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Vol. 25, No. 5, 2005
Issue release date: September–October 2005
Section title: Original Report: Patient-Oriented, Translational Research
Am J Nephrol 2005;25:507–513
(DOI:10.1159/000088279)

Applying an Artificial Neural Network to Predict Total Body Water in Hemodialysis Patients

Chiu J.-S. · Chong C.-F. · Lin Y.-F. · Wu C.-C. · Wang Y.-F. · Li Y.-C.
aDepartment of Nuclear Medicine, Buddhist Dalin Tzu Chi General Hospital, Chiayi County; bSchool of Medicine, Fu Jen Catholic University, Taipei County; cDivision of Nephrology, Department of Internal Medicine, Tri-Service General Hospital, and dGraduate Institute of Medical Informatics, Wanfang Hospital, Taipei Medical University, Taipei City, Taiwan

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Article / Publication Details

First-Page Preview
Abstract of Original Report: Patient-Oriented, Translational Research

Received: 4/21/2005
Accepted: 7/28/2005
Published online: 10/12/2005

Number of Print Pages: 7
Number of Figures: 2
Number of Tables: 3

ISSN: 0250-8095 (Print)
eISSN: 1421-9670 (Online)

For additional information: http://www.karger.com/AJN

Abstract

Background: Estimating total body water (TBW) is crucial in determining dry weight and dialytic dose for hemodialysis patients. Several anthropometric equations have been used to predict TBW, but a more accurate method is needed. We developed an artificial neural network (ANN) to predict TBW in hemodialysis patients. Methods: Demographic data, anthropometric measurements, and multifrequency bioelectrical impedance analysis (MF-BIA) were investigated in 54 patients. TBW measured by MF-BIA (TBW-BIA) was the reference. The predictive value of TBW based on ANN and five anthropometric equations (58% of actual body weight, Watson formula, Hume formula, Chertow formula, and Lee formula) was evaluated. Results: Predictive TBW values derived from anthropometric equations were significantly higher than TBW-BIA (31.341 ± 6.033 liters). The only non-significant difference was between TBW-ANN (31.468 ± 5.301 liters) and TBW-BIA (p = 0.639). ANN had the strongest Pearson’s correlation coefficient (0.911) and smallest root mean square error (2.480); its peak centered most closely to zero with the shortest tails in an empirical cumulative distribution plot when compared with the other five equations. Conclusion: ANN could surpass traditional anthropometric equations and serve as a feasible alternative method of TBW estimation for chronic hemodialysis patients.


  

Author Contacts

Yu-Chuan Li, MD, PhD
Graduate Institute of Medical Informatics, Wanfang Hospital, Taipei Medical University
No. 250, Wusing St., Sinyi District
Taipei City 110 (Taiwan)
Tel. +886 2 23776730/ext 110, Fax +886 2 27339049, E-Mail jack@tmu.edu.tw

  

Article Information

Received: April 21, 2005
Accepted: July 28, 2005
Published online: September 9, 2005
Number of Print Pages : 7
Number of Figures : 2, Number of Tables : 3, Number of References : 32

  

Publication Details

American Journal of Nephrology

Vol. 25, No. 5, Year 2005 (Cover Date: September-October 2005)

Journal Editor: Bakris, G. (Chicago, Ill.)
ISSN: 0250–8095 (print), 1421–9670 (Online)

For additional information: http://www.karger.com/ajn


Article / Publication Details

First-Page Preview
Abstract of Original Report: Patient-Oriented, Translational Research

Received: 4/21/2005
Accepted: 7/28/2005
Published online: 10/12/2005

Number of Print Pages: 7
Number of Figures: 2
Number of Tables: 3

ISSN: 0250-8095 (Print)
eISSN: 1421-9670 (Online)

For additional information: http://www.karger.com/AJN


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