聚类分析确定了重症监护队列中创伤性脑损伤的表型：CENTER-TBI 研究。

BACKGROUND: 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. METHODS: 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. RESULTS: 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). CONCLUSIONS: 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).

背景：格拉斯哥昏迷评分（GCS）是最强的预后预测因子之一，但目前根据该评分将创伤性脑损伤（TBI）分类为“轻度”、“中度”或“重度”，未能捕捉到病理生理学和治疗反应的巨大异质性。我们假设基于数据的 TBI 特征描述可以识别不同的表型，并提供机制见解。

方法：我们基于混合概率图开发了一种无监督统计聚类模型，用于呈现（<24 小时）人口统计学、临床、生理、实验室和影像学数据，以识别 CENTER-TBI 数据集（N=1728）中 ICU 收治的 TBI 患者亚组。使用聚类相似性指数来确定最佳聚类数。互信息用于量化特征重要性和聚类解释。

结果：确定了 6 种稳定的表型，具有不同的 GCS 和复合全身代谢应激特征，通过 GCS、血乳酸、氧饱和度、血清肌酐、血糖、碱剩余、pH、动脉二氧化碳分压和体温来区分。值得注意的是，一个具有“中度”TBI（按传统分类）和代谢紊乱特征的聚类比具有“重度”GCS 和正常代谢特征的聚类的预后更差。添加聚类标签显著提高了 IMPACT（创伤性脑损伤临床试验国际使命）扩展模型预测不良结局和死亡率的预后准确性（均<0.001）。

结论：通过概率无监督聚类确定了 6 种稳定且具有临床差异的 TBI 表型。除了表现出神经病学特征外，还发现生化紊乱特征是一个重要的鉴别特征，既具有生物学合理性，又与结局相关。我们的工作促使用描述代谢应激的因素来改进当前的 TBI 分类。这种基于数据的聚类提示 TBI 表型值得进一步研究，以确定改善护理的定制治疗策略。

试验注册：核心研究在 ClinicalTrials.gov 上注册，编号为 NCT02210221，于 2014 年 8 月 6 日注册，资源标识符门户（RRID：SCR_015582）。