麻醉深度监测临床应用研究进展
Research Progress on the Clinical Application of Depth of Anesthesia Monitoring
摘要: 全身麻醉是由麻醉药物引起的可逆性的无意识状态，在遗忘、镇痛、抑制体动反应的基础上，同时维持自主神经、循环、呼吸与体温调节的稳定。麻醉深度(Depth of Anaesthesia, DOA)过浅或过深可增加术中知晓和术后并发症，因此，对麻醉深度的准确评估有助于对患者进行麻醉过程中的个体化药物管理。目前麻醉深度监测涵盖了中枢神经系统、自主神经系统及运动神经反射等内容，反应意识状态及机体对伤害性刺激的反应，通过专用算法将麻醉深度生理状态的定性评估转换为定量指标。文章描述了麻醉深度监测临床应用研究进展，并讨论了它们未来的发展潜力和可能的应用。
Abstract: General anesthesia is a reversible state of unconsciousness induced by anesthetic drugs based on amnesia, analgesia, and suppression of motor responses while maintaining stability in autonomic nervous function, circulation, respiration, and thermoregulation. Inadequate or excessive depth of anesthesia (DOA) can increase the risk of intraoperative awareness and postoperative complications. Therefore, accurate assessment of DOA is crucial for personalized drug management during the anesthesia process. Currently, DOA monitoring encompasses the central nervous system, autonomic nervous system, and motor reflex responses, reflecting the state of consciousness and the body’s reaction to nociceptive stimuli. It converts qualitative assessments of physiological states during anesthesia into quantitative indicators through specialized algorithms. This article outlines the progress in clinical applications of DOA monitoring and discusses their future potential and possible applications.
文章引用：代蕾蕾, 齐峰. 麻醉深度监测临床应用研究进展[J]. 临床医学进展, 2025, 15(3): 1091-1097. https://doi.org/10.12677/acm.2025.153716

参考文献

[1] Vutskits, L. and Xie, Z. (2016) Lasting Impact of General Anaesthesia on the Brain: Mechanisms and Relevance. Nature Reviews Neuroscience, 17, 705-717. [Google Scholar] [CrossRef] [PubMed]
[2] Short, T.G., Campbell, D., Frampton, C., Chan, M.T.V., Myles, P.S., Corcoran, T.B., et al. (2019) Anaesthetic Depth and Complications after Major Surgery: An International, Randomised Controlled Trial. The Lancet, 394, 1907-1914. [Google Scholar] [CrossRef] [PubMed]
[3] Musizza, B. and Ribaric, S. (2010) Monitoring the Depth of Anaesthesia. Sensors, 10, 10896-10935. [Google Scholar] [CrossRef] [PubMed]
[4] Fahy, B.G. and Chau, D.F. (2018) The Technology of Processed Electroencephalogram Monitoring Devices for Assessment of Depth of Anesthesia. Anesthesia & Analgesia, 126, 111-117. [Google Scholar] [CrossRef] [PubMed]
[5] Laferrière-Langlois, P., Morisson, L., Jeffries, S., Duclos, C., Espitalier, F. and Richebé, P. (2024) Depth of Anesthesia and Nociception Monitoring: Current State and Vision for 2050. Anesthesia & Analgesia, 138, 295-307. [Google Scholar] [CrossRef] [PubMed]
[6] Seeck, M., Koessler, L., Bast, T., Leijten, F., Michel, C., Baumgartner, C., et al. (2017) The Standardized EEG Electrode Array of the IFCN. Clinical Neurophysiology, 128, 2070-2077. [Google Scholar] [CrossRef] [PubMed]
[7] Jameson, L.C. and Sloan, T.B. (2006) Using EEG to Monitor Anesthesia Drug Effects during Surgery. Journal of Clinical Monitoring and Computing, 20, 445-472. [Google Scholar] [CrossRef] [PubMed]
[8] Purdon, P.L., Sampson, A., Pavone, K.J. and Brown, E.N. (2015) Clinical Electroencephalography for Anesthesiologists: Part I: Background and Basic Signatures. Anesthesiology, 123, 937-960. [Google Scholar] [CrossRef] [PubMed]
[9] Zakaria, L., Desowska, A., Berde, C.B. and Cornelissen, L. (2023) Electroencephalographic Delta and Alpha Oscillations Reveal Phase-Amplitude Coupling in Paediatric Patients Undergoing Sevoflurane-Based General Anaesthesia. British Journal of Anaesthesia, 130, 595-602. [Google Scholar] [CrossRef] [PubMed]
[10] Sun, Y., Wei, C., Cui, V., Xiu, M. and Wu, A. (2020) Electroencephalography: Clinical Applications during the Perioperative Period. Frontiers in Medicine, 7, Article 251. [Google Scholar] [CrossRef] [PubMed]
[11] Lewis, L.D., Ching, S., Weiner, V.S., Peterfreund, R.A., Eskandar, E.N., Cash, S.S., et al. (2013) Local Cortical Dynamics of Burst Suppression in the Anaesthetized Brain. Brain, 136, 2727-2737. [Google Scholar] [CrossRef] [PubMed]
[12] Sirmpilatze, N., Mylius, J., Ortiz-Rios, M., Baudewig, J., Paasonen, J., Golkowski, D., et al. (2022) Spatial Signatures of Anesthesia-Induced Burst-Suppression Differ between Primates and Rodents. eLife, 11, e74813. [Google Scholar] [CrossRef] [PubMed]
[13] Hagihira, S. (2015) Changes in the Electroencephalogram during Anaesthesia and Their Physiological Basis. British Journal of Anaesthesia, 115, i27-i31. [Google Scholar] [CrossRef] [PubMed]
[14] Bong, C.L. and Yuan, I. (2024) The Utility of Electroencephalograhy in Guiding General Anesthesia in Children. Anesthesia & Analgesia. [Google Scholar] [CrossRef] [PubMed]
[15] Bennett, C., Voss, L.J., Barnard, J.P.M. and Sleigh, J.W. (2009) Practical Use of the Raw Electroencephalogram Waveform during General Anesthesia: The Art and Science. Anesthesia & Analgesia, 109, 539-550. [Google Scholar] [CrossRef] [PubMed]
[16] Gimson, A. and Smith, M. (2021) Processed EEG from Depth of Anaesthesia Monitors and Seizures: A Scoping Review. Seizure, 91, 198-206. [Google Scholar] [CrossRef] [PubMed]
[17] Hight, D., Kreuzer, M., Ugen, G., Schuller, P., Stüber, F., Sleigh, J., et al. (2023) Five Commercial ‘Depth of Anaesthesia’ Monitors Provide Discordant Clinical Recommendations in Response to Identical Emergence-Like EEG Signals. British Journal of Anaesthesia, 130, 536-545. [Google Scholar] [CrossRef] [PubMed]
[18] Punjasawadwong, Y., Phongchiewboon, A. and Bunchungmongkol, N. (2014) Bispectral Index for Improving Anaesthetic Delivery and Postoperative Recovery. Cochrane Database of Systematic Reviews, No. 6, CD003843. [Google Scholar] [CrossRef] [PubMed]
[19] Gelb, A.W., Morriss, W.W., Johnson, W. and Merry, A.F. (2018) World Health Organization-World Federation of Societies of Anaesthesiologists (WHO-WFSA) International Standards for a Safe Practice of Anesthesia. Anesthesia & Analgesia, 126, 2047-2055. [Google Scholar] [CrossRef] [PubMed]
[20] Lewis, S.R., Pritchard, M.W., Fawcett, L.J. and Punjasawadwong, Y. (2019) Bispectral Index for Improving Intraoperative Awareness and Early Postoperative Recovery in Adults. Cochrane Database of Systematic Reviews, No. 9, CD003843. [Google Scholar] [CrossRef] [PubMed]
[21] Liu, Y., Qiu, D., Jia, L., Tan, J., Kang, J., Xie, T., et al. (2019) Depth of Anesthesia Measured by Bispectral Index and Postoperative Mortality: A Meta-Analysis of Observational Studies. Journal of Clinical Anesthesia, 56, 119-125. [Google Scholar] [CrossRef] [PubMed]
[22] Schuller, P.J., Newell, S., Strickland, P.A. and Barry, J.J. (2015) Response of Bispectral Index to Neuromuscular Block in Awake Volunteers. British Journal of Anaesthesia, 115, i95-i103. [Google Scholar] [CrossRef] [PubMed]
[23] Myles, P., Leslie, K., McNeil, J., Forbes, A. and Chan, M. (2004) Bispectral Index Monitoring to Prevent Awareness during Anaesthesia: The B-Aware Randomised Controlled Trial. The Lancet, 363, 1757-1763. [Google Scholar] [CrossRef] [PubMed]
[24] Kaki, A.M. and Almarakbi, W.A. (2009) Does Patient Position Influence the Reading of the Bispectral Index Monitor? Anesthesia & Analgesia, 109, 1843-1846. [Google Scholar] [CrossRef] [PubMed]
[25] Dahaba, A.A. (2005) Different Conditions that Could Result in the Bispectral Index Indicating an Incorrect Hypnotic State. Anesthesia & Analgesia, 101, 765-773. [Google Scholar] [CrossRef] [PubMed]
[26] Raue, J.F., Tünsmeyer, J. and Kästner, S.B.R. (2020) Effects of Isoflurane, Remifentanil and Dexmedetomidine on Selected EEG Parameters Derived from a Narcotrend Monitor before and after Nociceptive Stimulation at Different MAC Multiples in Cats. BMC Veterinary Research, 16, Article No. 332. [Google Scholar] [CrossRef] [PubMed]
[27] Schultz, B., Grouven, U. and Schultz, A. (2002) Automatic Classification Algorithms of the EEG Monitor Narcotrend for Routinely Recorded EEG Data from General Anaesthesia: A Validation Study. Biomedizinische Technik/Biomedical Engineering, 47, 9-13. [Google Scholar] [CrossRef] [PubMed]
[28] Schneider, G., Kochs, E.F., Horn, B., Kreuzer, M. and Ningler, M. (2004) Narcotrend® Does Not Adequately Detect the Transition between Awareness and Unconsciousness in Surgical Patients. Anesthesiology, 101, 1105-1111. [Google Scholar] [CrossRef] [PubMed]
[29] Plourde, G. (2006) Auditory Evoked Potentials. Best Practice & Research Clinical Anaesthesiology, 20, 129-139. [Google Scholar] [CrossRef] [PubMed]
[30] Supp, G.G., Higgen, F.L., Hipp, J.F., Engel, A.K. and Siegel, M. (2018) Mid-Latency Auditory Evoked Potentials Differentially Predict Sedation and Drug Level under Opioid and Hypnotic Agents. Frontiers in Pharmacology, 9, Article 1427. [Google Scholar] [CrossRef] [PubMed]
[31] Mou, L., Li, M., Xu, D., Qin, W., Liu, X., Zhang, M., et al. (2015) Neurophysiological and Neuropsychological Correlates of Subconscious Auditory Processing during Anesthesia and Their Implications in Anesthesia Awareness. Cell Biochemistry and Biophysics, 73, 147-153. [Google Scholar] [CrossRef] [PubMed]
[32] Linassi, F., Vide, S., Ferreira, A., Schneider, G., Gambús, P. and Kreuzer, M. (2024) Relationships between the Qnox, Qcon, Burst Suppression Ratio, and Muscle Activity Index of the CONOX Monitor during Total Intravenous Anesthesia: A Pilot Study. Journal of Clinical Monitoring and Computing, 38, 1281-1290. [Google Scholar] [CrossRef] [PubMed]
[33] Melia, U., Gabarron, E., Agustí, M., Souto, N., Pineda, P., Fontanet, J., et al. (2016) Comparison of the qCON and qNOX Indices for the Assessment of Unconsciousness Level and Noxious Stimulation Response during Surgery. Journal of Clinical Monitoring and Computing, 31, 1273-1281. [Google Scholar] [CrossRef] [PubMed]
[34] Aho, A.J., Kamata, K., Jäntti, V., Kulkas, A., Hagihira, S., Huhtala, H., et al. (2015) Comparison of Bispectral Index and Entropy Values with Electroencephalogram during Surgical Anaesthesia with Sevoflurane. British Journal of Anaesthesia, 115, 258-266. [Google Scholar] [CrossRef] [PubMed]
[35] Cimenser, A., Purdon, P.L., Pierce, E.T., Walsh, J.L., Salazar-Gomez, A.F., Harrell, P.G., et al. (2011) Tracking Brain States under General Anesthesia by Using Global Coherence Analysis. Proceedings of the National Academy of Sciences, 108, 8832-8837. [Google Scholar] [CrossRef] [PubMed]
[36] Görges, M., West, N.C., Cooke, E.M., Pi, S., Brant, R.F., Dumont, G.A., et al. (2019) Evaluating NeuroSENSE for Assessing Depth of Hypnosis during Desflurane Anesthesia: An Adaptive, Randomized-Controlled Trial. Canadian Journal of Anesthesia/Journal canadien d’anesthésie, 67, 324-335. [Google Scholar] [CrossRef] [PubMed]
[37] Huang, Y., Wen, P., Song, B. and Li, Y. (2022) Real-Time Depth of Anaesthesia Assessment Based on Hybrid Statistical Features of EEG. Sensors, 22, Article 6099. [Google Scholar] [CrossRef] [PubMed]
[38] Gu, Y., Liang, Z. and Hagihira, S. (2019) Use of Multiple EEG Features and Artificial Neural Network to Monitor the Depth of Anesthesia. Sensors, 19, Article 2499. [Google Scholar] [CrossRef] [PubMed]
[39] Olivieri, F., Biscetti, L., Pimpini, L., Pelliccioni, G., Sabbatinelli, J. and Giunta, S. (2024) Heart Rate Variability and Autonomic Nervous System Imbalance: Potential Biomarkers and Detectable Hallmarks of Aging and Inflammaging. Ageing Research Reviews, 101, Article 102521. [Google Scholar] [CrossRef] [PubMed]
[40] Grossman, P. (2024) Respiratory Sinus Arrhythmia (RSA), Vagal Tone and Biobehavioral Integration: Beyond Parasympathetic Function. Biological Psychology, 186, Article 108739. [Google Scholar] [CrossRef] [PubMed]
[41] Zhao, Y., Yu, H., Gong, A., Zhang, S. and Xiao, B. (2023) Heart Rate Variability and Cardiovascular Diseases: A Mendelian Randomization Study. European Journal of Clinical Investigation, 54, e14085. [Google Scholar] [CrossRef] [PubMed]
[42] Zhan, J., Wu, Z., Duan, Z., Yang, G., Du, Z., Bao, X., et al. (2021) Heart Rate Variability-Derived Features Based on Deep Neural Network for Distinguishing Different Anaesthesia States. BMC Anesthesiology, 21, Article No. 66. [Google Scholar] [CrossRef] [PubMed]
[43] Tiwari, R., Kumar, R., Malik, S., Raj, T. and Kumar, P. (2021) Analysis of Heart Rate Variability and Implication of Different Factors on Heart Rate Variability. Current Cardiology Reviews, 17, Article ID: e160721189770. [Google Scholar] [CrossRef] [PubMed]
[44] Kanaya, N., Hirata, N., Kurosawa, S., Nakayama, M. and Namiki, A. (2003) Differential Effects of Propofol and Sevoflurane on Heart Rate Variability. Anesthesiology, 98, 34-40. [Google Scholar] [CrossRef] [PubMed]
[45] Keim, O.C., Bolwin, L., Feldmann, R.E., Thiel, M. and Benrath, J. (2024) Heart Rate Variability as a Predictor of Intraoperative Autonomic Nervous System Homeostasis. Journal of Clinical Monitoring and Computing, 38, 1305-1313. [Google Scholar] [CrossRef] [PubMed]
[46] Grossman, P. and Taylor, E.W. (2007) Toward Understanding Respiratory Sinus Arrhythmia: Relations to Cardiac Vagal Tone, Evolution and Biobehavioral Functions. Biological Psychology, 74, 263-285. [Google Scholar] [CrossRef] [PubMed]
[47] Grossman, P. (2023) Fundamental Challenges and Likely Refutations of the Five Basic Premises of the Polyvagal Theory. Biological Psychology, 180, Article 108589. [Google Scholar] [CrossRef] [PubMed]
[48] Charier, D., Vogler, M., Zantour, D., Pichot, V., Martins-Baltar, A., Courbon, M., et al. (2019) Assessing Pain in the Postoperative Period: Analgesia Nociception Indextm versus Pupillometry. British Journal of Anaesthesia, 123, e322-e327. [Google Scholar] [CrossRef] [PubMed]
[49] Fratino, S., Peluso, L., Talamonti, M., Menozzi, M., Costa Hirai, L., Lobo, F., et al. (2021) Evaluation of Nociception Using Quantitative Pupillometry and Skin Conductance in Critically Ill Unconscious Patients: A Pilot Study. Brain Sciences, 11, 109. [Google Scholar] [CrossRef] [PubMed]
[50] Upton, H.D., Ludbrook, G.L., Wing, A. and Sleigh, J.W. (2017) Intraoperative “Analgesia Nociception Index”—Guided Fentanyl Administration during Sevoflurane Anesthesia in Lumbar Discectomy and Laminectomy: A Randomized Clinical Trial. Anesthesia & Analgesia, 125, 81-90. [Google Scholar] [CrossRef] [PubMed]
[51] Kim, M.K., Choi, G.J., Oh, K.S., Lee, S.P. and Kang, H. (2023) Pain Assessment Using the Analgesia Nociception Index (ANI) in Patients Undergoing General Anesthesia: A Systematic Review and Meta-Analysis. Journal of Personalized Medicine, 13, Article 1461. [Google Scholar] [CrossRef] [PubMed]
[52] Vinclair, M., Roudaud, F., Francony, G. and Payen, J. (2017) Quantitative Pupillometry to Assess Nociception in a Sedated Patient with Hemispheric Cerebral Infarction. European Journal of Anaesthesiology, 34, 316-318. [Google Scholar] [CrossRef] [PubMed]
[53] Bornemann‐Cimenti, H., Lang‐Illievich, K., Kovalevska, K., Brenna, C.T.A. and Klivinyi, C. (2023) Effect of Nociception Level Index‐Guided Intra‐Operative Analgesia on Early Postoperative Pain and Opioid Consumption: A Systematic Review and Meta‐Analysis. Anaesthesia, 78, 1493-1501. [Google Scholar] [CrossRef] [PubMed]
[54] Ledowski, T., Schneider, M., Gruenewald, M., Goyal, R.K., Teo, S.R. and Hruby, J. (2019) Surgical Pleth Index: Prospective Validation of the Score to Predict Moderate-to-Severe Postoperative Pain. British Journal of Anaesthesia, 123, e328-e332. [Google Scholar] [CrossRef] [PubMed]

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