A Hofmaenner
- Bloomsbury Institute of Intensive Care Medicine, University College London, London, United Kingdom; and Institute of Intensive Care Medicine, University Hospital Zurich, Zurich, Switzerland
| | - Walter Pisciotta
- Bloomsbury Institute of Intensive Care Medicine, University College London, London, United Kingdom
| | - Patricia Refinetti
- Human Physiology and Performance Laboratory, Centre for Perioperative Medicine, Department of Targeted Intervention, University College London, London, United Kingdom
| | - Mervyn Singer
- Bloomsbury Institute of Intensive Care Medicine, University College London, London, United Kingdom
| | - Evangelos B Mazomenos
- Wellcome/EPSRC Centre of Interventional and Surgical Sciences and Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - John Whittle
- Human Physiology and Performance Laboratory, Centre for Perioperative Medicine, Department of Targeted Intervention, University College London, London, United Kingdom
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Guessous K, Touchard C, Glezerson B, Levé C, Sabbagh D, Mebazaa A, Gayat E, Paquet C, Vallée F, Cartailler J. Intraoperative Electroencephalography Alpha-Band Power Is a Better Proxy for Preoperative Low MoCA Under Propofol Compared With Sevoflurane. Anesth Analg 2023; 137:1084-1092. [PMID: 37014984 DOI: 10.1213/ane.0000000000006422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/06/2023]
Abstract
BACKGROUND Preoperative abnormal cognitive status is a risk factor for postoperative complications yet remains underdiagnosed. During propofol general anesthesia, intraoperative electroencephalography (EEG) variables, such as alpha band power (α-BP), correlate with cognitive status. This relationship under sevoflurane is unclear. We investigated whether EEG biomarkers of poor cognitive status found under propofol could be extended to sevoflurane. METHODS In this monocentric prospective observational study, 106 patients with intraoperative EEG monitoring were included (propofol/sevoflurane = 55/51). We administered the Montreal Cognitive Assessment (MoCA) scale to identify abnormal cognition (low MoCA) 1 day before intervention. EEG variables included delta to beta frequency band powers. Results were adjusted to age and drug dosage. We assessed depth of anesthesia (DoA) using the spectral edge frequency (SEF 95 ) and maintained it within (8-13) Hz. RESULTS The difference in α-BP between low and normal MoCA patients was significantly larger among propofol patients (propofol: 4.3 ± 4.8 dB versus sevoflurane: 1.5 ± 3.4 dB, P = .022). SEF 95 and age were not statistically different between sevoflurane and propofol groups. After adjusting to age and dose, low α-BP was significantly associated with low MoCA under propofol (odds ratio [OR] [confidence interval {CI}] = 0.39 [0.16-0.94], P = .034), but not under sevoflurane, where theta-band power was significantly associated with low MoCA (OR [CI] = 0.31 [0.13-0.73], P = .007). CONCLUSIONS We suggest that intraoperative EEG biomarkers of abnormal cognition differ between propofol and sevoflurane under general anesthesia.
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Affiliation(s)
- K Guessous
- From the AP-HP, Hôpital Lariboisière, Paris, France
- Sorbonne Université, Paris, France
- UMR-942, Inserm Délégation Régionale Paris 7, Bagnolet, France
| | - C Touchard
- From the AP-HP, Hôpital Lariboisière, Paris, France
- Université Paris Cité, Boulogne-Billancourt, France
| | - B Glezerson
- The Montréal Neurological Institute and Hospital, McGill University, Montréal, Canada
| | - C Levé
- From the AP-HP, Hôpital Lariboisière, Paris, France
- Université Paris Cité, Boulogne-Billancourt, France
| | - D Sabbagh
- Université Paris-Saclay, Inria, CEA, Palaiseau, France
| | - A Mebazaa
- From the AP-HP, Hôpital Lariboisière, Paris, France
- UMR-942, Inserm Délégation Régionale Paris 7, Bagnolet, France
- Université Paris Cité, Boulogne-Billancourt, France
| | - E Gayat
- Sorbonne Université, Paris, France
- UMR-942, Inserm Délégation Régionale Paris 7, Bagnolet, France
- Université Paris Cité, Boulogne-Billancourt, France
| | - C Paquet
- Cognitive Neurology Center, Memory department, Saint-Louis Lariboisière-Fernand Widal Hospital, APHP, Université Paris Cité INSERU1144, France
| | - F Vallée
- From the AP-HP, Hôpital Lariboisière, Paris, France
- UMR-942, Inserm Délégation Régionale Paris 7, Bagnolet, France
- Université Paris Cité, Boulogne-Billancourt, France
- Université Paris-Saclay, Inria, CEA, Palaiseau, France
| | - J Cartailler
- From the AP-HP, Hôpital Lariboisière, Paris, France
- UMR-942, Inserm Délégation Régionale Paris 7, Bagnolet, France
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6
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Sabbagh D, Cartailler J, Touchard C, Joachim J, Mebazaa A, Vallée F, Gayat É, Gramfort A, Engemann DA. Repurposing electroencephalogram monitoring of general anaesthesia for building biomarkers of brain ageing: an exploratory study. BJA OPEN 2023; 7:100145. [PMID: 37638087 PMCID: PMC10457469 DOI: 10.1016/j.bjao.2023.100145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 05/16/2023] [Indexed: 08/29/2023]
Abstract
Background Electroencephalography (EEG) is increasingly used for monitoring the depth of general anaesthesia, but EEG data from general anaesthesia monitoring are rarely reused for research. Here, we explored repurposing EEG monitoring from general anaesthesia for brain-age modelling using machine learning. We hypothesised that brain age estimated from EEG during general anaesthesia is associated with perioperative risk. Methods We reanalysed four-electrode EEGs of 323 patients under stable propofol or sevoflurane anaesthesia to study four EEG signatures (95% of EEG power <8-13 Hz) for age prediction: total power, alpha-band power (8-13 Hz), power spectrum, and spatial patterns in frequency bands. We constructed age-prediction models from EEGs of a healthy reference group (ASA 1 or 2) during propofol anaesthesia. Although all signatures were informative, state-of-the-art age-prediction performance was unlocked by parsing spatial patterns across electrodes along the entire power spectrum (mean absolute error=8.2 yr; R2=0.65). Results Clinical exploration in ASA 1 or 2 patients revealed that brain age was positively correlated with intraoperative burst suppression, a risk factor for general anaesthesia complications. Surprisingly, brain age was negatively correlated with burst suppression in patients with higher ASA scores, suggesting hidden confounders. Secondary analyses revealed that age-related EEG signatures were specific to propofol anaesthesia, reflected by limited model generalisation to anaesthesia maintained with sevoflurane. Conclusions Although EEG from general anaesthesia may enable state-of-the-art age prediction, differences between anaesthetic drugs can impact the effectiveness and validity of brain-age models. To unleash the dormant potential of EEG monitoring for clinical research, larger datasets from heterogeneous populations with precisely documented drug dosage will be essential.
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Affiliation(s)
- David Sabbagh
- INSERM, Université de Paris, Paris, France
- Inria, CEA, Université Paris-Saclay, Palaiseau, France
| | - Jérôme Cartailler
- INSERM, Université de Paris, Paris, France
- Department of Anesthesia and Critical Care Medicine, AP-HP, Hôpital Lariboisière, Paris, France
| | - Cyril Touchard
- Department of Anesthesia and Critical Care Medicine, AP-HP, Hôpital Lariboisière, Paris, France
| | - Jona Joachim
- Department of Anesthesia and Critical Care Medicine, AP-HP, Hôpital Lariboisière, Paris, France
| | - Alexandre Mebazaa
- INSERM, Université de Paris, Paris, France
- Department of Anesthesia and Critical Care Medicine, AP-HP, Hôpital Lariboisière, Paris, France
| | - Fabrice Vallée
- INSERM, Université de Paris, Paris, France
- Inria, CEA, Université Paris-Saclay, Palaiseau, France
- Department of Anesthesia and Critical Care Medicine, AP-HP, Hôpital Lariboisière, Paris, France
| | - Étienne Gayat
- INSERM, Université de Paris, Paris, France
- Department of Anesthesia and Critical Care Medicine, AP-HP, Hôpital Lariboisière, Paris, France
| | | | - Denis A. Engemann
- Inria, CEA, Université Paris-Saclay, Palaiseau, France
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland
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Fleischmann A, Georgii MT, Schuessler J, Schneider G, Pilge S, Kreuzer M. Always Assess the Raw Electroencephalogram: Why Automated Burst Suppression Detection May Not Detect All Episodes. Anesth Analg 2023; 136:346-354. [PMID: 35653440 DOI: 10.1213/ane.0000000000006098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
BACKGROUND Electroencephalogram (EEG)-based monitors of anesthesia are used to assess patients' level of sedation and hypnosis as well as to detect burst suppression during surgery. One of these monitors, the Entropy module, uses an algorithm to calculate the burst suppression ratio (BSR) that reflects the percentage of suppressed EEG. Automated burst suppression detection monitors may not reliably detect this EEG pattern. Hence, we evaluated the detection accuracy of BSR and investigated the EEG features leading to errors in the identification of burst suppression. METHODS With our study, we were able to compare the performance of the BSR to the visual burst suppression detection in the raw EEG and obtain insights on the architecture of the unrecognized burst suppression phases. RESULTS We showed that the BSR did not detect burst suppression in 13 of 90 (14%) patients. Furthermore, the time comparison between the visually identified burst suppression duration and elevated BSR values strongly depended on the BSR value being used as a cutoff. A possible factor for unrecognized burst suppression by the BSR may be a significantly higher suppression amplitude ( P = .002). Six of the 13 patients with undetected burst suppression by BSR showed intraoperative state entropy values >80, indicating a risk of awareness while being in burst suppression. CONCLUSIONS Our results complement previous results regarding the underestimation of burst suppression by other automated detection modules and highlight the importance of not relying solely on the processed index, but to assess the native EEG during anesthesia.
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Affiliation(s)
- Antonia Fleischmann
- From the Department of Anesthesiology and Intensive Care, School of Medicine, Klinikum rechts der Isar, Technical University Munich, Munich, Germany
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8
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Sun C, Longrois D, Holcman D. Spectral EEG correlations from the different phases of general anesthesia. Front Med (Lausanne) 2023; 10:1009434. [PMID: 36950512 PMCID: PMC10025404 DOI: 10.3389/fmed.2023.1009434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 02/15/2023] [Indexed: 03/08/2023] Open
Abstract
Introduction Electroencephalography (EEG) signals contain transient oscillation patterns commonly used to classify brain states in responses to action, sleep, coma or anesthesia. Methods Using a time-frequency analysis of the EEG, we search for possible causal correlations between the successive phases of general anesthesia. We hypothesize that it could be possible to anticipate recovery patterns from the induction or maintenance phases. For that goal, we track the maximum power of the α-band and follow its time course. Results and discussion We quantify the frequency shift of the α-band during the recovery phase and the associated duration. Using Pearson coefficient and Bayes factor, we report non-significant linear correlation between the α-band frequency and duration shifts during recovery and the presence of the δ or the α rhythms during the maintenance phase. We also found no correlations between the α-band emergence trajectory and the total duration of the flat EEG epochs (iso-electric suppressions) induced by a propofol bolus injected during induction. Finally, we quantify the instability of the α-band using the mathematical total variation that measures possible deviations from a flat line. To conclude, the present correlative analysis shows that EEG dynamics extracted from the initial and maintenance phases of general anesthesia cannot anticipate both the emergence trajectory and the extubation time.
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Affiliation(s)
- Christophe Sun
- Group of Data Modeling, Computational Biology and Predictive Medicine, Institut de Biologie (IBENS), École Normale Supérieure, Université PSL, Paris, France
| | - Dan Longrois
- Département d'Anesthésie-Réanimation, Hôpital Bichat-Claude Bernard, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - David Holcman
- Group of Data Modeling, Computational Biology and Predictive Medicine, Institut de Biologie (IBENS), École Normale Supérieure, Université PSL, Paris, France
- Churchill College, Cambridge, United Kingdom
- *Correspondence: David Holcman
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9
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Chao JY, Gutiérrez R, Legatt AD, Yozawitz EG, Lo Y, Adams DC, Delphin ES, Shinnar S, Purdon PL. Decreased Electroencephalographic Alpha Power During Anesthesia Induction Is Associated With EEG Discontinuity in Human Infants. Anesth Analg 2022; 135:1207-1216. [PMID: 35041633 PMCID: PMC9276847 DOI: 10.1213/ane.0000000000005864] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
BACKGROUND Electroencephalogram (EEG) discontinuity can occur at high concentrations of anesthetic drugs, reflecting suppression of electrocortical activity. This EEG pattern has been reported in children and reflects a deep state of anesthesia. Isoelectric events on the EEG, a more extreme degree of voltage suppression, have been shown to be associated with worse long-term neurologic outcomes in neonates undergoing cardiac surgery. However, the clinical significance of EEG discontinuities during pediatric anesthesia for noncardiac surgery is not yet known and merits further research. In this study, we assessed the incidence of EEG discontinuity during anesthesia induction in neurologically normal infants and the clinical factors associated with its development. We hypothesized that EEG discontinuity would be associated with sevoflurane-induced alpha (8-12 Hz) power during the period of anesthesia induction in infants. METHODS We prospectively recorded 26 channels of EEG during anesthesia induction in an observational cohort of 54 infants (median age, 7.6 months; interquartile range [IQR] [4.9-9.8 months]). We identified EEG discontinuity, defined as voltage amplitude <25 microvolts for >2 seconds, and assessed its association with sevoflurane-induced alpha power using spectral analysis and multivariable logistic regression adjusting for clinically important variables. RESULTS EEG discontinuity was observed in 20 of 54 subjects (37%), with a total of 25 discrete events. Sevoflurane-induced alpha power in the posterior regions of the head (eg, parietal or occipital regions) was significantly lower in the EEG discontinuity group (midline parietal channel on the electroencephalogram, International 10-20 System [Pz]; 8.3 vs 11.2 decibels [dBs]; P = .004), and this association remained after multivariable adjustment (adjusted odds ratio [aOR] = 0.51 per dB increase in alpha power [95% CI, 0.30-0.89]; P = .02). There were no differences in the baseline (unanesthetized) EEG between groups in alpha power or power in any other frequency band. CONCLUSIONS We demonstrate that EEG discontinuity is common during anesthesia induction and is related to the level of sevoflurane-induced posterior alpha power, a putative marker of cortical-thalamic circuit development in the first year of life. This association persisted even after adjusting for age and propofol coadministration. The fact that this difference was only observed during anesthesia and not in the baseline EEG suggests that otherwise hidden brain circuit properties are unmasked by general anesthesia. These neurophysiologic markers observed during anesthesia may be useful in identifying patients who may have a greater chance of developing discontinuity.
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Affiliation(s)
- Jerry Y. Chao
- Department of Anesthesiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Rodrigo Gutiérrez
- Department of Anesthesiology and Perioperative Medicine, Center of Advanced Clinical Research, University of Chile, Santiago, Chile
| | - Alan D. Legatt
- The Saul R. Korey Department of Neurology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Medicine (Critical Care), Montefiore Medical Center, Albert Einstein College, Bronx, NY, USA
| | - Elissa G. Yozawitz
- The Saul R. Korey Department of Neurology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Pediatrics, Children’s Hospital at Montefiore, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Yungtai Lo
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - David C. Adams
- Department of Anesthesiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Anesthesia, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Ellise S. Delphin
- Department of Anesthesiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Shlomo Shinnar
- The Saul R. Korey Department of Neurology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Pediatrics, Children’s Hospital at Montefiore, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Patrick L. Purdon
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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10
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Sun C, Holcman D. Combining transient statistical markers from the EEG signal to predict brain sensitivity to general anesthesia. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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11
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Manquat E, Ravaux H, Kindermans M, Joachim J, Serrano J, Touchard C, Mateo J, Mebazaa A, Gayat E, Vallée F, Cartailler J. Impact of impaired cerebral blood flow autoregulation on electroencephalogram signals in adults undergoing propofol anaesthesia: a pilot study. BJA OPEN 2022; 1:100004. [PMID: 37588691 PMCID: PMC10430849 DOI: 10.1016/j.bjao.2022.100004] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 01/26/2022] [Indexed: 08/18/2023]
Abstract
Background Cerebral autoregulation actively maintains cerebral blood flow over a range of MAPs. During general anaesthesia, this mechanism may not compensate for reductions in MAP leading to brain hypoperfusion. Cerebral autoregulation can be assessed using the mean flow index derived from Doppler measurements of average blood velocity in the middle cerebral artery, but this is impractical for routine monitoring within the operating room. Here, we investigate the possibility of using the EEG as a proxy measure for a loss of cerebral autoregulation, determined by the mean flow index. Methods Thirty-six patients (57.5 [44.25; 66.5] yr; 38.9% women, non-emergency neuroradiology surgery) anaesthetised using propofol were prospectively studied. Continuous recordings of MAP, average blood velocity in the middle cerebral artery, EEG, and regional cerebral oxygen saturation were made. Poor cerebral autoregulation was defined as a mean flow index greater than 0.3. Results Eighteen patients had preserved cerebral autoregulation, and 18 had altered cerebral autoregulation. The two groups had similar ages, MAPs, and average blood velocities in the middle cerebral artery. Patients with altered cerebral autoregulation exhibited a significantly slower alpha peak frequency (9.4 [9.0, 9.9] Hz vs 10.5 [10.1, 10.9] Hz, P<0.001), which persisted after adjusting for age, norepinephrine infusion rate, and ASA class (odds ratio=0.038 [confidence interval, 0.004, 0.409]; P=0.007). Conclusion In this pilot study, we found that loss of cerebral autoregulation was associated with a slower alpha peak frequency, independent of age. This work suggests that impaired cerebral autoregulation could be monitored in the operating room using the existing EEG setup. Clinical trial registration NCT03769142.
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Affiliation(s)
- Elsa Manquat
- Department of Anesthesiology, Burn and Critical Care, St-Louis-Lariboisiere University Hospital, Assistance Publique Hôpitaux de Paris, Paris, France
- AP-HP-Inria, Laboratoire Daniel Bernoulli, Paris, France
| | - Hugues Ravaux
- Department of Anesthesiology, Burn and Critical Care, St-Louis-Lariboisiere University Hospital, Assistance Publique Hôpitaux de Paris, Paris, France
| | - Manuel Kindermans
- Department of Anesthesiology, Burn and Critical Care, St-Louis-Lariboisiere University Hospital, Assistance Publique Hôpitaux de Paris, Paris, France
| | - Jona Joachim
- Department of Anesthesiology, Burn and Critical Care, St-Louis-Lariboisiere University Hospital, Assistance Publique Hôpitaux de Paris, Paris, France
| | - José Serrano
- Department of Anesthesiology, Burn and Critical Care, St-Louis-Lariboisiere University Hospital, Assistance Publique Hôpitaux de Paris, Paris, France
| | - Cyril Touchard
- Department of Anesthesiology, Burn and Critical Care, St-Louis-Lariboisiere University Hospital, Assistance Publique Hôpitaux de Paris, Paris, France
| | - Joaquim Mateo
- Department of Anesthesiology, Burn and Critical Care, St-Louis-Lariboisiere University Hospital, Assistance Publique Hôpitaux de Paris, Paris, France
| | - Alexandre Mebazaa
- Department of Anesthesiology, Burn and Critical Care, St-Louis-Lariboisiere University Hospital, Assistance Publique Hôpitaux de Paris, Paris, France
- INSERM, UMR-942, Paris, France
| | - Etienne Gayat
- Department of Anesthesiology, Burn and Critical Care, St-Louis-Lariboisiere University Hospital, Assistance Publique Hôpitaux de Paris, Paris, France
- INSERM, UMR-942, Paris, France
| | - Fabrice Vallée
- Department of Anesthesiology, Burn and Critical Care, St-Louis-Lariboisiere University Hospital, Assistance Publique Hôpitaux de Paris, Paris, France
- Laboratoire de Mécanique des Solides (LMS), Ecole Polytechnique/CNRS/Institut Polytechnique de Paris, France
- INSERM, UMR-942, Paris, France
| | - Jérôme Cartailler
- Department of Anesthesiology, Burn and Critical Care, St-Louis-Lariboisiere University Hospital, Assistance Publique Hôpitaux de Paris, Paris, France
- INSERM, UMR-942, Paris, France
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Thalamic T-Type Calcium Channels as Targets for Hypnotics and General Anesthetics. Int J Mol Sci 2022; 23:ijms23042349. [PMID: 35216466 PMCID: PMC8876360 DOI: 10.3390/ijms23042349] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 02/17/2022] [Accepted: 02/18/2022] [Indexed: 12/19/2022] Open
Abstract
General anesthetics mainly act by modulating synaptic inhibition on the one hand (the potentiation of GABA transmission) or synaptic excitation on the other (the inhibition of NMDA receptors), but they can also have effects on numerous other proteins, receptors, and channels. The effects of general anesthetics on ion channels have been the subject of research since the publication of reports of direct actions of these drugs on ion channel proteins. In particular, there is considerable interest in T-type voltage-gated calcium channels that are abundantly expressed in the thalamus, where they control patterns of cellular excitability and thalamocortical oscillations during awake and sleep states. Here, we summarized and discussed our recent studies focused on the CaV3.1 isoform of T-channels in the nonspecific thalamus (intralaminar and midline nuclei), which acts as a key hub through which natural sleep and general anesthesia are initiated. We used mouse genetics and in vivo and ex vivo electrophysiology to study the role of thalamic T-channels in hypnosis induced by a standard general anesthetic, isoflurane, as well as novel neuroactive steroids. From the results of this study, we conclude that CaV3.1 channels contribute to thalamocortical oscillations during anesthetic-induced hypnosis, particularly the slow-frequency range of δ oscillations (0.5–4 Hz), by generating “window current” that contributes to the resting membrane potential. We posit that the role of the thalamic CaV3.1 isoform of T-channels in the effects of various classes of general anesthetics warrants consideration.
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Bellini V, Valente M, Bertorelli G, Pifferi B, Craca M, Mordonini M, Lombardo G, Bottani E, Del Rio P, Bignami E. Machine learning in perioperative medicine: a systematic review. JOURNAL OF ANESTHESIA, ANALGESIA AND CRITICAL CARE 2022; 2:2. [PMCID: PMC8761048 DOI: 10.1186/s44158-022-00033-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
Background Risk stratification plays a central role in anesthetic evaluation. The use of Big Data and machine learning (ML) offers considerable advantages for collection and evaluation of large amounts of complex health-care data. We conducted a systematic review to understand the role of ML in the development of predictive post-surgical outcome models and risk stratification. Methods Following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines, we selected the period of the research for studies from 1 January 2015 up to 30 March 2021. A systematic search in Scopus, CINAHL, the Cochrane Library, PubMed, and MeSH databases was performed; the strings of research included different combinations of keywords: “risk prediction,” “surgery,” “machine learning,” “intensive care unit (ICU),” and “anesthesia” “perioperative.” We identified 36 eligible studies. This study evaluates the quality of reporting of prediction models using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) checklist. Results The most considered outcomes were mortality risk, systemic complications (pulmonary, cardiovascular, acute kidney injury (AKI), etc.), ICU admission, anesthesiologic risk and prolonged length of hospital stay. Not all the study completely followed the TRIPOD checklist, but the quality was overall acceptable with 75% of studies (Rev #2, comm #minor issue) showing an adherence rate to TRIPOD more than 60%. The most frequently used algorithms were gradient boosting (n = 13), random forest (n = 10), logistic regression (LR; n = 7), artificial neural networks (ANNs; n = 6), and support vector machines (SVM; n = 6). Models with best performance were random forest and gradient boosting, with AUC > 0.90. Conclusions The application of ML in medicine appears to have a great potential. From our analysis, depending on the input features considered and on the specific prediction task, ML algorithms seem effective in outcomes prediction more accurately than validated prognostic scores and traditional statistics. Thus, our review encourages the healthcare domain and artificial intelligence (AI) developers to adopt an interdisciplinary and systemic approach to evaluate the overall impact of AI on perioperative risk assessment and on further health care settings as well.
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Affiliation(s)
- Valentina Bellini
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126 Parma, Italy
| | - Marina Valente
- General Surgery Unit, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126 Parma, Italy
| | - Giorgia Bertorelli
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126 Parma, Italy
| | - Barbara Pifferi
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126 Parma, Italy
| | - Michelangelo Craca
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126 Parma, Italy
| | - Monica Mordonini
- Department of Engineering and Architecture, University of Parma, Viale G.P.Usberti 181/A, 43124 Parma, Italy
| | - Gianfranco Lombardo
- Department of Engineering and Architecture, University of Parma, Viale G.P.Usberti 181/A, 43124 Parma, Italy
| | - Eleonora Bottani
- Department of Engineering and Architecture, University of Parma, Viale G.P.Usberti 181/A, 43124 Parma, Italy
| | - Paolo Del Rio
- General Surgery Unit, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126 Parma, Italy
| | - Elena Bignami
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126 Parma, Italy
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Emergence and fragmentation of the alpha-band driven by neuronal network dynamics. PLoS Comput Biol 2021; 17:e1009639. [PMID: 34871305 PMCID: PMC8675921 DOI: 10.1371/journal.pcbi.1009639] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 12/16/2021] [Accepted: 11/14/2021] [Indexed: 11/22/2022] Open
Abstract
Rhythmic neuronal network activity underlies brain oscillations. To investigate how connected neuronal networks contribute to the emergence of the α-band and to the regulation of Up and Down states, we study a model based on synaptic short-term depression-facilitation with afterhyperpolarization (AHP). We found that the α-band is generated by the network behavior near the attractor of the Up-state. Coupling inhibitory and excitatory networks by reciprocal connections leads to the emergence of a stable α-band during the Up states, as reflected in the spectrogram. To better characterize the emergence and stability of thalamocortical oscillations containing α and δ rhythms during anesthesia, we model the interaction of two excitatory networks with one inhibitory network, showing that this minimal topology underlies the generation of a persistent α-band in the neuronal voltage characterized by dominant Up over Down states. Finally, we show that the emergence of the α-band appears when external inputs are suppressed, while fragmentation occurs at small synaptic noise or with increasing inhibitory inputs. To conclude, α-oscillations could result from the synaptic dynamics of interacting excitatory neuronal networks with and without AHP, a principle that could apply to other rhythms. Brain oscillations, recorded from electroencephalograms characterize behaviors such as sleep, wakefulness, brain evoked responses, coma or anesthesia. The underlying rhythms for these oscillations are associated at a neuronal population level to fluctuations of the membrane potential between Up (depolarized) and Down (hyperpolarized) states. During anesthesia with propofol, a dominant α-band (8–12Hz) can emerge or disappear, but the underlying mechanism remains unclear. Using modeling, we report that the α-band appears during Up states in neuronal populations driven by short-term synaptic plasticity and synaptic noise. Moreover, we show that three connected neuronal networks representing the thalamocortical loop reproduce the dynamics of the α-band, which emerges following the arrest of excitatory stimulations, but that can disappear by increasing inhibitory inputs. To conclude, short-term plasticity in well connected neuronal networks can explain the emergence and fragmentation of the α-band.
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Cartailler J, Touchard C, Parutto P, Gayat E, Paquet C, Vallée F. Brain fragility among middle-aged and elderly patients from electroencephalogram during induction of anaesthesia. Eur J Anaesthesiol 2021; 38:1304-1306. [PMID: 34735402 PMCID: PMC8635248 DOI: 10.1097/eja.0000000000001524] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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Obert DP, Hight D, Sleigh J, Kaiser HA, García PS, Schneider G, Kreuzer M. The First Derivative of the Electroencephalogram Facilitates Tracking of Electroencephalographic Alpha Band Activity During General Anesthesia. Anesth Analg 2021; 134:1062-1071. [PMID: 34677164 DOI: 10.1213/ane.0000000000005783] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
BACKGROUND Intraoperative neuromonitoring can help to navigate anesthesia. Pronounced alpha oscillations in the frontal electroencephalogram (EEG) appear to predict favorable perioperative neurocognitive outcomes and may also provide a measure of intraoperative antinociception. Monitoring the presence and strength of these alpha oscillations can be challenging, especially in elderly patients, because the EEG in these patients may be dominated by oscillations in other frequencies. Hence, the information regarding alpha oscillatory activity may be hidden and hard to visualize on a screen. Therefore, we developed an effective approach to improve the detection and presentation of alpha activity in the perioperative setting. METHODS We analyzed EEG records of 180 patients with a median age of 60 years (range, 18-90 years) undergoing noncardiac, nonneurologic surgery under general anesthesia with propofol induction and sevoflurane maintenance. We calculated the power spectral density (PSD) for the unprocessed EEG as well as for the time-discrete first derivative of the EEG (diffPSD) from 10-second epochs. Based on these data, we estimated the power-law coefficient κ of the PSD and diffPSD, as the EEG coarsely follows a 1/fκ distribution when displayed in double logarithmic coordinates. In addition, we calculated the alpha (7.8-12.1 Hz) to delta (0.4-4.3 Hz) ratio from the PSD as well as diffPSD. RESULTS The median κ was 0.899 [first and third quartile: 0.786, 0.986] for the unaltered PSD, and κ = -0.092 [-0.202, -0.013] for the diffPSD, corresponding to an almost horizontal PSD of the differentiated EEG. The alpha-to-delta ratio of the diffPSD was strongly increased (median ratio = -8.0 dB [-10.5, -4.7 dB] for the unaltered PSD versus 30.1 dB [26.1, 33.8 dB] for the diffPSD). A strong narrowband oscillatory alpha power component (>20% of total alpha power) was detected in 23% using PSD, but in 96% of the diffPSD. CONCLUSIONS We demonstrated that the calculation of the diffPSD from the time-discrete derivative of the intraoperative frontal EEG is a straightforward approach to improve the detection of alpha activity by eliminating the broadband background noise. This improvement in alpha peak detection and visualization could facilitate the guidance of general anesthesia and improve patient outcome.
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Affiliation(s)
- David P Obert
- From the Department of Anesthesiology and Intensive Care Medicine, Technical University of Munich, School of Medicine, Munich, Germany
| | - Darren Hight
- Department of Anaesthesiology and Pain Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Jamie Sleigh
- Department of Anaesthesia, Waikato Clinical School, University of Auckland, Hamilton, New Zealand
| | - Heiko A Kaiser
- Department of Anaesthesiology and Pain Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Paul S García
- Department of Anesthesiology, Columbia University, New York, New York
| | - Gerhard Schneider
- From the Department of Anesthesiology and Intensive Care Medicine, Technical University of Munich, School of Medicine, Munich, Germany
| | - Matthias Kreuzer
- From the Department of Anesthesiology and Intensive Care Medicine, Technical University of Munich, School of Medicine, Munich, Germany
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Touchard C, Cartailler J, Levé C, Serrano J, Sabbagh D, Manquat E, Joachim J, Mateo J, Gayat E, Engemann D, Vallée F. Propofol Requirement and EEG Alpha Band Power During General Anesthesia Provide Complementary Views on Preoperative Cognitive Decline. Front Aging Neurosci 2020; 12:593320. [PMID: 33328973 PMCID: PMC7729157 DOI: 10.3389/fnagi.2020.593320] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 11/05/2020] [Indexed: 11/15/2022] Open
Abstract
Background: Although cognitive decline (CD) is associated with increased post-operative morbidity and mortality, routinely screening patients remains difficult. The main objective of this prospective study is to use the EEG response to a Propofol-based general anesthesia (GA) to reveal CD. Methods: 42 patients with collected EEG and Propofol target concentration infusion (TCI) during GA had a preoperative cognitive assessment using MoCA. We evaluated the performance of three variables to detect CD (MoCA < 25 points): age, Propofol requirement to induce unconsciousness (TCI at SEF95: 8–13 Hz) and the frontal alpha band power (AP at SEF95: 8–13 Hz). Results: The 17 patients (40%) with CD were significantly older (p < 0.001), had lower TCI (p < 0.001), and AP (p < 0.001). We found using logistic models that TCI and AP were the best set of variables associated with CD (AUC: 0.89) and performed better than age (p < 0.05). Propofol TCI had a greater impact on CD probability compared to AP, although both were complementary in detecting CD. Conclusion: TCI and AP contribute additively to reveal patient with preoperative cognitive decline. Further research on post-operative cognitive trajectory are necessary to confirm the interest of intra operative variables in addition or as a substitute to cognitive evaluation.
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Affiliation(s)
- Cyril Touchard
- Department of Anesthesiology and Intensive Care, Lariboisière - Saint Louis Hospitals, Paris, France
| | - Jérôme Cartailler
- Department of Anesthesiology and Intensive Care, Lariboisière - Saint Louis Hospitals, Paris, France.,Inserm, UMRS-942, Paris Diderot University, Paris, France
| | - Charlotte Levé
- Department of Anesthesiology and Intensive Care, Lariboisière - Saint Louis Hospitals, Paris, France
| | - José Serrano
- Department of Anesthesiology and Intensive Care, Lariboisière - Saint Louis Hospitals, Paris, France
| | - David Sabbagh
- Université Paris-Saclay, Inria, CEA Palaiseau, France
| | - Elsa Manquat
- Department of Anesthesiology and Intensive Care, Lariboisière - Saint Louis Hospitals, Paris, France
| | - Jona Joachim
- Department of Anesthesiology and Intensive Care, Lariboisière - Saint Louis Hospitals, Paris, France
| | - Joaquim Mateo
- Department of Anesthesiology and Intensive Care, Lariboisière - Saint Louis Hospitals, Paris, France
| | - Etienne Gayat
- Department of Anesthesiology and Intensive Care, Lariboisière - Saint Louis Hospitals, Paris, France.,Inserm, UMRS-942, Paris Diderot University, Paris, France
| | - Denis Engemann
- Université Paris-Saclay, Inria, CEA Palaiseau, France.,Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Fabrice Vallée
- Department of Anesthesiology and Intensive Care, Lariboisière - Saint Louis Hospitals, Paris, France.,Inserm, UMRS-942, Paris Diderot University, Paris, France.,Université Paris-Saclay, Inria, CEA Palaiseau, France
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The influence of induction speed on the frontal (processed) EEG. Sci Rep 2020; 10:19444. [PMID: 33173114 PMCID: PMC7655958 DOI: 10.1038/s41598-020-76323-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 10/27/2020] [Indexed: 12/12/2022] Open
Abstract
The intravenous injection of the anaesthetic propofol is clinical routine to induce loss of responsiveness (LOR). However, there are only a few studies investigating the influence of the injection rate on the frontal electroencephalogram (EEG) during LOR. Therefore, we focused on changes of the frontal EEG especially during this period. We included 18 patients which were randomly assigned to a slow or fast induction group and recorded the frontal EEG. Based on this data, we calculated the power spectral density, the band powers and band ratios. To analyse the behaviour of processed EEG parameters we calculated the beta ratio, the spectral entropy, and the spectral edge frequency. Due to the prolonged induction period in the slow injection group we were able to distinguish loss of responsiveness to verbal command (LOvR) from loss of responsiveness to painful stimulus (LOpR) whereas in the fast induction group we could not. At LOpR, we observed a higher relative alpha and beta power in the slow induction group while the relative power in the delta range was lower than in the fast induction group. When concentrating on the slow induction group the increase in relative alpha power pre-LOpR and even before LOvR indicated that frontal EEG patterns, which have been suggested as an indicator of unconsciousness, can develop before LOR. Further, LOvR was best reflected by an increase of the alpha to delta ratio, and LOpR was indicated by a decrease of the beta to alpha ratio. These findings highlight the different spectral properties of the EEG at various levels of responsiveness and underline the influence of the propofol injection rate on the frontal EEG during induction of general anesthesia.
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Electroencephalographic Alpha and Delta Oscillation Dynamics in Response to Increasing Doses of Propofol. J Neurosurg Anesthesiol 2020; 34:79-83. [PMID: 33060553 DOI: 10.1097/ana.0000000000000733] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Accepted: 09/05/2020] [Indexed: 11/26/2022]
Abstract
BACKGROUND The electroencephalogram (EEG) may be useful for monitoring anesthetic depth and avoiding overdose. We aimed to characterize EEG-recorded brain oscillations during increasing depth of anesthesia in a real-life surgical scenario. We hypothesized that alpha power and coherency will diminish as propofol dose increases between loss of consciousness (LOC) and an EEG burst suppression (BS) pattern. METHODS This nonrandomized dose-response clinical trial with concurrent control included EEG monitoring in 16 patients receiving slowly increasing doses of propofol. We assessed 3 intraoperative EEG segments (LOC, middle-dose, and BS) with spectral analysis. RESULTS Alpha band power diminished with each step increase in propofol dose. Average alpha power and average delta power during the BS step (-1.4±3.8 and 6.2±3.1 dB, respectively) were significantly lower than during the LOC step (2.8±2.6; P=0.004 and 10.1±5.2 dB; P=0.03, respectively). Peak alpha power was significantly higher during the LOC (5.4±2.6 dB) compared with middle-dose (2.6±3.6; P=0.04) and BS (0.7±3.2; P=0.0002) steps. In addition, as propofol dose increased, alpha band coherence between the F7 and F8 electrodes decreased, whereas delta band coherence exhibited a biphasic response (initial increase between LOC and middle-dose steps and decrease between middle-dose and BS steps). CONCLUSION We report compelling data regarding EEG patterns associated with increases in propofol dose. This information may more accurately define "therapeutic windows" for anesthesia and provide insights into brain dynamics that are sequentially affected by increased anesthetic doses.
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State entropy and burst suppression ratio can show contradictory information. Eur J Anaesthesiol 2020; 37:1084-1092. [DOI: 10.1097/eja.0000000000001312] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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