저체온요법을 사용한 심정지 후 환자에서 정량뇌파분석을 통한 신경계 예후예측
Neurologic Prognostication by QEEG in Post Cardiac Arrest Patients with Therapeutic Hypothermia
2
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
Background: Post-cardiac arrest syndrome (PCAS) is one of the critical conditions which can result in a more serious brain injury. Early and accurate prognostication is crucial for deciding the patient’s therapeutic plan and setting the treatment goal. This study aimed to establish the prognostication values of quantitative electroencephalography (QEEG) in PCAS patients. Methods: We recruited 183 PCAS patients treated with therapeutic hypothermia. Electroencephalography (EEG) data within 72 hours after cardiac arrest (CA) and clinical data were collected. QEEG analysis including power spectral density (PSD) and connectivity analysis of default mode network (DMN) with imaginary coherence were performed. Results: There were significantly different patterns of PSD between neurologic good and poor outcome groups; absolute and relative power of the alpha 2 and beta 1 frequency (10-15 Hz) bands were increased in all brain regions of good outcome group. However, the relative power of the delta band and higher frequency bands over fast alpha (beta 3 and gamma bands over 20 Hz) were poor outcome markers. We found out that connectivity of DMN were significantly decreased in the poor outcome group compared with the good outcome group. Conclusions: These findings suggest that QEEG analysis could quantify and automate the interpretation of EEG. Furthermore, they can improve the prognostic values for neurologic outcomes relatively accurately and objectively in PCAS patients treated with hypothermia compared with traditional visual grading.
Abstract
Background: Post-cardiac arrest syndrome (PCAS) is one of the critical conditions which can result in a more serious brain injury. Early and accurate prognostication is crucial for deciding the patient’s therapeutic plan and setting the treatment goal. This study aimed to establish the prognostication values of quantitative electroencephalography (QEEG) in PCAS patients. Methods: We recruited 183 PCAS patients treated with therapeutic hypothermia. Electroencephalography (EEG) data within 72 hours after cardiac arrest (CA) and clinical data were collected. QEEG analysis including power spectral density (PSD) and connectivity analysis of default mode network (DMN) with imaginary coherence were performed. Results: There were significantly different patterns of PSD between neurologic good and poor outcome groups; absolute and relative power of the alpha 2 and beta 1 frequency (10-15 Hz) bands were increased in all brain regions of good outcome group. However, the relative power of the delta band and higher frequency bands over fast alpha (beta 3 and gamma bands over 20 Hz) were poor outcome markers. We found out that connectivity of DMN were significantly decreased in the poor outcome group compared with the good outcome group. Conclusions: These findings suggest that QEEG analysis could quantify and automate the interpretation of EEG. Furthermore, they can improve the prognostic values for neurologic outcomes relatively accurately and objectively in PCAS patients treated with hypothermia compared with traditional visual grading.
참고문헌 (32)
- Mozaffarian D, "Heart disease and stroke statistics--2015 update : a report from the American Heart Association", Circulation, 131, pp.e29~e322 (2015)
- Kim YH, "Feasibility of optic nerve sheath diameter measured on initial brain computed tomography as an early neurologic outcome predictor after cardiac arrest", Acad Emerg Med, 21, pp.1121~1128 (2014)
- Young GB, "Clinical practice. Neurologic prognosis after cardiac arrest", N Engl J Med, 361, pp.605~611 (2009)
- Golan E, "Predicting neurologic outcome after targeted temperature management for cardiac arrest : systematic review and meta-analysis", Crit Care Med, 42, pp.1919~1930 (2014)
- Lodder SS, "Quantification of the adult EEG background pattern", Clin Neurophysiol, 124, pp.228~237 (2013)
- Hypothermia after Cardiac Arrest Study Group, "Mild therapeutic hypothermia to improve the neurologic outcome after cardiac arrest", N Engl J Med, 346, pp.549~556 (2002)
- Asgari S, "Quantitative measures of EEG for prediction of outcome in cardiac arrest subjects treated with hypothermia : a literature review", J Clin Monit Comput, 32, pp.977~992 (2018)
- Buckner RL, "The brain's default network : anatomy, function, and relevance to disease", Ann N Y Acad Sci, 1124, pp.1~38 (2008)
- McKiernan KA, "Interrupting the"stream of consciousness" : an fMRI investigation", Neuroimage, 29, pp.1185~1191 (2006)
- Norton L, "Disruptions of functional connectivity in the default mode network of comatose patients", Neurology, 78, pp.175~181 (2012)
- Cauda F, "Disrupted intrinsic functional connectivity in the vegetative state", J Neurol Neurosurg Psychiatry, 80, pp.429~431 (2009)
- Vanhaudenhuyse A, "Default network connectivity reflects the level of consciousness in non-communicative brain-damaged patients", Brain, 133, pp.161~171 (2010)
- Boly M, "Functional connectivity in the default network during resting state is preserved in a vegetative but not in a brain dead patient", Hum Brain Mapp, 30, pp.2393~2400 (2009)
- McLaren J, "Functional connectivity in term neonates with hypoxic-ischemic encephalopathy undergoing therapeutic hypothermia", Pediatr Neurol, pp.74~79 (2019)
- Beudel M, "Small-world characteristics of EEG patterns in post-anoxic encephalopathy", Front Neurol, 5, pp.97~ (2014)
- Jennett B, "Assessment of outcome after severe brain damage", Lancet, 1, pp.480~484 (1975)
- Delorme A, "Independent EEG sources are dipolar", PLoS One, 7, pp.e30135~ (2012)
- Delorme A, "EEGLAB : an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis", J Neurosci Methods, 134, pp.9~21 (2004)
- Desikan RS, "An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest", Neuroimage, 31, pp.968~980 (2006)
- Holmes CJ, "Enhancement of MR images using registration for signal averaging", J Comput Assist Tomogr, 22, pp.324~333 (1998)
- Pascual-Marqui RD, "Standardized low-resolution brain electromagnetic tomography(sLORETA) : technical details", Methods Find Exp Clin Pharmacol, 24(Suppl D), pp.5~12 (2002)
- Nolte G, "Identifying true brain interaction from EEG data using the imaginary part of coherency", Clin Neurophysiol, 115, pp.2292~2307 (2004)
- Fugate JE, "Predictors of neurologic outcome in hypothermia after cardiac arrest", Ann Neurol, 68, pp.907~914 (2010)
- Sivaraju A, "Prognostication of post-cardiac arrest coma : early clinical and electroencephalographic predictors of outcome", Intensive Care Med, 41, pp.1264~1272 (2015)
- Hofmeijer J, "Early EEG contributes to multimodal outcome prediction of postanoxic coma", Neurology, 85, pp.137~143 (2015)
- Wijdicks EF, "Practice parameter: prediction of outcome in comatose survivors after cardiopulmonary resuscitation (an evidence-based review): report of the Quality Standards Subcommittee of the American Academy of Neurology", Neurology, 67, pp.203~210 (2006)
- Sandroni C, "Prognostication in comatose survivors of cardiac arrest : an advisory statement from the European Resuscitation Council and the European Society of Intensive Care Medicine", Resuscitation, 85, pp.1779~1789 (2014)
- Taccone FS, "Neuroprognostication after adult cardiac arrest treated with targeted temperature management : task force for Belgian recommendations", Acta Neurol Belg, 117, pp.3~15 (2017)
- Sondag L, "Early EEG for outcome prediction of postanoxic coma : prospective cohort study with cost-minimization analysis", Crit Care, 21, pp.111~ (2017)
- Rothstein TL, "Predicting outcome in hypoxic-ischemic coma. A prospective clinical and electrophysiologic study", Electroencephalogr Clin Neurophysiol, 79, pp.101~107 (1991)
- Borjigin J, "Surge of neurophysiological coherence and connectivity in the dying brain", Proc Natl Acad Sci U S A, 110, pp.14432~14437 (2013)
- Knyazev GG, "The default mode network and EEG α oscillations : an independent component analysis", Brain Res, 1402, pp.67~79 (2011)