A simplified prediction model for end-stage kidney disease in patients with diabetes.

ABSTRACT: This study aimed to develop a simplified model for predicting end-stage kidney disease (ESKD) in patients with diabetes. The cohort included 2549 individuals who were followed up at Kyushu University Hospital (Japan) between January 1, 2008 and December 31, 2018. The outcome was a composite of ESKD, defined as an eGFR < 15 mL min^-1 [1.73 m]^-2, dialysis, or renal transplantation. The mean follow-up was 5.6 [Formula: see text] 3.7 years, and ESKD occurred in 176 (6.2%) individuals. Both a machine learning random forest model and a Cox proportional hazard model selected eGFR, proteinuria, hemoglobin A1c, serum albumin levels, and serum bilirubin levels in a descending order as the most important predictors among 20 baseline variables. A model using eGFR, proteinuria and

SUBMITTER: Inoguchi T 

PROVIDER: S-EPMC9304378 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature