Clinical Neurophysiology Group, University of Twente, Enschede, the Netherlands; Department of Neurology and Clinical Neurophysiology, Medisch Spectrum Twente, Enschede, the Netherlands; Department of Neurology, Amsterdam UMC/VUmc, Amsterdam, the Netherlands.
Department of Neurology and Clinical Neurophysiology, Medisch Spectrum Twente, Enschede, the Netherlands.
Neuroimage Clin. 2023;37:103350. doi: 10.1016/j.nicl.2023.103350. Epub 2023 Feb 14.
There is a need for reliable predictors in patients with moderate to severe traumatic brain injury to assist clinical decision making. We assess the ability of early continuous EEG monitoring at the intensive care unit (ICU) in patients with traumatic brain injury (TBI) to predict long term clinical outcome and evaluate its complementary value to current clinical standards. We performed continuous EEG measurements in patients with moderate to severe TBI during the first week of ICU admission. We assessed the Extended Glasgow Outcome Scale (GOSE) at 12 months, dichotomized into poor (GOSE 1-3) and good (GOSE 4-8) outcome. We extracted EEG spectral features, brain symmetry index, coherence, aperiodic exponent of the power spectrum, long range temporal correlations, and broken detailed balance. A random forest classifier using feature selection was trained to predict poor clinical outcome based on EEG features at 12, 24, 48, 72 and 96 h after trauma. We compared our predictor with the IMPACT score, the best available predictor, based on clinical, radiological and laboratory findings. In addition we created a combined model using EEG as well as the clinical, radiological and laboratory findings. We included hundred-seven patients. The best prediction model using EEG parameters was found at 72 h after trauma with an AUC of 0.82 (0.69-0.92), specificity of 0.83 (0.67-0.99) and sensitivity of 0.74 (0.63-0.93). The IMPACT score predicted poor outcome with an AUC of 0.81 (0.62-0.93), sensitivity of 0.86 (0.74-0.96) and specificity of 0.70 (0.43-0.83). A model using EEG and clinical, radiological and laboratory parameters resulted in a better prediction of poor outcome (p < 0.001) with an AUC of 0.89 (0.72-0.99), sensitivity of 0.83 (0.62-0.93) and specificity of 0.85 (0.75-1.00). EEG features have potential use for predicting clinical outcome and decision making in patients with moderate to severe TBI and provide complementary information to current clinical standards.
对于中度至重度创伤性脑损伤患者，需要可靠的预测指标来辅助临床决策。我们评估了在创伤性脑损伤（TBI）患者的重症监护病房（ICU）中进行早期连续脑电图监测以预测长期临床结果的能力，并评估其对当前临床标准的补充价值。我们在 ICU 入院的第一周内对中度至重度 TBI 患者进行了连续 EEG 测量。我们在 12 个月时评估了扩展格拉斯哥结局量表（GOSE），分为不良（GOSE 1-3）和良好（GOSE 4-8）结局。我们提取了 EEG 频谱特征、脑对称指数、相干性、功率谱的非周期性指数、长程时间相关性和破坏的详细平衡。使用特征选择的随机森林分类器根据创伤后 12、24、48、72 和 96 小时的 EEG 特征训练来预测不良临床结局。我们将我们的预测器与 IMPACT 评分（基于临床，影像学和实验室发现的最佳预测器）进行了比较。此外，我们还使用 EEG 以及临床，影像学和实验室发现创建了一个组合模型。我们纳入了 107 例患者。在创伤后 72 小时发现使用 EEG 参数的最佳预测模型，AUC 为 0.82（0.69-0.92），特异性为 0.83（0.67-0.99），敏感性为 0.74（0.63-0.93）。IMPACT 评分预测不良结局的 AUC 为 0.81（0.62-0.93），敏感性为 0.86（0.74-0.96），特异性为 0.70（0.43-0.83）。使用 EEG 和临床，影像学和实验室参数的模型对不良结局的预测效果更好（p <0.001），AUC 为 0.89（0.72-0.99），敏感性为 0.83（0.62-0.93），特异性为 0.85（0.75-1.00）。EEG 特征有可能用于预测中度至重度 TBI 患者的临床结局并为临床决策提供依据，并为当前的临床标准提供补充信息。
