Predicting dry weight change in Hemodialysis patients using machine learning

BMC Nephrology

Research

Masatomo Kamimae¹  Eiryo Kawakami¹  Megumi Oya¹  Kyogo Wagatsuma¹  Noriyuki Hattori²  Hiroko Inoue³  Masashi Aizawa³  Narihito Tatsumoto³  Yusuke Kashiwagi³  Katsuhiko Asanuma⁴  Hanae Wakabayashi⁴  Masayoshi Ishii⁴  Satoshi Suzuki⁵  Takayuki Fujii⁵ 

[1] Department of Artificial Intelligence Medicine, Graduate School of Medicine, Chiba University, Chuo- ku, Chiba, Japan;Advanced Data Science Project (ADSP), RIKEN Information R&D and Strategy Headquarters, RIKEN, Yokohama, Kanagawa, Japan;Department of Artificial Kidney, Chiba University Hospital, Chuo-ku, Chiba, Japan;Department of Emergency and Critical Care Medicine, Graduate School of Medicine, Chiba University, Chiba, Japan;Department of Nephrology, Graduate School of Medicine, Chiba University, Chuo-ku, Chiba, Japan;Department of Nephrology, Graduate School of Medicine, Chiba University, Chuo-ku, Chiba, Japan;Department of Artificial Kidney, Chiba University Hospital, Chuo-ku, Chiba, Japan;Department of Nephrology, Seirei Sakura Citizen hospital, Sakura, Chiba, Japan;

关键词: Machine learning;    Random Forest classifier;    Importance analysis;    Dry Weight;    Hemodialysis;   

DOI  :  10.1186/s12882-023-03248-5

 received in 2023-04-17, accepted in 2023-06-19,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundMachine Learning has been increasingly used in the medical field, including managing patients undergoing hemodialysis. The random forest classifier is a Machine Learning method that can generate high accuracy and interpretability in the data analysis of various diseases. We attempted to apply Machine Learning to adjust dry weight, the appropriate volume status of patients undergoing hemodialysis, which requires a complex decision-making process considering multiple indicators and the patient’s physical conditions.MethodsAll medical data and 69,375 dialysis records of 314 Asian patients undergoing hemodialysis at a single dialysis center in Japan between July 2018 and April 2020 were collected from the electronic medical record system. Using the random forest classifier, we developed models to predict the probabilities of adjusting the dry weight at each dialysis session.ResultsThe areas under the receiver-operating-characteristic curves of the models for adjusting the dry weight upward and downward were 0.70 and 0.74, respectively. The average probability of upward adjustment of the dry weight had sharp a peak around the actual change over time, while the average probability of downward adjustment of the dry weight formed a gradual peak. Feature importance analysis revealed that median blood pressure decline was a strong predictor for adjusting the dry weight upward. In contrast, elevated serum levels of C-reactive protein and hypoalbuminemia were important indicators for adjusting the dry weight downward.ConclusionsThe random forest classifier should provide a helpful guide to predict the optimal changes to the dry weight with relative accuracy and may be useful in clinical practice.

【 授权许可】

CC BY   
© The Author(s) 2023

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