While the Glasgow coma scale (GCS) is one of the strongest outcome predictors, the current classification of traumatic brain injury (TBI) as ‘mild’, ‘moderate’ or ‘severe’ based on this fails to capture enormous heterogeneity in pathophysiology and treatment response. We hypothesized that data-driven characterization of TBI could identify distinct endotypes and give mechanistic insights. We developed an unsupervised statistical clustering model based on a mixture of probabilistic graphs for presentation (< 24 h) demographic, clinical, physiological, laboratory and imaging data to identify subgroups of TBI patients admitted to the intensive care unit in the CENTER-TBI dataset (N = 1,728). A cluster similarity index was used for robust determination of optimal cluster number. Mutual information was used to quantify feature importance and for cluster interpretation. Six stable endotypes were identified with distinct GCS and composite systemic metabolic stress profiles, distinguished by GCS, blood lactate, oxygen saturation, serum creatinine, glucose, base excess, pH, arterial partial pressure of carbon dioxide, and body temperature. Notably, a cluster with ‘moderate’ TBI (by traditional classification) and deranged metabolic profile, had a worse outcome than a cluster with ‘severe’ GCS and a normal metabolic profile. Addition of cluster labels significantly improved the prognostic precision of the IMPACT (International Mission for Prognosis and Analysis of Clinical trials in TBI) extended model, for prediction of both unfavourable outcome and mortality (both p < 0.001). Six stable and clinically distinct TBI endotypes were identified by probabilistic unsupervised clustering. In addition to presenting neurology, a profile of biochemical derangement was found to be an important distinguishing feature that was both biologically plausible and associated with outcome. Our work motivates refining current TBI classifications with factors describing metabolic stress. Such data-driven clusters suggest TBI endotypes that merit investigation to identify bespoke treatment strategies to improve care. Trial registration The core study was registered with ClinicalTrials.gov, number NCT02210221 , registered on August 06, 2014, with Resource Identification Portal (RRID: SCR_015582).

中文翻译：

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聚类确定重症监护队列中创伤性脑损伤的内型：CENTER-TBI 研究

虽然格拉斯哥昏迷量表 (GCS) 是最强的结果预测指标之一，但目前基于此将创伤性脑损伤 (TBI) 分类为“轻度”、“中度”或“重度”未能捕捉到病理生理学和治疗的巨大异质性回复。我们假设 TBI 的数据驱动表征可以识别不同的内型并提供机械见解。我们开发了一种无监督统计聚类模型，基于用于呈现（<24 小时）人口统计、临床、生理、实验室和影像数据的概率图的混合，以识别 CENTER-TBI 数据集中入住重症监护病房的 TBI 患者亚组（ N = 1,728)。聚类相似性指数用于稳健地确定最佳聚类数。互信息用于量化特征重要性和聚类解释。鉴定出六种具有不同 GCS 和复合全身代谢应激特征的稳定内型，分别通过 GCS、血乳酸、氧饱和度、血清肌酐、葡萄糖、碱过剩、pH、动脉二氧化碳分压和体温来区分。值得注意的是，具有“中度”TBI（按照传统分类）和紊乱的代谢特征的集群，其结果比具有“严重”GCS 和正常代谢特征的集群更差。添加集群标签显着提高了 IMPACT（TBI 临床试验预后和分析国际使命）扩展模型的预后精度，用于预测不利结果和死亡率（均 p < 0.001）。通过概率无监督聚类确定了六种稳定且临床上不同的 TBI 内型。除了呈现神经病学，生化紊乱的概况被发现是一个重要的区别特征，在生物学上是合理的并且与结果相关。我们的工作激发了用描述代谢压力的因素来改进当前的 TBI 分类。这种数据驱动的集群表明 TBI 内型值得研究以确定定制的治疗策略以改善护理。试验注册 核心研究已在 ClinicalTrials.gov 注册，编号为 NCT02210221，注册于 2014 年 8 月 6 日，注册于资源识别门户网站 (RRID: SCR_015582)。生化紊乱的概况被发现是一个重要的区别特征，它在生物学上是合理的并且与结果相关。我们的工作激发了用描述代谢压力的因素来改进当前的 TBI 分类。这种数据驱动的集群表明 TBI 内型值得研究以确定定制的治疗策略以改善护理。试验注册 核心研究已在 ClinicalTrials.gov 注册，编号为 NCT02210221，注册于 2014 年 8 月 6 日，注册于资源识别门户网站 (RRID: SCR_015582)。生化紊乱的概况被发现是一个重要的区别特征，它在生物学上是合理的并且与结果相关。我们的工作激发了用描述代谢压力的因素来改进当前的 TBI 分类。这种数据驱动的集群表明 TBI 内型值得研究以确定定制的治疗策略以改善护理。试验注册 核心研究已在 ClinicalTrials.gov 注册，编号为 NCT02210221，注册于 2014 年 8 月 6 日，注册于资源识别门户网站 (RRID: SCR_015582)。这种数据驱动的集群表明 TBI 内型值得研究以确定定制的治疗策略以改善护理。试验注册 核心研究已在 ClinicalTrials.gov 注册，编号为 NCT02210221，注册于 2014 年 8 月 6 日，注册于资源识别门户网站 (RRID: SCR_015582)。这种数据驱动的集群表明 TBI 内型值得研究以确定定制的治疗策略以改善护理。试验注册 核心研究已在 ClinicalTrials.gov 注册，编号为 NCT02210221，注册于 2014 年 8 月 6 日，注册于资源识别门户网站 (RRID: SCR_015582)。