ArticlePloS one2021
A novel approach to dry weight adjustments for dialysis patients using machine learning.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06279156 (Optimal Frequency of Total Body Water Measurements by Bioelectrical Impedance Analysis to Prevent Intradialytic Hypotension), which is not on this map. Cited by 11 papers.
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The trial behind it
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Optimal Frequency of Total Body Water Measurements by Bioelectrical Impedance Analysis to Prevent Intradialytic Hypotension
Who cites it
11 citing papers in PubMed.
- Oral creatine in hemodialysis patients increases physical functional capacity and muscle mass, an open label study.PloS one · 2025Trial
- From assistant to collaborator: A systematic review of the evolution of artificial intelligence in end-stage renal disease care and management.PLOS digital health · 2026Article
- Transforming hemodialysis care: a tripartite collaboration model among medical staff, AI agents, and robots.Clinical and experimental nephrology · 2026Review
- Comparison of machine learning models for hemoglobin prediction in patients undergoing maintenance hemodialysis.Frontiers in molecular biosciences · 2026Article
- Abdominal subcutaneous fat area can predict 2-year survival in patients with end-stage renal disease initiating hemodialysis.PloS one · 2025Article
- Patient handoff from the perspective of nursing teams in hemodialysis units.Revista brasileira de enfermagem · 2025Article
- Machine learning models to predict post-dialysis blood pressure in children and young adults on maintenance hemodialysis.Scientific reports · 2023Article
- Predicting dry weight change in Hemodialysis patients using machine learning.BMC nephrology · 2023Article
- The Implementation of a Modified Fluid Assessment Tool to Improve the Clinical Assessment, Detection, and Management of Blood Pressure Control and Fluid Alterations Among Hemodialysis Patients.International journal of nephrology and renovascular disease · 2023Article
- Artificial intelligence and digital health for volume maintenance in hemodialysis patients.Hemodialysis international. International Symposium on Home Hemodialysis · 2022Review
- Consistency of the estimated target weights and ECW/TBW using BIA after hemodialysis in patients between standing and lying-down positions.BMC nephrology · 2022Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
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 <1 kg, ≧1kg and <2 kg, and ≧2 kg, respectively. For less than 1 kg and 2 kg of GapDW, It can be seen that the average accuracies for the two groups are 83% and 72%, respectively, in usign XGBoost machine learning. As GapDW increases, it is more difficult to predict the target property. As GapDW increase, the mean values of hemoglobin, total protein, serum albumin, creatinine, phosphorus, potassium, and the fat tissue index tended to decrease. However, the height, total body water, extracellular water (ECW), and ECW to intracellular water ratio tended to increase.
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.
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