Machine-learning-based early prediction of end-stage renal disease in patients with diabetic kidney disease using clinical trials data.

ABSTRACT:

Aim

To predict end-stage renal disease (ESRD) in patients with type 2 diabetes by using machine-learning models with multiple baseline demographic and clinical characteristics.

Materials and methods

In total, 11 789 patients with type 2 diabetes and nephropathy from three clinical trials, RENAAL (n = 1513), IDNT (n = 1715) and ALTITUDE (n = 8561), were used in this study. Eighteen baseline demographic and clinical characteristics were used as predictors to train machine-learning models to predict ESRD (doubling of serum creatinine and/or ESRD). We used the area under the receiver operator curve (AUC) to assess the prediction performance of models and compared this with traditional Cox proportional hazard regression and kidney failure risk equation models.

Results

The

SUBMITTER: Belur Nagaraj S 

PROVIDER: S-EPMC7756814 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature