Predictive value of early blood glucose trajectory for poor prognosis in ARDS patients.

Abstract

Patients with acute respiratory distress syndrome (ARDS) often develop stress-induced hyperglycemia, and glycemic variability is associated with adverse outcomes. Traditional static glycemic indicators cannot capture dynamic glucose changes, and current ARDS prognostic models lack integration of dynamic glycemic trajectories, leading to insufficient precision in early risk stratification. This study aimed to investigate the association between early glycemic trajectory and 30-day mortality in ARDS patients, while constructing and validating a prognostic prediction model integrating dynamic glycemic features. A total of 8,103 ARDS patients from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database were enrolled as training set, and 158 patients from a single center served as external validation set. Group-based trajectory modeling (GBTM) was used to cluster the blood glucose trajectory within 48 h of admission. Independent prognostic factors were identified using Cox proportional hazards models, and a nomogram model was constructed. And the nomogram was validate using receiver operating characteristic curve, calibration curve and decision curve analysis. Four blood glucose trajectories were identified: G1 (20.01%), G2 (37.43%), G3 (34.64%) and G4 (7.91%). Among them, G2 group with stable and low blood glucose levels had the highest 30-day survival probability, while G4 group with an initial high blood glucose level that decreased sharply and then slightly increased had a significantly higher 30-day death (log-rank P < 0.0001). The nomogram integrating 16 predictors including glucose trajectory, age, respiratory rate, and anion gap achieved good discrimination (AUC = 0.72 in training set, 0.75 in validation set), favorable calibration, and high net clinical benefit. Early blood glucose trajectory is an independent predictor of 30-day mortality in patients with ARDS. The nomogram constructed based on dynamic blood glucose evolution has good predictive efficacy and clinical applicability, and can provide a quantitative tool for the early intervention of high-risk patients.