Dynamic TyG trajectories cumulative TyG burden are associated with in-hospital mortality in acute brain injury: a multicenter interpretable machine-learning analysis

Background Dynamic metabolic changes may influence outcomes after acute brain injury (ABI), but most ICU studies use only a single triglyceride–glucose (TyG) value. We examined whether ICU TyG trajectories and a cumulative TyG burden provide time-sensitive prognostic information and can be embedded in an interpretable mortality model. Methods Adults with ABI from three ICU databases (NSICU, MIMIC-IV, eICU) were retrospectively analyzed. TyG trajectories were derived from serial ICU measurements, cumulative exposure was summarized as prespecified threshold-based mean area under the curve (TBM), and in-hospital mortality was evaluated with 7-day time-stratified Cox models. A machine-learning model including TyG trajectory, TBM, and routinely available clinical variables was trained in NSICU and validated in the pooled external cohort. Results Among 4,760 admissions, three trajectories were identified—low–slightly increasing (LSI), moderate–increasing (MI), and persistently high (PH). Mortality did not differ across trajectories during days 0–7, but after day 7 both MI (HR 1.48, 95% CI 1.18–1.86; P < 0.001) and PH (HR 1.51, 95% CI 1.17–1.93; P = 0.001) showed higher in-hospital mortality than LSI. TBM showed a parallel positive association; TBM8p7 remained significant in fully adjusted models (HR 1.42, 95% CI 1.18–1.70; P < 0.001). ExtraTrees was selected for its consistent internal and external validation performance, and model interpretability analyses placed TyG trajectory and TBM8p7 among the next most important predictors alongside SOFA score and vasopressor use. Conclusion In ICU-treated ABI, TyG is better modeled as a time-aware exposure: trajectory differences become prognostically relevant only after the first week, whereas cumulative TBM8p7 shows a graded, independent association with mortality. Both metrics add risk information beyond conventional severity indicators and can be integrated into an interpretable, externally tested model.