Atherogenic index of plasma and risk of acute kidney injury in critically ill patients: a multi-cohort study with machine learning and SHAP analysis.

ABSTRACT: Abnormal lipid metabolism poses a risk for acute kidney injury (AKI), a prevalent complication among critically ill patients. Early detection and intervention of AKI are essential; however, reliable lipid-derived predictive biomarkers remain understudied. This study aimed to investigate the relationship between the novel lipid biomarker atherogenic index of plasma (AIP) and AKI in critically ill patients and to construct an AIP-integrated early warning model to identify high-risk patients. Patient data were derived from the MIMIC-IV database (training and internal validation sets) and a local hospital cohort (external validation set). Multivariable logistic regression was used to evaluate the association between AIP and AKI. Restricted cubic spline (RCS) regression was utilized to explore

SUBMITTER: Li DM 

PROVIDER: S-EPMC12818314 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature