Abstract
Objective
To explore the value of electroencephalogram (EEG) pattern in predicting awakening
of comatose patients after cardiopulmonary resuscitation (CPR).
Methods
A retrospective cohort study was conducted on comatose patients after CPR in the neuro-critical
care unit of Xuanwu Hospital, Capital Medical University, from 2002 to 2018. The included
patients received clinical evaluation, and the Glasgow coma scale (GCS) score was
recorded. Bedside EEG monitoring was performed for visual grading and reactivity detection.
The 3-month prognostic assessment was performed using the Glasgow outcome scale (GOS).
The patients were dichotomized into the awakening group (GOS 3–5) and the unawakening
group (GOS 1–2).
Results
A total of 160 patients were included. There was no significant difference in the
baseline data between the two groups except that the GCS score of the awakening group
was higher (P = 0.000). Different EEG patterns were used to predict awakening from
coma. As a result, the slow wave pattern showed the highest accuracy (73.1%, 95% CI:
0.66–0.79), and the sensitivity and specificity reached 61.3% (95% CI: 0.48–0.73)
and 80.6% (95% CI: 0.71–0.88), respectively. Compared with EEG reactivity, the slow
wave pattern was more sensitive in predicting awakening. It was also more specific
than GCS in predicting awakening. The slow wave pattern within different time frame
after coma was used to predict the prognosis of awakening, suggesting that the accuracy
(100%, 95% CI: 0.75–1.00), sensitivity (100%, 95% CI: 0.46–1.00), and specificity
(100%, 95% CI: 0.63–1.00) of predicting awakening within 8–14 days were the highest.
Conclusions
The slow wave pattern of EEG had a good predictive value for awakening in comatose
patients after CPR, and the highest accuracy occurred within 8–14 days from coma.
Keywords
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Article info
Publication history
Published online: November 27, 2019
Accepted:
November 1,
2019
Received in revised form:
October 14,
2019
Received:
April 16,
2019
Identification
Copyright
© 2019 Published by Elsevier B.V.
