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Estimating Resting Energy Expenditure of Patients on Dialysis: Development and Validation of a Predictive Equation

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Journal Nutrition
Date 2019 Jul 30
PMID 31357136
Citations 3
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Abstract

Objectives: The aims of this study were to develop and validate a resting energy expenditure (REE) predictive equation in a cohort of patients on dialysis and to test the accuracy of two previously developed specific equations to estimate REE of these patients.

Methods: A database with REE measured by indirect calorimetry (IC) of 189 patients on hemodialysis and peritoneal dialysis was used to develop and validate the new equation. The sample including only patients on hemodialysis (n = 131) was used to test the accuracy of the specific REE dialysis equations by Vilar and Byham-Gray.

Results: Multiple regression analysis generated two equations: REE (kcal/d) = 957.02 - 8.08 × age + 11.07 × body weight + 136.4 (if men) (R = 0.515) (1) REE (kcal/d) = 624.6-4.8 × age + 20.6 × fat-free, ass-fat-free mass-8.65 (if men) (R = 0.512) (2) In the validation group, REE by both equations did not differ from the REE measured by IC. No bias was found in the Bland-Altman analysis and the intraclass correlation coefficient and P20 test showed good reliability with measured REE. Vilar's equation overestimated REE; whereas REE generated by Byham-Gray's equation did not differ from measured REE. Proportional and systematic biases were significant for both equations.

Conclusions: The new equations developed showed good accuracy and can be valuable to estimate energy needs of patients on dialysis. Byham-Gray's and Vilar's equations presented low to moderate performance to estimate REE of the patients on dialysis.

Citing Articles

Equations for estimating resting energy expenditure in patients on peritoneal dialysis.

Xu X, Abi N, Yang Z, Ma T, Zhang N, Zheng Y Clin Kidney J. 2025; 18(2):sfaf004.

PMID: 39991651 PMC: 11842989. DOI: 10.1093/ckj/sfaf004.


Agreement Between Resting Energy Expenditure Predictive Formulas and Indirect Calorimetry in Non-Dialysis Dependent Chronic Kidney Disease.

de Oliveira M, Bufarah M, Oliveira R, de Goes C, Balbi A Diagnostics (Basel). 2024; 14(22).

PMID: 39594269 PMC: 11592986. DOI: 10.3390/diagnostics14222603.


Machine learning models using non-linear techniques improve the prediction of resting energy expenditure in individuals receiving hemodialysis.

Bailey A, Eltawil M, Gohel S, Byham-Gray L Ann Med. 2023; 55(2):2238182.

PMID: 37505893 PMC: 10392315. DOI: 10.1080/07853890.2023.2238182.


Current methods for developing predictive energy equations in maintenance dialysis are imprecise.

Bailey A, Brody R, Sackey J, Parrott J, Peters E, Byham-Gray L Ann Med. 2022; 54(1):909-920.

PMID: 35356849 PMC: 8979515. DOI: 10.1080/07853890.2022.2057581.