From glycemic variability to digital signal biomarker: a prognostic and precision medicine framework for sepsis-associated acute kidney injury.

Sepsis, a condition with substantial global morbidity and mortality, frequently leads to sepsis-associated acute kidney injury (SA-AKI). While glycemic variability (GV) correlates with adverse outcomes in critically ill populations, its prognostic value in SA-AKI remains underexplored. Using the MIMIC-IV database, this large-scale machine learning cohort study examined SA-AKI patients. Restricted cubic spline, Kaplan-Meier analysis, and Cox regression analyses were conducted to evaluate associations between GV, measured by glycemic coefficient of variation (CV) and 28- and 90-day mortality. Subgroup analyses stratified by age, sex, and diabetes status were performed. Prognostic models were developed using Cox proportional hazards (CoxPH), Least absolute shrinkage and selection operator (LASSO), and random survival forests (RSF). Among 12,268 eligible SA-AKI patients, the Boruta algorithm identified glycemic CV as a key prognostic determinant. When stratified by CV quartiles, higher CV quartiles exhibited significantly increased 28-day and 90-day mortality. Subgroup analyses revealed consistent associations except in diabetic patients, where increases in CV showed no correlation with mortality. Machine learning models exhibited strong predictive performance, with 28-day area under the curves (AUCs) of 0.822 (CoxPH), 0.822 (LASSO), and 0.845 (RSF), and 90-day AUCs of 0.819 (CoxPH), 0.820 (LASSO), and 0.837 (RSF). Elevated GV is associated with increased short- and long-term mortality in SA-AKI. Beyond prognostication, these findings position GV as a real-time, modifiable digital biomarker that may underpin a mechanistic Glycemic Oscillation-Induced Renal Injury (GO-RI) Axis. This framework supports future development of machine learning-enabled precision ICU nephrology strategies for dynamic risk stratification and phenotype-specific glycemic modulation in SA-AKI.