Zhou Liang, Chen Yuanyi, Liu Ziyuan, You Jia, Chen Siming, Liu Ganzhi, Yu Yang, Wang Jian, Chen Xin
Department of Neurosurgery, Xiangya Hospital of Central South University, Changsha, Hunan, China.
National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, Hunan, China.
Front Neurosci. 2023 Feb 8;17:1088666. doi: 10.3389/fnins.2023.1088666. eCollection 2023.
Predicting the consciousness recovery for comatose patients with acute brain injury is an important issue. Although some efforts have been made in the study of prognostic assessment methods, it is still unclear which factors can be used to establish model to directly predict the probability of consciousness recovery.
We aimed to establish a model using clinical and neuroelectrophysiological indicators to predict consciousness recovery of comatose patients after acute brain injury.
The clinical data of patients with acute brain injury admitted to the neurosurgical intensive care unit of Xiangya Hospital of Central South University from May 2019 to May 2022, who underwent electroencephalogram (EEG) and auditory mismatch negativity (MMN) examinations within 28 days after coma onset, were collected. The prognosis was assessed by Glasgow Outcome Scale (GOS) at 3 months after coma onset. The least absolute shrinkage and selection operator (LASSO) regression analysis was applied to select the most relevant predictors. We combined Glasgow coma scale (GCS), EEG, and absolute amplitude of MMN at Fz to develop a predictive model using binary logistic regression and then presented by a nomogram. The predictive efficiency of the model was evaluated with AUC and verified by calibration curve. The decision curve analysis (DCA) was used to evaluate the clinical utility of the prediction model.
A total of 116 patients were enrolled for analysis, of which 60 had favorable prognosis (GOS ≥ 3). Five predictors, including GCS (OR = 13.400, < 0.001), absolute amplitude of MMN at Fz site (FzMMNA, OR = 1.855, = 0.038), EEG background activity (OR = 4.309, = 0.023), EEG reactivity (OR = 4.154, = 0.030), and sleep spindles (OR = 4.316, = 0.031), were selected in the model by LASSO and binary logistic regression analysis. This model showed favorable predictive power, with an AUC of 0.939 (95% CI: 0.899-0.979), and calibration. The threshold probability of net benefit was between 5% and 92% in the DCA.
This predictive model for consciousness recovery in patients with acute brain injury is based on a nomogram incorporating GCS, EEG background activity, EEG reactivity, sleep spindles, and FzMMNA, which can be conveniently obtained during hospitalization. It provides a basis for care givers to make subsequent medical decisions.
预测急性脑损伤昏迷患者的意识恢复是一个重要问题。尽管在预后评估方法的研究方面已经做出了一些努力，但仍不清楚哪些因素可用于建立直接预测意识恢复概率的模型。
我们旨在建立一个使用临床和神经电生理指标的模型，以预测急性脑损伤后昏迷患者的意识恢复情况。
收集了2019年5月至2022年5月在中南大学湘雅医院神经外科重症监护病房收治的急性脑损伤患者的临床资料，这些患者在昏迷发作后28天内接受了脑电图（EEG）和听觉失匹配负波（MMN）检查。在昏迷发作后3个月通过格拉斯哥预后量表（GOS）评估预后。应用最小绝对收缩和选择算子（LASSO）回归分析来选择最相关的预测因子。我们将格拉斯哥昏迷量表（GCS）、EEG和Fz点MMN的绝对波幅相结合，使用二元逻辑回归建立预测模型，然后通过列线图呈现。用AUC评估模型的预测效率，并通过校准曲线进行验证。采用决策曲线分析（DCA）评估预测模型的临床实用性。
共纳入116例患者进行分析，其中60例预后良好（GOS≥3）。通过LASSO和二元逻辑回归分析，模型中选择了5个预测因子，包括GCS（OR = 13.400，P<0.001）、Fz点MMN的绝对波幅（FzMMNA，OR = 1.855，P = 0.038）、EEG背景活动（OR = 4.309，P = 0.023）、EEG反应性（OR = 4.154，P = 0.030）和睡眠纺锤波（OR = 4.316，P = 0.031）。该模型显示出良好的预测能力，AUC为0.939（95%CI：0.899 - 0.979），且具有校准性。在DCA中，净效益的阈值概率在5%至92%之间。
这个用于急性脑损伤患者意识恢复的预测模型基于一个包含GCS、EEG背景活动、EEG反应性、睡眠纺锤波和FzMMNA的列线图，这些指标在住院期间可方便地获得。它为护理人员做出后续医疗决策提供了依据。
