Predicting dry weight change in Hemodialysis patients using machine learning

Machine 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 Machin...

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Bibliographic Details
Published in:	BMC nephrology Vol. 24; no. 1; p. 196
Main Authors:	Inoue, Hiroko, Oya, Megumi, Aizawa, Masashi, Wagatsuma, Kyogo, Kamimae, Masatomo, Kashiwagi, Yusuke, Ishii, Masayoshi, Wakabayashi, Hanae, Fujii, Takayuki, Suzuki, Satoshi, Hattori, Noriyuki, Tatsumoto, Narihito, Kawakami, Eiryo, Asanuma, Katsuhiko
Format:	Journal Article
Language:	English
Published:	England BioMed Central Ltd 29-06-2023 BioMed Central BMC
Subjects:	Algorithms Analysis Anemia Asian Blood Pressure Blood tests Body Weight C-reactive protein Complications and side effects Datasets Decision making Decision trees Dialysate Dry Weight Edema Electronic medical records Evaluation Heart failure Hemodialysis Humans Hypotension Importance analysis Laboratories Learning algorithms Machine Learning Medical records Minority & ethnic groups Mortality Nephrology Patient outcomes Patients Performance evaluation Pleural effusion Random Forest classifier Renal Dialysis Serum levels Variables Weight Japan Random Forest classifier Dry Weight Importance analysis Hemodialysis Machine learning
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Summary:	Machine 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. All 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. The 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. The 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.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1471-2369 1471-2369
DOI:	10.1186/s12882-023-03248-5