Guo Zhibao, Wan Wang, Liu Wenxue, Liu Ling, Yang Yi, Yang Congshan, Cui Xingran
Jiangsu Provincial Key Laboratory of Critical Care Medicine, Department of Critical Care Medicine, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, 210009, China.
State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210009, China.
Sci Rep. 2024 Dec 28;14(1):31101. doi: 10.1038/s41598-024-82422-7.
The diagnostic and prognostic value of quantitative electroencephalogram (qEEG) in the the onset of postoperative delirium (POD) remains an area of inquiry. We aim to determine whether qEEG could assist in the diagnosis of early POD in cardiac surgery patients. We prospectively studied a cohort of cardiac surgery patients undergoing qEEG for evaluation of altered mental status. Delirium was assessed with the Confusion Assessment Method for the intensive care unit (CAM-ICU). The qEEG were interpreted by clinician, and reports were reviewed to identify features such as amplitude-integrated EEG (aEEG), relative band energy in ɑ/β/θ/δ frequencies, α variability and spectral entropy. The raw EEG was also preprocessed offline for nonlinear analysis including Multi-scale Entropy analysis (MSE) and Detrended Fluctuation Analysis (DFA). Linear regression was performed to quantify associations among EEG findings, delirium, and clinical outcomes. Receiver operating characteristic (ROC) analysis was used to assess the accuracy of the qEEG as POD prediction index. Meanwhile, a comprehensive comparison of dynamic complexity across time scales and DFA exponent α was conducted between the non-delirium and delirium groups. Among those recruited initially (n = 64), 60 patients were evaluated and 29 patients (48.3%) met delirium criteria. When comparing delirious and non-delirious participants, significant differences were found in terms of age (p = 0.03), APACHE II scores (p = 0.004), lactate (p = 0.03), and hospital days (p = 0.048). Multivariate regression analysis revealed that the first quartile (Q1) and fourth quartile (Q4) of peak or valley value of F3-P3/F4-P4 derivation (for example, Q1 of peak value for F3-P3 derivation: OR 12.4, 95% CI 1.72-89.76, p = 0.012) showed a higher association with the incidence of POD. ROC analysis demonstrated qEEG could predict POD with high sensitivity and specificity, yielding an overall good accuracy. For instance, the peak value of F3-P3 derivation (the area under the curve of 0.81), as a predictor of POD showed a sensitivity of 90% and specificity pf 72% (p < 0.001). Furthermore, the MSE curves indicated that the non-delirium group exhibited higher complexity values at fine scales, while the delirium group had significantly higher complexity at coarse scales. The DFA comparison results revealed that long-term fractal exponent alpha2 values were higher in delirium patients than in non-delirium patients, with significant differences observed at the F4-P4 electrodes (p = 0.04). The qEEG can reliably predict delirium after heart cardiac surgery. It is helpful for clinicians to early diagnose and manage these patients.Trial registration: Clinical Trials.gov Identifier, NCT03351985. Registered 1 December 2017.
定量脑电图（qEEG）在术后谵妄（POD）发病中的诊断和预后价值仍是一个研究领域。我们旨在确定qEEG是否有助于诊断心脏手术患者的早期POD。我们前瞻性地研究了一组接受qEEG检查以评估精神状态改变的心脏手术患者。使用重症监护病房谵妄评估方法（CAM-ICU）评估谵妄。qEEG由临床医生解读，并审查报告以识别诸如振幅整合脑电图（aEEG）、α/β/θ/δ频率的相对频段能量、α变异性和频谱熵等特征。原始脑电图还进行了离线预处理以进行非线性分析，包括多尺度熵分析（MSE）和去趋势波动分析（DFA）。进行线性回归以量化脑电图结果、谵妄和临床结局之间的关联。使用受试者工作特征（ROC）分析评估qEEG作为POD预测指标的准确性。同时，在非谵妄组和谵妄组之间进行了跨时间尺度的动态复杂性和DFA指数α的综合比较。在最初招募的64名患者中，60名患者接受了评估，29名患者（48.3%）符合谵妄标准。在比较谵妄和非谵妄参与者时，发现年龄（p = 0.03）、急性生理与慢性健康状况评分系统II（APACHE II）评分（p = 0.004）、乳酸（p = 0.03）和住院天数（p = 0.048）存在显著差异。多变量回归分析显示，F3-P3/F4-P4导联峰值或谷值的第一四分位数（Q1）和第四四分位数（Q4）（例如，F3-P3导联峰值的Q1：比值比12.4，95%置信区间1.72 - 89.76，p = 0.012）与POD的发生率具有更高的相关性。ROC分析表明，qEEG能够以高灵敏度和特异性预测POD，总体准确性良好。例如，F3-P3导联的峰值（曲线下面积为0.81）作为POD的预测指标，灵敏度为90%，特异性为72%（p < 0.001）。此外，MSE曲线表明，非谵妄组在精细尺度上表现出更高的复杂性值，而谵妄组在粗糙尺度上具有显著更高的复杂性。DFA比较结果显示，谵妄患者的长期分形指数alpha2值高于非谵妄患者，在F4-P4电极处观察到显著差异（p = 0.04）。qEEG能够可靠地预测心脏手术后的谵妄。这有助于临床医生对这些患者进行早期诊断和管理。试验注册：ClinicalTrials.gov标识符，NCT03351985。于2017年12月1日注册。
