A novel approach to dry weight adjustments for dialysis patients using machine learning.

Abstract

Background and aimsKnowledge of the proper dry weight plays a critical role in the efficiency of dialysis and the survival of hemodialysis patients. Recently, bioimpedance spectroscopy(BIS) has been widely used for set dry weight in hemodialysis patients. However, BIS is often misrepresented in clinical healthy weight. In this study, we tried to predict the clinically proper dry weight (DWCP) using machine learning for patient's clinical information including BIS. We then analyze the factors that influence the prediction of the clinical dry weight.MethodsAs a retrospective, single center study, data of 1672 hemodialysis patients were reviewed. DWCP data were collected when the dry weight was measured using the BIS (DWBIS). The gap between the two (GapDW) was calculated and then grouped and analyzed based on gaps of 1 kg and 2 kg.ResultsBased on the gap between DWBIS and DWCP, 972, 303, and 384 patients were placed in groups with gaps of ConclusionsMachine learning made it slightly easier to predict DWCP based on DWBIS under limited conditions and gave better insights into predicting DWCP. Malnutrition-related factors and ECW were important in reflecting the differences between DWBIS and DWCP.