A validated multivariable machine learning model to predict cardio-kidney risk in diabetic kidney disease.

ABSTRACT:

Background

Individuals with diabetic kidney disease (DKD) often suffer cardiac and kidney events. We sought to develop an accurate means by which to stratify risk in DKD.

Methods

Clinical variables and biomarkers were evaluated for their ability to predict the adjudicated primary composite endpoint of CREDENCE (Canagliflozin and Renal Events in Diabetes with Established Nephropathy Clinical Evaluation) by 3 years. Using machine learning techniques, a parsimonious risk algorithm was developed.

Results

The final model included age, body-mass index, systolic blood pressure, and concentrations of N-terminal pro-B type natriuretic peptide, high sensitivity cardiac troponin T, insulin-like growth factor binding protein-7 and growth differentiation factor-15. The model had a

SUBMITTER: Januzzi JLJ 

PROVIDER: S-EPMC12082972 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature