长程脑电图监测对重症脑功能损伤的评价

Dynamic long-term electroencephalogram assessment of critical cerebrovascular disease

目的 应用长程脑电图 (EEG)研究重症脑血管疾病的EEG动态变化 ,旨在建立评价脑功能损伤程度和预测预后的新方法。方法 对于CT或MRI证实病变位于大脑半球的急性重症 (GCS评分 <8分 )脑血管疾病患者进行床边长程脑电图动态监测 ,按Young氏分级标准分级判断 ,同时进行GCS -P评分。结果 EEG分级Ⅰ级的生存率为 6 1 9% ,EEG分级≥Ⅲ级的生存的可能性极小。EEG分级生存预测的准确率 92 31% ,死亡的预测准确率 70 5 9%。GCS -P生存的预测准确率84 6 2 % ,死亡的预测准确率 70 5 9%。结论 长程EEG动态监测和分级判断可以准确、客观地评价和预测重症脑血管疾病脑功能损伤的预后。

Objective We used bedside long-term electroencephalogram (EEG) to observe the patterns of eletroencephalograms (EEGs) and classify the EEGs according to Young's electroencephalographic classification system in patients with critical cerebral vascular disease (CCVD). The aim of this study was to investigate the predictive value and accuracy of EEG's classification, so that we could establish an objective, accurate and immediate indicator to assess cerebral function and prognosis. Methods All the 30 patients suffered from critical cerebral vascular disease in the distribution of carotid artery system. EEGs classification and Glasgow-Pittsburgh coma scale (GCS-P) were dynamically recorded at the bedside with CCVD occurred in 8～72 hours, patient alone with GCS<8 were admitted. Using discriminant analysis, the predictability of outcome was calculated. Results Cases with the classification of grade Ⅰ showed that the mortality was 38.1%, and those with the classification of Ⅰa was 29.4% and Ⅰb was 75%. All the patients with the classification of Ⅲb or higher died. We found that only GCS-P and EEG classification were reliably predictive for outcome. EEG and GCS-P classification could correctly classified 92.31% and 84.62% of the patients into good outcome groups; while they could classified 70.59% and 70.59% of the patients into bad outcome groups. Combined them could not increase prognostic accuracy. Conclusion EEG classification was excellent outcome predictors in CCVD. EEG classification could accurately classify 92.31% of the patients into good outcome group. The dynamic observation of EEG classification was the determinant factor for prognosis, especially for the patients with EEG classification Ⅰa or Ⅰb.