[Comparison of the accuracy of predicting poor outcome of coma after cardiopulmonary resuscitation with two kinds of electroencephalogram techniques].

OBJECTIVE

To compare the accuracy of electroencephalography (EEG) grading scale with amplitude-integrated electroencephalography (aEEG) in predicting poor outcomes (3-month), who sustained coma after cardiopulmonary resuscitation (CPR) in adults.

METHODS

A retrospective study was conducted. The patients with post-anoxic coma admitted to intensive care unit (ICU) of Tongren Hospital, Capital Medical University from March 2010 to June 2017 were enrolled. EEG was registered and recorded at least once within 7 days of coma after CPR, while not being subjected to therapeutic hypothermia. General data, Glasgow coma scale (GCS), EEG grading and aEEG model were collected. According to Glasgow prognosis score (GOS) of 3-month outcome, patients were divided into poor prognosis group (GOS 1-2) and good prognosis group (GOS 3-5), and the differences of related indexes between the two groups were compared. The predictive ability of aEEG model and EEG grading for brain function prognosis was evaluated by receiver operating characteristic (ROC) curve.

RESULTS

Fifty-four patients were included, with 31 males and 23 females, and age of (53.9±19.3) years. Among the EEG Young grades, 17 cases (31.5%) were grade 1, 4 cases (7.4%) were grade 2-5, and 33 cases (61.1%) were grade 6. Among the aEEG model grades, 26 cases (48.1%) had slow wave pattern grade 1, 23 cases (42.6%) had suppressed mode grade 4, 4 cases (7.4%) had status epilepticus mode grade 2, and 1 case (1.9%) had burst suppression mode grade 3. Thirty-six patients had poor prognosis 3-month after onset, 26 of them died and 10 had persistent vegetative state. The prognosis was good in 18 cases, including 16 cases with severe neurological disability and 2 cases with moderate neurological disability. There was no significant difference in gender, age, anoxic time between two groups with different prognosis, while the degree of consciousness disorder in poor prognosis group was more severe than that in good prognosis group (GCS score: 4.1±1.7 vs. 5.0±2.1, P < 0.05). The consistency test showed that different physicians had good consistency in EEG grading and aEEG model (Kappa values were 0.917 and 0.932, respectively). It was shown by ROC curve analysis that the area under ROC curve (AUC) of aEEG model and EEG grading for predicting poor prognosis of coma patients after CPR were 0.815 and 0.720, respectively (both P < 0.01); when the cut-off value of aEEG was 2.5, the sensitivity was 79.3%, the specificity was 77.4%, the positive likelihood ratios (PLR) was 3.508, and the negative likelihood ratios (NLR) was 0.267; when the cut-off value of EEG grading was 4.5, the sensitivity was 82.8%, the specificity was 61.3%, the PLR was 2.140, and NLR was 0.281.

CONCLUSIONS

aEEG model was more accurate in prognosticating poor outcomes (3-month) in patients with post-anoxic coma, when compared to EEG grading. Its operation was simple, so aEEG is very suitable in ICU.