Proescholdt Martin A, Faltermeier Rupert, Bele Sylvia, Brawanski Alexander
Department of Neurosurgery, University Hospital Regensburg, Regensburg, Germany.
Comput Math Methods Med. 2017;2017:8454527. doi: 10.1155/2017/8454527. Epub 2017 Jan 31.
Multimodal brain monitoring has been utilized to optimize treatment of patients with critical neurological diseases. However, the amount of data requires an integrative tool set to unmask pathological events in a timely fashion. Recently we have introduced a mathematical model allowing the simulation of pathophysiological conditions such as reduced intracranial compliance and impaired autoregulation. Utilizing a mathematical tool set called selected correlation analysis (sca), correlation patterns, which indicate impaired autoregulation, can be detected in patient data sets (scp). In this study we compared the results of the sca with the pressure reactivity index (PRx), an established marker for impaired autoregulation. Mean PRx values were significantly higher in time segments identified as scp compared to segments showing no selected correlations (nsc). The sca based approach predicted cerebral autoregulation failure with a sensitivity of 78.8% and a specificity of 62.6%. Autoregulation failure, as detected by the results of both analysis methods, was significantly correlated with poor outcome. Sca of brain monitoring data detects impaired autoregulation with high sensitivity and sufficient specificity. Since the sca approach allows the simultaneous detection of both major pathological conditions, disturbed autoregulation and reduced compliance, it may become a useful analysis tool for brain multimodal monitoring data.
多模态脑监测已被用于优化重症神经疾病患者的治疗。然而，数据量需要一套综合工具集以便及时揭示病理事件。最近我们引入了一个数学模型，可模拟诸如颅内顺应性降低和自动调节受损等病理生理状况。利用一种名为选定相关性分析（sca）的数学工具集，能够在患者数据集中检测到表明自动调节受损的相关性模式（scp）。在本研究中，我们将sca的结果与压力反应性指数（PRx）进行了比较，PRx是自动调节受损的一个既定标志物。与未显示选定相关性的时间段（nsc）相比，在被确定为scp的时间段内，平均PRx值显著更高。基于sca的方法预测脑自动调节功能衰竭的灵敏度为78.8%，特异性为62.6%。两种分析方法的结果所检测到的自动调节功能衰竭与不良预后显著相关。对脑监测数据进行sca能够以高灵敏度和足够的特异性检测到自动调节受损。由于sca方法能够同时检测两种主要病理状况，即自动调节紊乱和顺应性降低，它可能会成为脑多模态监测数据的一种有用分析工具。
