Stress hyperglycemia ratio and machine learning model for prediction of all-cause mortality in critically ill patients with acute kidney injury: A cohort study from MIMIC-IV

Objectives: Acute kidney injury (AKI) is marked by a rapid decline in renal function, often identified by elevated serum creatinine or reduced urine output. Although stress hyperglycemia ratio (SHR) has been linked to adverse outcomes in various conditions, its association with clinical prognosis in AKI patients remains unclear. Methods: This cohort study analyzed data from critically ill patients with AKI extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV, version 3.1) database. The primary outcomes were 28-day and 365-day all-cause mortality, while the secondary outcomes included ICU mortality and in-hospital mortality. The association between SHR and all-cause mortality was explored by Cox proportional hazards regression. The discriminative performance of SHR was evaluated through the Boruta feature selection model, followed by the development of a prognostic prediction model utilizing advanced machine learning Results: 001, Model 3). Upon categorizing SHR into quartiles, individuals in the highest quartile (Q4) faced a substantially elevated risk, with a 101% greater likelihood of 28-day death and a 34% elevated hazard of 365-day death relative to those in the lowest quartile (Q1). Boruta feature selection analysis identified SHR as a significant predictor. Among various predictive models evaluated, the CatBoost classifier exhibited the most robust discriminative performance for 28-day and 365-day mortality, achieving an area under the receiver operating characteristic curve of 0.83 and 0.82, respectively, showing comparable discriminative performance to the other models. Conclusion: The SHR demonstrated a nonlinear association all-cause mortality among critically ill patients AKI, suggesting its potential utility as a reliable prognostic indicator for predicting unfavorable results in AKI patients.