Prediction of all-cause in-hospital mortality after ICU admission and 1-year mortality after discharge of patients with acute kidney injury.

ABSTRACT: Acute kidney injury (AKI) is a common comorbidity for in-hospital patients. This study aimed to develop dual prediction models for in-hospital and 1-year post-discharge mortality in patients with AKI, with a focus on identifying novel immunological risk factors. 7,937 patients (1,567 in-hospital deaths; 6,370 discharged) were selected from the Medical Information Mart for Intensive Care (MIMIC-IV) database based on serum creatinine and urine output criteria. Forty-two features were selected using least absolute shrinkage and selection operator (LASSO) regression and were used to construct prediction models. eXtreme Gradient Boosting (XGBoost) showed the best performance for in-hospital mortality prediction (training area under the curve (AUC) = 0.833; test AUC = 0.755). Kaplan-Meier curve

SUBMITTER: Zhong L 

PROVIDER: S-EPMC12481539 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature