Schneider Gerhard, Hollweck Regina, Ningler Michael, Stockmanns Gudrun, Kochs Eberhard F
Department of Anesthesiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany.
Anesthesiology. 2005 Nov;103(5):934-43. doi: 10.1097/00000542-200511000-00006.
A set of electroencephalographic and auditory evoked potential (AEP) parameters should be identified that allows separation of consciousness from unconsciousness (reflected by responsiveness/unresponsiveness to command).
Forty unpremedicated patients received anesthesia with remifentanil and either sevoflurane or propofol. With remifentanil infusion (0.2 microg . kg . min), patients were asked every 30 s to squeeze the investigator's hand. Sevoflurane or propofol was given until loss of consciousness. After intubation, propofol or sevoflurane was stopped until patients followed the command (return of consciousness). Thereafter, propofol or sevoflurane was started again (loss of consciousness), and surgery was performed. Return of consciousness was observed after surgery. The electroencephalogram and AEP from immediately before and after the transitions were selected. Logistic regression was calculated to identify models for the separation between consciousness and unconsciousness. For the top 10 models, 1,000-fold cross-validation was performed. Backward variable selection was applied to identify a minimal model. Prediction probability was calculated. The digitized electroencephalogram was replayed, and the Bispectral Index was measured and accordingly analyzed.
The best full model (prediction probability 0.89) contained 15 AEP and 4 electroencephalographic parameters. The best minimal model (prediction probability 0.87) contained 2 AEP and 2 electroencephalographic parameters (median frequency of the amplitude spectrum from 8-30 Hz and approximate entropy). The prediction probability of the Bispectral Index was 0.737.
A combination of electroencephalographic and AEP parameters can be used to differentiate between consciousness and unconsciousness even in a very challenging data set. The minimal model contains a combination of AEP and electroencephalographic parameters and has a higher prediction probability than Bispectral Index for the separation between consciousness and unconsciousness.
应确定一组脑电图和听觉诱发电位(AEP)参数,以实现意识与无意识状态的区分(通过对指令的反应性/无反应性体现)。
40例未使用术前药的患者接受瑞芬太尼联合七氟醚或丙泊酚麻醉。在输注瑞芬太尼(0.2微克·千克·分钟)时,每隔30秒要求患者挤压研究者的手。给予七氟醚或丙泊酚直至患者失去意识。插管后,停止丙泊酚或七氟醚输注,直至患者能听从指令(意识恢复)。此后,再次开始输注丙泊酚或七氟醚(意识丧失),并进行手术。观察术后意识恢复情况。选取意识转变前后即刻的脑电图和AEP数据。计算逻辑回归以确定区分意识和无意识状态的模型。对排名前10的模型进行1000倍交叉验证。应用向后变量选择法确定最小模型。计算预测概率。回放数字化脑电图,测量并分析脑电双频指数。
最佳完整模型(预测概率0.89)包含15个AEP参数和4个脑电图参数。最佳最小模型(预测概率0.87)包含2个AEP参数和2个脑电图参数(8 - 30赫兹振幅谱的中位数频率和近似熵)。脑电双频指数的预测概率为0.737。
即使在极具挑战性的数据集中,脑电图和AEP参数的组合也可用于区分意识和无意识状态。最小模型包含AEP和脑电图参数的组合,在区分意识和无意识状态方面比脑电双频指数具有更高的预测概率。