PDF(Pubmed)
Abstract：
Outcome prediction in prolonged disorders of consciousness (DOC) remains challenging. This can result in either inappropriate withdrawal of treatment or unnecessary prolongation of treatment. Electroencephalography (EEG) is a cheap, portable, and non-invasive device with various opportunities for complex signal analysis. Computational EEG measures, such as EEG connectivity and network metrics, might be ideal candidates for the investigation of DOC, but their capacity in prognostication is still undisclosed. We conducted a meta-analysis aiming to compare the prognostic power of the widely used clinical scale, Coma Recovery Scale-Revised - CRS-R and EEG connectivity and network metrics. We found that the prognostic power of the CRS-R scale was moderate (AUC: 0.67 (0.60-0.75)), but EEG connectivity and network metrics predicted outcome with significantly (p = 0.0071) higher accuracy (AUC:0.78 (0.70-0.86)). We also estimated the prognostic capacity of EEG spectral power, which was not significantly (p = 0.3943) inferior to that of the EEG connectivity and graph-theory measures (AUC:0.75 (0.70-0.80)). Multivariate automated outcome prediction tools seemed to outperform clinical and EEG markers.
摘要：
长期意识障碍（DOC）的结果预测仍然具有挑战性。这可能导致不适当的治疗退出或不必要的治疗延长。脑电图(EEG)是一种廉价的，便携式，和非侵入性的设备与复杂的信号分析的各种机会。计算脑电图测量，如脑电图连通性和网络指标，可能是DOC调查的理想人选，但是他们的预测能力仍未透露。我们进行了一项荟萃分析，旨在比较广泛使用的临床量表的预后能力，昏迷恢复量表-修订版-CRS-R和EEG连通性和网络指标。我们发现CRS-R量表的预后能力中等（AUC：0.67（0.60-0.75）），但脑电图连通性和网络指标预测结果具有显著(p=0.0071)更高的准确性(AUC:0.78(0.70-0.86))。我们还估计了脑电图谱功率的预后能力，与EEG连通性和图论测量（AUC：0.75（0.70-0.80））相比，没有显着（p=0.3943）。多变量自动结果预测工具似乎优于临床和脑电图标记。
