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

Hae Ri Kim, Hong Jin Bae, Jae Wan Jeon, Young Rok Ham, Ki Ryang Na, Kang Wook Lee, Yun Kyong Hyon and Dae Eun Choi

PLOS ONE, 2021, vol. 16, issue 4, 1-12

Abstract: Background and aims: Knowledge 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. Methods: As 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. Results: Based on the gap between DWBIS and DWCP, 972, 303, and 384 patients were placed in groups with gaps of

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0250467

DOI: 10.1371/journal.pone.0250467

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