Disease-specific predictive formulas for energy expenditure in the dialysis population

Vilar, Enric; Machado, Ashwini; Garrett, Andrew; Kozarski, Robert; Wellsted, David; Farrington, Ken

Abstract

Background Metabolic rate is poorly understood in advanced kidney disease, direct measurement being expensive and time-consuming. Predictive equations for Resting Energy Expenditure (REE) are needed based on simple bedside parameters. Algorithms derived for normal individuals may not be valid in the renal population. We aimed to develop predictive equations for REE specific for the dialysis population. Design 200 subjects on maintenance dialysis underwent a comprehensive metabolic assessment including REE from indirect calorimetry. Parameters predicting REE were identified, regression equations developed, and validated in 20 separate subjects. Results Mean REE was 1658±317 kCal/day (males) and 1380±287 kCal/day (females). Weight and height correlated positively with REE (r2=0.54 and 0.31) and age negatively above 65 years (r2=0.18). The energy cost of a unitary kg of body weight increased non-linearly for lower Body Mass Index. Existing equations derived in normal individuals underestimated REE (bias 50-114kCal/day for three equations). The novel derived equation was: REE(kCal/day)=-2.497∙Age∙Factorage+0.011∙height2.023+83.573∙Weight0.6291+68.171∙Factorsex where Factorage=1 if ≥65 years and zero if <65, Factorsex=1 if male, and zero if female. This algorithm performed at least as well as those developed for normal individuals in terms of limits of agreement and reduced bias. In validation with Bland-Altman technique, bias was not significant for our algorithm (-22±96kCal/day). 95% limits of agreement were +380 to -424 kCal/day. Conclusion Existing equations for REE derived from normal individuals are not valid in the dialysis population. The relatively increased REE in those with low BMI implies the need for higher dialysis doses in this subgroup. This disease-specific algorithm may be useful clinically and as a research tool to predict REE.

Citation

Vilar, E., Machado, A., Garrett, A., Kozarski, R., Wellsted, D., & Farrington, K. (2014). Disease-specific predictive formulas for energy expenditure in the dialysis population. Journal of renal nutrition : the official journal of the Council on Renal Nutrition of the National Kidney Foundation, 24(4), 243-251. https://doi.org/10.1053/j.jrn.2014.03.001

• Online Publication Date: Apr 28, 2014
• Publication Date: 2014-07
• Deposit Date: May 6, 2015
• Publicly Available Date: May 6, 2015
• Journal: Journal of renal nutrition
• Print ISSN: 1051-2276
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 24
• Issue: 4
• Pages: 243-251
• DOI: https://doi.org/10.1053/j.jrn.2014.03.001
• Keywords: Dialysis; Kidney disease; REE
• Public URL: https://hull-repository.worktribe.com/output/373448
• Publisher URL: http://www.sciencedirect.com/science/article/pii/S1051227614000491
• Additional Information: Author's accepted manuscript of article published in: Journal of renal nutrition, 2014, v.24, issue 4 at http://www.sciencedirect.com/science/article/pii/S1051227614000491
• Contract Date: May 6, 2015