Prediction of dialysis adequacy using data-driven machine learning algorithms.

ABSTRACT:

Background

Adequate delivery of hemodialysis (HD), measured by the spKt/V derived from urea reduction, is an important determinant of clinical outcomes in chronic hemodialysis patients. However, the need for pre- and postdialysis blood samples prevented the assessment of spKt/V in every session.

Methods

This retrospective single-center study was performed on end-stage renal disease (ESKD) patients aged ≥ 18 years who received standard thrice-weekly chronic HD therapy. Eighty-seven variables, including general, intradialytic, and laboratory variables, were collected from the medical records for analysis. Five steps of preprocessing procedure were deployed to select only the most relevant variables. Six binary classification models were developed to predict whether spKt/V was

SUBMITTER: Liu YC 

PROVIDER: S-EPMC11556278 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature