脑电图在神经重症昏迷患者预后预测中的研究进展
Research Progress of Electroencephalogram in Predicting Prognosis of Patients with Severe Neurological Coma
DOI: 10.12677/ACM.2023.1361339, PDF,   
作者: 熊 成, 张 杨, 戴永建*：湖北医药学院附属十堰市人民医院神经外科，湖北 十堰
关键词: 脑电描记术；意识障碍；昏迷；预后；Electroencephalography； Disturbance of Consciousness； Coma； Prognosis
摘要: 神经重症患者大多存在严重的意识障碍，病情复杂且多变，而临床中对此类患者的病情评估及预后预测大多依赖于主观性的量表和客观性的影像学检查，存在一定的时间滞后性。脑电图作为神经功能学检查之一，以其无创性、动态及时性、廉价性、相对准确性等越来越受到临床医师的重视。本文从脑电活动的角度出发，收集近几年相关文献，探讨脑电图在神经重症昏迷患者预后预测中的影响。
Abstract: Most of the severe neurological patients have serious disturbance of consciousness, the condition is complex and changeable, and the clinical evaluation and the prognosis prediction of such patients mostly depend on subjective scales and objective imaging examinations, which has a certain time lag. As one of the neurofunctional examinations, the EEG has been paid more and more attention by clinicians because of its non-invasiveness, dynamic timeliness, cheapness and relative accuracy. From the point of view of EEG activity, this paper collected a few relevant literatures in recent years to explore the effect of the EEG on the prognosis of patients with severe neurological coma.
文章引用：熊成, 张杨, 戴永建. 脑电图在神经重症昏迷患者预后预测中的研究进展[J]. 临床医学进展, 2023, 13(6): 9569-9576. https://doi.org/10.12677/ACM.2023.1361339

参考文献

[1] 刘晓燕. 临床脑电图学[M]. 北京: 人民卫生出版社, 2017: 570-588.
[2] 管向东. 重症量化脑电图[M]. 广州: 广东科技出版社, 2019: 2-28.
[3] Sanz, L.R.D., Thibaut, A., Edlow, B.L., et al. (2021) Update on Neuroimaging in Dis-orders of Consciousness. Current Opinion in Neurology, 34, 488-496. [Google Scholar] [CrossRef] 
[4] Chen, S., Lachance, B.B., Gao, L. and Jia, X.F. (2021) Targeted Temperature Management and Early Neuro-Prognostication after Cardiac Arrest. Journal of Cerebral Blood Flow & Metabolism, 41, 1193-1209. [Google Scholar] [CrossRef] 
[5] Pruvost-Robieux, E., Marchi, A., Martinelli, I., et al. (2022) Evoked and Event-Related Potentials as Biomarkers of Consciousness State and Recovery. Journal of Clinical Neuro-physiology, 39, 22-31. [Google Scholar] [CrossRef] 
[6] Smith, A.E. and Friess, S.H. (2020) Neurological Prognos-tication in Children after Cardiac Arrest. Pediatric Neurology, 108, 13-22. [Google Scholar] [CrossRef] [PubMed]
[7] Fidali, B.C., Stevens, R.D. and Claassen, J. (2020) Novel Approaches to Prediction in Severe Brain Injury. Current Opinion in Neurology, 33, 669-675. [Google Scholar] [CrossRef] 
[8] 宋薛艺, 李哲, 王国胜, 等. 意识障碍患者意识水平评估方法研究进展[J]. 颈腰痛杂志, 2022, 43(6): 922-925.
[9] Azabou, E., Navarro, V., Kubis, N., et al. (2018) Value and Mechanisms of EEG Reactivity in the Prognosis of Patients with Impaired Consciousness: A Systematic Review. Critical Care, 22, Article No. 184. [Google Scholar] [CrossRef] [PubMed]
[10] Khazanova, D., Douglas, V.C. and Amorim, E. (2021) A Matter of Timing: EEG Monitoring for Neurological Prognostication after Cardiac Arrest in the Era of Targeted Temperature Management. Minerva Anestesiologica, 87, 704-713.
[11] Bouchereau, E., Marchi, A., Hermann, B., et al. (2023) Quantitative Analysis of Early-Stage EEG Reactivity Predicts Awakening and Recovery of Consciousness in Patients with Severe Brain Injury. British Journal of Anaesthesia, 130, e225-e232. [Google Scholar] [CrossRef] [PubMed]
[12] Wang, J., Huang, L., Ma, X., Zhao, J., Liu, J. and Xu, D. (2022) Role of Quantitative EEG and EEG Reactivity in Traumatic Brain Injury. Clinical EEG and Neuroscience, 53, 452-459. [Google Scholar] [CrossRef] [PubMed]
[13] Hockaday, J.M., Potts, F., Epstein, E., et al. (1965) Electroen-cephalographic Changes in Acute Cerebral Anoxia from Cardiac or Respiratory Arrest. Electroencephalography and Clinical Neurophysiology, 18, 575-586. [Google Scholar] [CrossRef] [PubMed]
[14] Synek, V.M. (1988) Prognostically Important EEG Coma Patterns in Diffuse Anoxic and Traumatic Encephalopathies in Adults. Journal of Clinical Neurophysiology, 5, 161-174. [Google Scholar] [CrossRef] [PubMed]
[15] Young, G.B., McLachlan, R.S., Kreeft, J.H. and Demelo, J.D. (1997) An Electroencephalographic Classification for Coma. Canadian Journal of Neurological Sciences/Journal Canadien des Sciences Neurologiques, 24, 320-325. [Google Scholar] [CrossRef] 
[16] Su, Y.Y., Wang, M., Chen, W.B., et al. (2013) Early Prediction of Poor Outcome in Severe Hemispheric Stroke by EEG Patterns and Gradings. Neurological Research, 35, 512-516. [Google Scholar] [CrossRef] 
[17] Hirsch, L.J., Fong, M.W.K., Leitinger, M., et al. (2021) American Clinical Neurophysiology Society’s Standardized Critical Care EEG Terminology: 2021 Version. Journal of Clinical Neurophysiology, 38, 1-29. [Google Scholar] [CrossRef] 
[18] Westhall, E., Rossetti, A.O., van Rootselaar, A.F., et al. (2016) Standardized EEG Interpretation Accurately Predicts Prognosis after Cardiac Arrest. Neurology, 86, 1482-1490. [Google Scholar] [CrossRef] 
[19] Sekar, K., Schiff, N.D., Labar, D. and Forgacs, P.B. (2019) Spectral Content of Electroencephalographic Burst-Suppression Patterns May Reflect Neuronal Recovery in Comatose Post-Cardiac Arrest Patients. Journal of Clinical Neurophysiology, 36, 119-126. [Google Scholar] [CrossRef] 
[20] Willems, L.M., Trienekens, F., Knake, S., et al. (2021) EEG Patterns and Their Correlations with Short- and Long-Term Mortality in Patients with Hypoxic Encephalopathy. Clinical Neurophysiology, 132, 2851-2860. [Google Scholar] [CrossRef] [PubMed]
[21] Kim, Y., Kim, M., Kim, Y.H., et al. (2021) Background Fre-quency Can Enhance the Prognostication Power of EEG Patterns Categories in Comatose Cardiac Arrest Survivors: A Prospective, Multicenter, Observational Cohort Study. Critical Care, 25, Article No. 398. [Google Scholar] [CrossRef] [PubMed]
[22] Ruijter, B.J., Keijzer, H.M., Tjepkema-Cloostermans, M.C., et al. (2022) Treating Rhythmic and Periodic EEG Patterns in Comatose Survivors of Cardiac Arrest. The New England Jour-nal of Medicine, 386, 724-734. [Google Scholar] [CrossRef] 
[23] Johnsen, B., Nøhr, K.B., Duez, C.H.V. and Ebbesen, M.Q. (2017) The Nature of EEG Reactivity to Light, Sound and Pain Stimulation in Neurosurgical Comatose Patients Evaluated by a Quantitative Method. Clinical EEG and Neuroscience, 48, 428-437. [Google Scholar] [CrossRef] [PubMed]
[24] Amorim, E., Gilmore, E.J., Abend, N.S., et al. (2018) EEG Reac-tivity Evaluation Practices for Adult and Pediatric Hypoxic-Ischemic Coma Prognostication in North America. Journal of Clinical Neurophysiology, 35, 510-514. [Google Scholar] [CrossRef] 
[25] Benghanem, S., Paul, M., Charpentier, J., et al. (2019) Value of EEG reactivity for prediction of Neurologic Outcome after Cardiac Arrest: Insights from the Parisian Registry. Resuscitation, 142, 168-174. [Google Scholar] [CrossRef] [PubMed]
[26] Johnsen, B., Jeppesen, J. and Duez, C.H.V. (2022) Com-mon Patterns of EEG Reactivity in Post-Anoxic Coma Identified by Quantitative Analyses. Clinical Neurophysiology, 142, 143-153. [Google Scholar] [CrossRef] [PubMed]
[27] 江茜茜, 元小冬, 吴宗武, 等. 脑电图对神经重症意识障碍患者预后评估的研究进展[J]. 中华危重症医学杂志(电子版), 2017, 10(6): 421-425.
[28] 余学婕. 脑电图对神经重症意识障碍患者预后评估的研究进展[J]. 中外医学研究, 2019, 17(15): 176-178.
[29] Pauli, R., O’Donnell, A. and Cruse, D. (2020) Resting-State Electroencephalography for Prognosis in Disorders of Consciousness Following Traumatic Brain Injury. Frontiers in Neurology, 11, Article 586945. [Google Scholar] [CrossRef] [PubMed]
[30] Altwegg-Boussac, T., Schramm, A.E., Ballestero, J., et al. (2017) Cortical Neurons and Networks Are Dormant But Fully Responsive during Isoelectric Brain State. Brain, 140, 2381-2398. [Google Scholar] [CrossRef] [PubMed]
[31] You, W., Tang, Q., Wu, X., Feng, J., Mao, Q., Gao, G. and Jiang, J. (2018) Amplitude-Integrated Electroencephalography Predicts Outcome in Patients with Coma After Acute Brain Injury. Neuroscience Bulletin, 34, 639-646. [Google Scholar] [CrossRef] [PubMed]
[32] Lu, J.P., Che, C.H. and Huang, H.P. (2020) Comparison of the Accuracy of Predicting Prognosis of Brain Function in Patients after Cardiopulmonary Cerebral Resuscitation with Two Kinds of Electroencephalogram Techniques Combined with Neuron-Specific Enolase. Chinese Medical Journal, 100, 1629-1633. (In Chinese)
[33] Zhao, W., Liu, Y., Pan, H.R., et al. (2021) [Predictive Value of Quantitative Electroen-cephalogram in the Poor Outcome of Children with Non-Traumatic Disturbance of Consciousness in Pediatric Intensive Care Unit]. Chinese Journal of Pediatrics, 59, 374-379. (In Chinese)
[34] Vespa, P.M., Boscardin, W.J., Hovda, D.A. and David, L. (2002) Early and Persistent Impaired Percent Alpha Variability on Continuous Electroencephalography Monitoring as Predictive of Poor Outcome after Traumatic Brain Injury. Journal of Neurosurgery, 97, 84-92. [Google Scholar] [CrossRef] [PubMed]
[35] Hebb, M.O., McArthur, D.L., Alger, J., et al. (2007) Impaired Percent Alpha Variability on Continuous Electroencephalography Is Associated with Thalamic Injury and Predicts Poor Long-Term Outcome after Human Traumatic Brain Injury. Journal of Neurotrauma, 24, 579-590. [Google Scholar] [CrossRef] [PubMed]
[36] Fingelkurts, A.A., Fingelkurts, A.A., Bagnato, S., et al. (2016) Long-Term (Six Years) Clinical Outcome Discrimination of Patients in the Vegetative State Could be Achieved Based on the Operational Architectonics EEG Analysis: A Pilot Feasibility Study. The Open Neuroimaging Journal, 10, 69-79. [Google Scholar] [CrossRef] [PubMed]
[37] Bareham, C.A., Allanson, J., Roberts, N., et al. (2018) Lon-gitudinal Bedside Assessments of Brain Networks in Disorders of Consciousness: Case Reports from the Field. Fron-tiers in Neurology, 9, Article 676. [Google Scholar] [CrossRef] [PubMed]
[38] 吕威, 虞容豪, 谢秋幼. 脑机接口在意识障碍中应用的研究进展[J]. 医学研究杂志, 2020, 49(3): 12-15.
[39] Wolpaw, J.R., Millan, J. and Ramsey, N.F. (2020) Chapter 2—Brain-Computer Interfaces: Definitions and Principles. In: Handbook of Clinical Neurology, Vol. 168, Elsevier, Am-sterdam, 15-23. [Google Scholar] [CrossRef] 
[40] 李远清. 脑机接口技术在意识障碍领域应用的前景展望[J]. 中华神经创伤外科电子杂志, 2015, 1(2): 60-61.
[41] Gibson, R.M., Owen, A.M. and Cruse, D. (2016) Chapter 9—Brain-Computer Interfaces for Patients with Disorders of Consciousness. In: Progress in Brain Research, Vol. 228, Elsevier, Amsterdam, 241-291. [Google Scholar] [CrossRef] [PubMed]
[42] Pan, J., Xie, Q., Qin, P., et al. (2020) Prognosis for Patients with Cognitive Motor Dissociation Identified by Brain-Computer Interface. Brain, 143, 1177-1189. [Google Scholar] [CrossRef] [PubMed]
[43] Li, J., Huang, B., Wang, F., et al. (2022) A Potential Prognosis Indica-tor Based on P300 Brain-Computer Interface for Patients with Disorder of Consciousness. Brain Sciences, 12, Article 1556. [Google Scholar] [CrossRef] [PubMed]

为你推荐