For a study, researchers sought to understand that data-driven traumatic brain injury (TBI) characterization could differentiate between different endotypes and provide mechanistic details. In the CENTER-TBI dataset (N=1,728), they developed an unsupervised statistical clustering model based on a combination of probabilistic graphs for presentation (<24 h) of demographic, clinical, physiological, laboratory, and imaging data to identify subgroups of TBI patients admitted to the intensive care unit. A cluster similarity index was used to find the best cluster number. Cluster analysis and feature relevance assessment were conducted using mutual information. About 6 stable endotypes with various Glasgow coma scale (GCS) and composite systemic metabolic stress profiles were identified using GCS, blood lactate, oxygen saturation, serum creatinine, glucose, base excess, pH, arterial partial pressure of carbon dioxide, and body temperature. Notably, compared to a cluster with “severe” GCS and a normal metabolic profile, the prognosis was poorer for a cluster with “moderate” TBI (by traditional categorization) and a disordered metabolic profile. The addition of cluster labels for both the prognosis of a poor outcome and mortality (both P<0.001) significantly improved the prognostic accuracy of the IMPACT (International Mission for Prognosis and Analysis of Clinical Trials in TBI) extended model. By using probabilistic unsupervised clustering, 6 stable and clinically different TBI endotypes were found. A profile of metabolic dysregulation was discovered to be a crucial differentiating factor that was both biologically plausible and associated with outcome, in addition to the presentation of neurology. The addition of metabolic stress parameters in the TBI categories was supported by their findings. These data-driven clusters of TBI endotypes warrant further investigation to find specialized therapy methods to enhance care.