Abstract：
BACKGROUND: Patients with traumatic brain injury are a heterogeneous population, and the most severely injured individuals are often treated in an intensive care unit (ICU). The primary injury at impact, and the harmful secondary events that can occur during the first week of the ICU stay, will affect outcome in this vulnerable group of patients. We aimed to identify clinical variables that might distinguish disease trajectories among patients with traumatic brain injury admitted to the ICU.
METHODS: We used data from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) prospective observational cohort study. We included patients aged 18 years or older with traumatic brain injury who were admitted to the ICU at one of the 65 CENTER-TBI participating centres, which range from large academic hospitals to small rural hospitals. For every patient, we obtained pre-injury data and injury features, clinical characteristics on admission, demographics, physiological parameters, laboratory features, brain biomarkers (ubiquitin carboxy-terminal hydrolase L1 [UCH-L1], S100 calcium-binding protein B [S100B], tau, neurofilament light [NFL], glial fibrillary acidic protein [GFAP], and neuron-specific enolase [NSE]), and information about intracranial pressure lowering treatments during the first 7 days of ICU stay. To identify clinical variables that might distinguish disease trajectories, we applied a novel clustering method to these data, which was based on a mixture of probabilistic graph models with a Markov chain extension. The relation of clusters to the extended Glasgow Outcome Scale (GOS-E) was investigated.
RESULTS: Between Dec 19, 2014, and Dec 17, 2017, 4509 patients with traumatic brain injury were recruited into the CENTER-TBI core dataset, of whom 1728 were eligible for this analysis. Glucose variation (defined as the difference between daily maximum and minimum glucose concentrations) and brain biomarkers (S100B, NSE, NFL, tau, UCH-L1, and GFAP) were consistently found to be the main clinical descriptors of disease trajectories (ie, the leading variables contributing to the distinguishing clusters) in patients with traumatic brain injury in the ICU. The disease trajectory cluster to which a patient was assigned in a model was analysed as a predictor together with variables from the IMPACT model, and prediction of both mortality and unfavourable outcome (dichotomised GOS-E ≤4) was improved.
CONCLUSIONS: First-day ICU admission data are not the only clinical descriptors of disease trajectories in patients with traumatic brain injury. By analysing temporal variables in our study, variation of glucose was identified as the most important clinical descriptor that might distinguish disease trajectories in the ICU, which should direct further research. Biomarkers of brain injury (S100B, NSE, NFL, tau, UCH-L1, and GFAP) were also top clinical descriptors over time, suggesting they might be important in future clinical practice.
BACKGROUND: European Union 7th Framework program, Hannelore Kohl Stiftung, OneMind, Integra LifeSciences Corporation, and NeuroTrauma Sciences.
METHODS: We used data from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) prospective observational cohort study. We included patients aged 18 years or older with traumatic brain injury who were admitted to the ICU at one of the 65 CENTER-TBI participating centres, which range from large academic hospitals to small rural hospitals. For every patient, we obtained pre-injury data and injury features, clinical characteristics on admission, demographics, physiological parameters, laboratory features, brain biomarkers (ubiquitin carboxy-terminal hydrolase L1 [UCH-L1], S100 calcium-binding protein B [S100B], tau, neurofilament light [NFL], glial fibrillary acidic protein [GFAP], and neuron-specific enolase [NSE]), and information about intracranial pressure lowering treatments during the first 7 days of ICU stay. To identify clinical variables that might distinguish disease trajectories, we applied a novel clustering method to these data, which was based on a mixture of probabilistic graph models with a Markov chain extension. The relation of clusters to the extended Glasgow Outcome Scale (GOS-E) was investigated.
RESULTS: Between Dec 19, 2014, and Dec 17, 2017, 4509 patients with traumatic brain injury were recruited into the CENTER-TBI core dataset, of whom 1728 were eligible for this analysis. Glucose variation (defined as the difference between daily maximum and minimum glucose concentrations) and brain biomarkers (S100B, NSE, NFL, tau, UCH-L1, and GFAP) were consistently found to be the main clinical descriptors of disease trajectories (ie, the leading variables contributing to the distinguishing clusters) in patients with traumatic brain injury in the ICU. The disease trajectory cluster to which a patient was assigned in a model was analysed as a predictor together with variables from the IMPACT model, and prediction of both mortality and unfavourable outcome (dichotomised GOS-E ≤4) was improved.
CONCLUSIONS: First-day ICU admission data are not the only clinical descriptors of disease trajectories in patients with traumatic brain injury. By analysing temporal variables in our study, variation of glucose was identified as the most important clinical descriptor that might distinguish disease trajectories in the ICU, which should direct further research. Biomarkers of brain injury (S100B, NSE, NFL, tau, UCH-L1, and GFAP) were also top clinical descriptors over time, suggesting they might be important in future clinical practice.
BACKGROUND: European Union 7th Framework program, Hannelore Kohl Stiftung, OneMind, Integra LifeSciences Corporation, and NeuroTrauma Sciences.
摘要：
背景：创伤性脑损伤患者是异质人群，最严重受伤的人通常在重症监护病房（ICU）接受治疗。撞击时的主要伤害，以及ICU住院第一周可能发生的有害二次事件，会影响这个脆弱患者群体的预后。我们的目的是确定临床变量，以区分ICU收治的创伤性脑损伤患者的疾病轨迹。
方法：我们使用的数据来自欧洲合作的创伤性脑损伤神经创伤有效性研究（CENTER-TBI）前瞻性观察性队列研究。我们纳入了65个CENTER-TBI参与中心之一入住ICU的18岁或以上创伤性脑损伤患者，从大型学术医院到小型乡村医院。对每个病人来说,我们获得了伤前数据和损伤特征，入院时的临床特征，人口统计，生理参数,实验室特点,脑生物标志物(泛素羧基末端水解酶L1[UCH-L1]，S100钙结合蛋白B[S100B],tau,神经丝光[NFL]，胶质纤维酸性蛋白[GFAP]，和神经元特异性烯醇化酶[NSE])，以及ICU住院前7天颅内压降低治疗的相关信息。为了确定可能区分疾病轨迹的临床变量，我们对这些数据应用了一种新的聚类方法，它基于具有马尔可夫链扩展的概率图模型的混合。研究了聚类与扩展的格拉斯哥结果量表（GOS-E）的关系。
结果：在2014年12月19日至2017年12月17日之间，招募了4509名创伤性脑损伤患者进入CENTER-TBI核心数据集，其中1728人符合这项分析的资格。葡萄糖变化（定义为每日最大和最小葡萄糖浓度之间的差异）和大脑生物标志物（S100B，NSE,NFL,tau,UCH-L1和GFAP)被一致发现是疾病轨迹的主要临床描述符(即，导致区分聚类的主要变量）在ICU中的创伤性脑损伤患者中。将患者在模型中被分配到的疾病轨迹聚类与IMPACT模型中的变量一起作为预测因子进行分析。并且对死亡率和不良结局(二分类GOS-E≤4)的预测均得到改善.
结论：入住ICU的第一天数据并不是创伤性脑损伤患者疾病轨迹的唯一临床描述指标。通过分析我们研究中的时间变量，葡萄糖的变化被确定为最重要的临床描述符，可以区分ICU中的疾病轨迹，这应该指导进一步的研究。脑损伤的生物标志物(S100B，NSE,NFL,tau,随着时间的推移，UCH-L1和GFAP)也是最高的临床描述符，表明它们在未来的临床实践中可能很重要。
背景：欧盟第七框架计划，HanneloreKohlStiftung,OneMind,IntegraLifeSciences公司，和神经创伤科学。
方法：我们使用的数据来自欧洲合作的创伤性脑损伤神经创伤有效性研究（CENTER-TBI）前瞻性观察性队列研究。我们纳入了65个CENTER-TBI参与中心之一入住ICU的18岁或以上创伤性脑损伤患者，从大型学术医院到小型乡村医院。对每个病人来说,我们获得了伤前数据和损伤特征，入院时的临床特征，人口统计，生理参数,实验室特点,脑生物标志物(泛素羧基末端水解酶L1[UCH-L1]，S100钙结合蛋白B[S100B],tau,神经丝光[NFL]，胶质纤维酸性蛋白[GFAP]，和神经元特异性烯醇化酶[NSE])，以及ICU住院前7天颅内压降低治疗的相关信息。为了确定可能区分疾病轨迹的临床变量，我们对这些数据应用了一种新的聚类方法，它基于具有马尔可夫链扩展的概率图模型的混合。研究了聚类与扩展的格拉斯哥结果量表（GOS-E）的关系。
结果：在2014年12月19日至2017年12月17日之间，招募了4509名创伤性脑损伤患者进入CENTER-TBI核心数据集，其中1728人符合这项分析的资格。葡萄糖变化（定义为每日最大和最小葡萄糖浓度之间的差异）和大脑生物标志物（S100B，NSE,NFL,tau,UCH-L1和GFAP)被一致发现是疾病轨迹的主要临床描述符(即，导致区分聚类的主要变量）在ICU中的创伤性脑损伤患者中。将患者在模型中被分配到的疾病轨迹聚类与IMPACT模型中的变量一起作为预测因子进行分析。并且对死亡率和不良结局(二分类GOS-E≤4)的预测均得到改善.
结论：入住ICU的第一天数据并不是创伤性脑损伤患者疾病轨迹的唯一临床描述指标。通过分析我们研究中的时间变量，葡萄糖的变化被确定为最重要的临床描述符，可以区分ICU中的疾病轨迹，这应该指导进一步的研究。脑损伤的生物标志物(S100B，NSE,NFL,tau,随着时间的推移，UCH-L1和GFAP)也是最高的临床描述符，表明它们在未来的临床实践中可能很重要。
背景：欧盟第七框架计划，HanneloreKohlStiftung,OneMind,IntegraLifeSciences公司，和神经创伤科学。
