Traumatic Brain Injury (TBI) presents a broad spectrum of clinical presentations and outcomes due to its inherent heterogeneity, leading to diverse recovery trajectories and varied therapeutic responses. While many studies have delved into TBI phenotyping for distinct patient populations, identifying TBI phenotypes that consistently generalize across various settings and populations remains a critical research gap. Our research addresses this by employing multivariate time-series clustering to unveil TBI's dynamic intricates. Utilizing a self-supervised learning-based approach to clustering multivariate time-Series data with missing values (SLAC-Time), we analyzed both the research-centric TRACK-TBI and the real-world MIMIC-IV datasets. Remarkably, the optimal hyperparameters of SLAC-Time and the ideal number of clusters remained consistent across these datasets, underscoring SLAC-Time's stability across heterogeneous datasets. Our analysis revealed three generalizable TBI phenotypes ({\alpha}, \b{eta}, and {\gamma}), each exhibiting distinct non-temporal features during emergency department visits, and temporal feature profiles throughout ICU stays. Specifically, phenotype {\alpha} represents mild TBI with a remarkably consistent clinical presentation. In contrast, phenotype \b{eta} signifies severe TBI with diverse clinical manifestations, and phenotype {\gamma} represents a moderate TBI profile in terms of severity and clinical diversity. Age is a significant determinant of TBI outcomes, with older cohorts recording higher mortality rates. Importantly, while certain features varied by age, the core characteristics of TBI manifestations tied to each phenotype remain consistent across diverse populations.

翻译：创伤性脑损伤（TBI）因其固有的异质性而呈现出广泛的临床表现和预后差异，导致恢复轨迹多样且治疗反应不一。尽管已有许多研究针对特定患者群体进行TBI表型分析，但识别能够跨不同环境和人群稳定泛化的TBI表型仍是一个关键的研究空白。本研究通过采用多元时间序列聚类方法来揭示TBI的动态复杂性，填补了这一空白。我们利用基于自监督学习的缺失值多元时间序列聚类方法（SLAC-Time），分析了以科研为导向的TRACK-TBI数据集和真实世界的MIMIC-IV数据集。值得注意的是，SLAC-Time的最佳超参数和最优聚类数量在这些数据集间保持一致，这凸显了SLAC-Time在异质数据集上的稳定性。我们的分析揭示了三种可泛化的TBI表型（α、β和γ），每种表型在急诊科就诊期间展现出独特的非时序特征，并在ICU住院期间呈现不同的时序特征模式。具体而言，表型α代表临床表现高度一致的轻度TBI；表型β代表临床表现多样的重度TBI；而表型γ则在严重程度和临床多样性方面表现为中度TBI。年龄是TBI预后的重要决定因素，老年群体的死亡率更高。重要的是，尽管某些特征随年龄变化，但与各表型相关的TBI表现核心特征在不同人群中保持稳定。