Resting energy expenditure in hemodialysis patients: which prediction equation estimates the best?

In chronic kidney disease (CKD) there are many metabolic changes that can influence resting energy expenditure (REE). Protein energy wasting is highly prevalent in hemodialysis patients. Estimating REE is the first step in the process to advise an adequate nutritional therapy. There are many predict...

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Main Authors: Trudeke (G) I. Struijk-Wielinga, Peter J.M. Weijs
Format: Article
Language:English
Published: The Korean Society of Nephrology 2012-06-01
Series:Kidney Research and Clinical Practice
Online Access:http://www.sciencedirect.com/science/article/pii/S2211913212005931
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spelling doaj-97a65affc8cf47378e4d69702b24b8502020-11-25T02:26:21ZengThe Korean Society of NephrologyKidney Research and Clinical Practice2211-91322012-06-01312A7610.1016/j.krcp.2012.04.560Resting energy expenditure in hemodialysis patients: which prediction equation estimates the best?Trudeke (G) I. Struijk-WielingaPeter J.M. WeijsIn chronic kidney disease (CKD) there are many metabolic changes that can influence resting energy expenditure (REE). Protein energy wasting is highly prevalent in hemodialysis patients. Estimating REE is the first step in the process to advise an adequate nutritional therapy. There are many prediction equations for REE. The purpose of this study is to determine which equation predicts REE in a reliable way. In the VU University Medical Centre 56 hemodialysis patients (33 men), mean age 56±14.8 year, BMI (kg/m2) 27.0±6 indirect calorimetry was performed. Measured REE was compared with sixteen different prediction equations based on weight, height, sex, and/or age. The Root Mean Squared prediction Error (RMSE) are presented as well as the average difference between estimation and measurement (bias). 90–110% adequacy of measured REE was accepted as accurate prediction. The measured REE was 1526±299 kcal/d (mean±SD). The percentage accurate predictions, percentage bias and RMSE were: for original Harris Benedict equation (1919), Schofield equation based on weight and height and Cole equation respectively 48%, 50%, 57%; bias: +2.9%, +4.4%, -0.5%; RMSE: 288, 286,271 kcal/d. In this study Coles prediction equations provide an acceptable estimate of resting energy expenditure of HD patients. Further insight into explaining factors is warranted.http://www.sciencedirect.com/science/article/pii/S2211913212005931
collection DOAJ
language English
format Article
sources DOAJ
author Trudeke (G) I. Struijk-Wielinga
Peter J.M. Weijs
spellingShingle Trudeke (G) I. Struijk-Wielinga
Peter J.M. Weijs
Resting energy expenditure in hemodialysis patients: which prediction equation estimates the best?
Kidney Research and Clinical Practice
author_facet Trudeke (G) I. Struijk-Wielinga
Peter J.M. Weijs
author_sort Trudeke (G) I. Struijk-Wielinga
title Resting energy expenditure in hemodialysis patients: which prediction equation estimates the best?
title_short Resting energy expenditure in hemodialysis patients: which prediction equation estimates the best?
title_full Resting energy expenditure in hemodialysis patients: which prediction equation estimates the best?
title_fullStr Resting energy expenditure in hemodialysis patients: which prediction equation estimates the best?
title_full_unstemmed Resting energy expenditure in hemodialysis patients: which prediction equation estimates the best?
title_sort resting energy expenditure in hemodialysis patients: which prediction equation estimates the best?
publisher The Korean Society of Nephrology
series Kidney Research and Clinical Practice
issn 2211-9132
publishDate 2012-06-01
description In chronic kidney disease (CKD) there are many metabolic changes that can influence resting energy expenditure (REE). Protein energy wasting is highly prevalent in hemodialysis patients. Estimating REE is the first step in the process to advise an adequate nutritional therapy. There are many prediction equations for REE. The purpose of this study is to determine which equation predicts REE in a reliable way. In the VU University Medical Centre 56 hemodialysis patients (33 men), mean age 56±14.8 year, BMI (kg/m2) 27.0±6 indirect calorimetry was performed. Measured REE was compared with sixteen different prediction equations based on weight, height, sex, and/or age. The Root Mean Squared prediction Error (RMSE) are presented as well as the average difference between estimation and measurement (bias). 90–110% adequacy of measured REE was accepted as accurate prediction. The measured REE was 1526±299 kcal/d (mean±SD). The percentage accurate predictions, percentage bias and RMSE were: for original Harris Benedict equation (1919), Schofield equation based on weight and height and Cole equation respectively 48%, 50%, 57%; bias: +2.9%, +4.4%, -0.5%; RMSE: 288, 286,271 kcal/d. In this study Coles prediction equations provide an acceptable estimate of resting energy expenditure of HD patients. Further insight into explaining factors is warranted.
url http://www.sciencedirect.com/science/article/pii/S2211913212005931
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