Research article summary:

Prediction of single-pool Kt/v based on clinical and hemodialysis variables using multilinear regression, tree-based modeling, and artificial neural networks.

Abstract Extract:
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), ... (Full abstract text below)

Published 2003Jun in Journal: Artif Organs (Language : eng)

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1. Artif Organs. 2003 Jun;27(6):544-54

Prediction of single-pool Kt/v based on clinical and hemodialysis variables using multilinear regression, tree-based modeling, and artificial neural networks.

Goldfarb-Rumyantzev A, Schwenk MH, Liu S, Charytan C, Spinowitz BS

Division of Nephrology and Hypertension, University of Utah School of Medicine, Salt Lake City, Utah, USA.

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.

PMID : 12780509 [PubMed - Indexed for MEDLINE]

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Full Author Information

First Name	LastName	Initials
Alexander	Goldfarb-Rumyantzev	A
Michael H	Schwenk	MH
Samuel	Liu	S
Chaim	Charytan	C
Bruce S	Spinowitz	BS

Affiliation: Division of Nephrology and Hypertension, University of Utah School of Medicine, Salt Lake City, Utah, USA.

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