Perioperative, electroencephalographic characteristics of postoperative delirum in elderly

老年患者术后谵妄（POD）是手术后脑功能障碍最常见的临床表现。POD与住院时间延长、死亡率升高以及长期认知功能受损相关。识别个体诱发因素和避免沉淀危险因素是有效预防POD的必要条件。然而，到目前为止，还没有有效的生物标志物来识别处于发展POD的较高风险的患者，其可以使麻醉师调整麻醉程序/药物并预测POD治疗选项。（BIS指数、Narcotrend指数、PSI指数）用于常规麻醉实践，不考虑年龄和不同麻醉剂。对全麻下脑电数据振荡进行复杂的频谱分析是当前研究项目的重点，分析了全麻期间不同麻醉剂和年龄组的影响。不同的麻醉剂与不同的分子靶点和神经回路相互作用以诱导无意识，随后诱导不同的术中EEG特征。埃默里·布朗周围的研究小组最近通过对原始EEG文件进行频谱分析来代表这一点。当旨在识别有发生POD风险的老年患者的围手术期EEG特征时，这一知识至关重要。有趣的是，已经证明，长时间的深度麻醉，如通过原始EEG的视觉分析识别长时间爆发抑制时期所分类的，是导致POD发生率较高的主要促发风险因素之一。此外，EEG频谱分析最近已被证明是监测患有神经退行性疾病的老年患者的认知功能的有价值的工具，可以帮助预测从轻度认知障碍到痴呆的转换。基于这些来自EEG中更复杂的分析方法的结果，我们假设可以使用特定的EEG参数/特征在围手术期识别有发生POD风险的患者。在本研究中，我们专注于从术前，术中和术后EEG记录中获得的生物标志物。我们的目的是（1）识别术前EEG标记物，表明患者有发生POD的风险;（2）指定与POD相关的术中EEG特征/状态;（3）检测在恢复室停留期间与POD直接相关的EEG特征，因此可用作诊断工具。 我们希望通过识别基于EEG的POD生物标志物来改善老年患者的术后认知结果，该生物标志物采用更复杂的围手术期EEG数据分析方法开发，考虑到与年龄和不同麻醉剂相关的EEG动力学。

Postoperative Delirium (POD) in elderly patients is the most common clinical manifestation of brain dysfunction following surgery. POD is associated with an increased length of hospital stay, higher mortality rate, as well as an impaired long-term cognitive function. Identifying individual predisposing factors and avoiding precipitating risk factors are essential for an effective POD prevention. However, until now there are no effective biomarkers to identify patients at higher risk for developing POD that may enable anesthesiologist to adapt the anesthetic procedure / medication and to prepone POD therapy options.Although EEG monitoring is the most feasible approach for tracking brain states under general anesthesia, nowadays solely a single EEG derived, Index (BIS-Index, Narcotrend Index, PSI Index) is used in routine anesthesiological practices not taking age and different anesthetic agents into account. Sophisticated spectral analysis of EEG data oscillation under general anesthesia are in the focus of current research projects analyzing the effect of different anesthetic agents and age groups during general anesthesia. Different anesthetics agents interact with different molecular targets and neural circuits to induce unconsciousness, subsequently inducing distinct intraoperative EEG signatures. The research group around Emery Brown has recently represented this by conducting spectral analysis of raw EEG files. This knowledge is critical, when aiming to identify perioperative EEG signatures in elderly patients at risk to develop POD. Interestingly, it has been proven that prolonged periods of deep anesthesia, as classified by visual analysis of the raw EEG identifying prolonged burst suppression epochs, are one of the main precipitating risk factors contributing to a higher incidence of POD. Additionally, EEG spectral analysis has recently been shown to be a valuable tool to monitor cognitive function in elderly patients with neurodegenerative diseases that could help predict conversion from mild cognitive impairment to dementia. Based on these results derived from more sophisticated analysis methods in EEG, we presume that patients at risk for developing POD can be identified perioperatively using specific EEG parameters / signatures. In the present study, we focus on biomarkers obtained from pre-, intra- and post-operative EEG recordings. We aim (1) to identify preoperative EEG markers indicating patients at risk to develop POD; (2) to specify intraoperative EEG signatures / states that are related to POD; and (3) to detect EEG signatures during stay in the recovery room that are directly related to POD, and may therefore be used as diagnostic tool. We want to improve postoperative, cognitive outcome in elderly patients, by identifying EEG based POD-Biomarkers, developed with more sophisticated, perioperative EEG data analysis methods, taking EEG dynamics related to age and different anesthetics agents into account.