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Abstract: PO0867

Dry Weight Adjustments for Hemodialysis Patients Using Machine Learning

Session Information

Category: Dialysis

  • 701 Dialysis: Hemodialysis and Frequent Dialysis

Authors

  • Kim, Hae Ri, Chungnam National University Sejong Hospital, Sejong, Korea (the Republic of)
  • Jeon, Jae wan, Chungnam National University Sejong Hospital, Sejong, Korea (the Republic of)
  • Ham, Youngrok, Chungnam National University, Daejeon, Daejeon, Korea (the Republic of)
  • Na, Kiryang, Chungnam National University, Daejeon, Daejeon, Korea (the Republic of)
  • Lee, Kang Wook, Chungnam National University, Daejeon, Daejeon, Korea (the Republic of)
  • Chang, Yoon-Kyung, Daejeon Saint Mary's Hospital, Daejeon, Daejeon, Korea (the Republic of)
  • Choi, Dae Eun, Chungnam National University, Daejeon, Daejeon, Korea (the Republic of)
Background

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 <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.

Conclusion

Machine 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.