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(Traumatic Brain Injury, TBI)因其固有异质性表现为广泛的临床症状和预后，导致康复轨迹多样且治疗反应各异。尽管已有诸多研究针对特定患者群体进行TBI表型分析，但识别能在不同环境和人群中稳定泛化的TBI表型仍是关键研究空白。本研究通过多变量时间序列聚类揭示TBI的动态复杂性，采用基于自监督学习的含缺失值多变量时间序列数据聚类方法(SLAC-Time)，分析了以研究为核心的TRACK-TBI数据集和真实世界的MIMIC-IV数据集。值得注意的是，SLAC-Time的最优超参数和理想聚类数量在两个数据集中保持一致，凸显了该方法在异质性数据集中的稳定性。分析揭示了三种可泛化的TBI表型（α、β和γ），各自在急诊就诊期间呈现独特的非时间特征，并在重症监护住院期间呈现不同的时间特征谱。具体而言，α表型代表轻度TBI，具有显著一致的临床表现；β表型代表重度TBI，临床表现多样；γ表型在严重程度和临床多样性上表现为中度TBI特征。年龄是TBI预后的重要决定因素，年长人群的死亡率更高。重要的是，尽管部分特征随年龄变化，但各表型对应的TBI核心表现特征在不同人群中保持一致性。