Neurologic Prognostication by QEEG in Post Cardiac Arrest Patients with Therapeutic Hypothermia
- Author:
Sue Hyun LEE
1
;
Hyung Seok AHN
;
Yong Hwan KIM
;
Hyang Woon LEE
;
Jung Hwa LEE
Author Information
- Publication Type:원저
- From:Journal of the Korean Neurological Association 2020;38(4):260-271
- CountryRepublic of Korea
- Language:Korean
-
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.
