Data Sheet 3_Machine learning–based prognostic prediction and subphenotype identification in trauma-induced coagulopathy using early blood glucose trajectories.xlsx

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.</p>Methods<p>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.</p>Results<p>Six key prognostic variables were identified: red cell distribution width (RDW), white blood cell count, blood glucose, anion gap, lactate, and pH.