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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.


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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Copyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher or, in the case of photocopying, direct payment of a specified fee to the Copyright Clearance Center.
Drug Dosage: The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in goverment regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any changes in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug.
Disclaimer: The statements, opinions and data contained in this publication are solely those of the individual authors and contributors and not of the publishers and the editor(s). The appearance of advertisements or/and product references in the publication is not a warranty, endorsement, or approval of the products or services advertised or of their effectiveness, quality or safety. The publisher and the editor(s) disclaim responsibility for any injury to persons or property resulting from any ideas, methods, instructions or products referred to in the content or advertisements.

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