Development and validation of a machine learning model to predict time to renal replacement therapy in patients with chronic kidney disease.

ABSTRACT:

Background

Predicting time to renal replacement therapy (RRT) is important in patients at high risk for end-stage kidney disease. We developed and validated machine learning models for predicting the time to RRT and compared its accuracy with conventional prediction methods that uses the rate of estimated glomerular filtration rate (eGFR) decline.

Methods

Data of adult chronic kidney disease (CKD) patients who underwent hemodialysis at Oita University Hospital from April 2016 to March 2021 were extracted from electronic medical records (N = 135). A new machine learning predictor was compared with the established prediction method that uses the eGFR decline rate and the accuracy of the prediction models was determined using the coefficient of determination (R²). Th

SUBMITTER: Okita J 

PROVIDER: S-EPMC10943785 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature