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Prediction of Single‐Pool Kt/V Based on Clinical and Hemodialysis Variables Using Multilinear Regression, Tree‐Based Modeling, and Artificial Neural Networks
Author(s) -
GoldfarbRumyantzev Alexander,
Schwenk Michael H.,
Liu Samuel,
Charytan Chaim,
Spinowitz Bruce S.
Publication year - 2003
Publication title -
artificial organs
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.684
H-Index - 76
eISSN - 1525-1594
pISSN - 0160-564X
DOI - 10.1046/j.1525-1594.2003.07001.x
Subject(s) - kt/v , hemodialysis , multilinear map , dialysis , artificial neural network , linear regression , regression , regression analysis , mathematics , medicine , statistics , urology , computer science , artificial intelligence , pure mathematics
The impact of clinical and other variables on single‐pool Kt/V (spKt/V) is unclear. The goal of this study was to identify clinical and hemodialysis treatment related predictors of spKt/V and use multilinear regression (LM), tree‐based modeling (TBM), and artificial neural networks (ANN) to predict actual spKt/V. When 602 hemodialysis records were analyzed, spKt/V correlated with urea reduction ratio (URR) (r=0.91) and weakly with other variables. When URR was excluded, both LM and TBM identified normalized protein equivalent of total nitrogen appearance (nPNA), prehemodialysis (HD) and post‐HD weights, blood flow rate, and dialyzer surface area as predictors of spKt/V. LM identified sex, height, dialyzer ultrafiltration coefficient (Kuf), and duration of dialysis, while TBM identified the dialysis nurse code. Prediction algorithms were developed from a “training” dataset, and validated on a separate (“testing”) dataset. Correlation coefficients of predicted spKt/V with measured spKt/V with and without nPNA respectively were 0.745 and 0.679 for LM, 0.6 and 0.512 for TBM, and 0.634 for ANN, which performed better without using nPNA.