Preserved Electroencephalogram Power and Global Synchronization Predict Better Neurological Outcome in Sudden Cardiac Arrest Survivors

Quantitative EEG (qEEG) delineates complex brain activities. Global field synchronization (GFS) is one multichannel EEG analysis that measures global functional connectivity through quantification of synchronization between signals. We hypothesized that preservation of global functional connectivity...

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Bibliographic Details
Published in:	Frontiers in physiology Vol. 13; p. 866844
Main Authors:	Ho, Li-Ting, Serafico, Bess Ma Fabinal, Hsu, Ching-En, Chen, Zhao-Wei, Lin, Tse-Yu, Lin, Chen, Lin, Lian-Yu, Lo, Men-Tzung, Chien, Kuo-Liong
Format:	Journal Article
Language:	English
Published:	Switzerland Frontiers Media S.A 20-04-2022
Subjects:	electroencephalography global synchronization neurological outcome phase coherence Physiology prognosis sudden cardiac arrest neurological outcome electroencephalography sudden cardiac arrest global synchronization phase coherence prognosis
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Summary:	Quantitative EEG (qEEG) delineates complex brain activities. Global field synchronization (GFS) is one multichannel EEG analysis that measures global functional connectivity through quantification of synchronization between signals. We hypothesized that preservation of global functional connectivity of brain activity might be a surrogate marker for good outcome in sudden cardiac arrest (SCA) survivors. In addition, we examined the relation of phase coherence and GFS in a mathematical approach. We retrospectively collected EEG data of SCA survivors in one academic medical center. We included 75 comatose patients who were resuscitated following in-hospital or out-of-hospital nontraumatic cardiac arrest between 2013 and 2017 in the intensive care unit (ICU) of National Taiwan University Hospital (NTUH). Twelve patients (16%) were defined as good outcome (GO) (CPC 1-2). The mean age in the GO group was low (51.6 ± 15.7 vs. 68.1 ± 12.9, < 0.001). We analyzed standard EEG power, computed EEG GFS, and assessed the cerebral performance category (CPC) score 3 months after discharge. The alpha band showed the highest discrimination ability (area under curve [AUC] = 0.78) to predict GO using power. The alpha band of GFS showed the highest AUC value (0.8) to predict GO in GFS. Furthermore, by combining EEG power + GFS, the alpha band showed the best prediction value (AUC 0.86) in predicting GO. The sensitivity of EEG power + GFS was 73%, specificity was 93%, PPV was 0.67%, and NPV was 0.94%. In conclusion, by combining GFS and EEG power analysis, the neurological outcome of the nontraumatic cardiac arrest survivor can be well-predicted. Furthermore, we proved from a mathematical point of view that although both amplitude and phase contribute to obtaining GFS, the interference in phase variation drastically changes the possibility of generating a good GFS score.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Reviewed by: Han Hwa Hu, Taipei Medical University, Taiwan Edited by: Chien-Hung Yeh, Beijing Institute of Technology, China These authors share first authorship Seo-Young Lee, Kangwon National University, South Korea This article was submitted to Computational Physiology and Medicine, a section of the journal Frontiers in Physiology
ISSN:	1664-042X 1664-042X
DOI:	10.3389/fphys.2022.866844