Machine learning-based prognostic prediction and subphenotype identification in trauma-induced coagulopathy using early blood glucose trajectories.

Background: Trauma-induced coagulopathy (TIC) is characterized by marked heterogeneity, and conventional classification methods have limited ability to capture its dynamic progression, frequently failing to account for inter-individual variability. This study aimed to develop a framework for identifying glucose trajectory phenotypes based on early blood glucose trajectories and to evaluate their association with 28-day mortality. Methods: A retrospective analysis was performed using data from 905 trauma patients in the discovery cohort obtained from the MIMIC-IV database and 260 trauma patients in a validation cohort from Hebei Medical University Third Hospital. Least absolute shrinkage and selection operator regression analysis was applied to identify prognostic variables, followed by the development and comparative evaluation of 12 machine learning models. Group-based trajectory modeling (GBTM) was used to characterize dynamic blood glucose trajectory patterns during the first 7 days following admission, and their associations with clinical outcomes were examined. Results: Six key prognostic variables were identified: red cell distribution width (RDW), white blood cell count, blood glucose, anion gap, lactate, and pH. The logistic classifier model achieved an area under the curve of 0.785 in the discovery cohort and 0.761 in the validation cohort, with acceptable calibration (ECE = 0.119). Among 184 eligible patients in the discovery cohort. GBTM identified three distinct blood glucose trajectory patterns: declining-then-stabilizing, persistently fluctuating, and stabilizing-then-rising. The stabilizing-then-rising trajectory pattern was significantly associated with the highest 28-day mortality ( Conclusion: Application of GBTM to early blood glucose trajectories enables the identification of glucose trajectory phenotypes associated with differential mortality risk. This approach may provide a novel framework for dynamic risk stratification and targeted clinical management.