Recommendations Summary
CKD: Nutrition Assessment: Energy Requirements (2020)
Click here to see the explanation of recommendation ratings (Strong, Fair, Weak, Consensus, Insufficient Evidence) and labels (Imperative or Conditional). To see more detail on the evidence from which the following recommendations were drawn, use the hyperlinks in the Supporting Evidence Section below.
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Recommendation(s)
CKD: Assessment of Resting Energy Expenditure
In adults with CKD 1-5D or posttransplantation, it is reasonable to use indirect calorimetry to measure resting energy expenditure when feasible and indicated, as it remains the gold standard for determining resting energy expenditure (OPINION).
Rating: Consensus
ConditionalCKD: Resting Energy Expenditure Equations
In adults with CKD 5D who are metabolically stable, we suggest that in the absence of indirect calorimetry, disease-specific predictive energy equations may be used to estimate resting energy expenditure as they include factors that may influence the metabolic rate in this population (2C).
Rating: Weak
Conditional-
Risks/Harms of Implementing This Recommendation
There are no potential risks or harms related to these recommendations.
Patients should be monitored routinely to assess whether energy requirements are being met satisfactorily. Changes in nutritional status should be treated and the energy prescription modified accordingly.
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Conditions of Application
Special Discussions
Among patients with stage 5 CKD on MHD or PD, there are several factors that may influence energy expenditure beyond the traditional determinants (age, sex, and fat-free mass), such as hyperparathyroidism, hyperglycemia, and chronic inflammation that should be considered into the overall energy prescription. Energy needs will be variable depending on the health status of the patient (e.g., acutely ill versus chronically managed) as well as overall health goals (e.g., weight maintenance, repletion or loss). Energy needs may be different depending on the stage of CKD and its respective treatment (dialysis versus transplantation). In the context of these recommendations, “metabolically stable” indicates absence of any active inflammatory or infectious diseases; no hospitalization within two weeks; absence of poorly controlled diabetes and consumptive diseases such as cancer; absence of antibiotics or immunosuppressive medications; and no significant short-term loss of body weight.
Implementation Considerations
- The RDN should consider a number of factors when determining the energy requirements for adults diagnosed with CKD, and these include the patient’s overall health status, CKD diagnosis and associated therapies, level of physical activity, age, gender, weight status, disease-specific determinants, metabolic stressors, and treatment goals.
- Disease specific equations, such as the Maintenance Hemodialysis Equation, should be used when estimating energy requirements for the CKD population.
- Thermal effects of food may be decreased in individuals who are non-dialyzed compared to dialyzed due to to lower protein intake.
Monitoring and Evaluation
Patients should be monitored routinely to assess whether energy requirements are being met satisfactorily. Changes in nutritional status should be treated and the energy prescription modified accordingly.
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Potential Costs Associated with Application
There are no costs associated with these recommendations.
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Recommendation Narrative
Achieving energy balance is critical in persons diagnosed with CKD so that protein-energy malnutrition and wasting can be prevented or treated in susceptible persons. Thus, obtaining reliable data regarding dietary energy intake as well as having a valid measure for energy expenditure is paramount.
Indirect calorimetry remains as the best practice measure for determining resting energy expenditure (REE) in adults diagnosed with CKD stages 1-5, including those receiving renal replacement therapies such as MHD, PD or post-transplant. More research is needed to demonstrate whether handheld indirect calorimetric devices may be a suitable alternative in this population.
In the absence of indirect calorimetry, there are over 200 predictive energy equations available that may be able to estimate REE in patients diagnosed with CKD. Several have been shown to either over- or under-estimate REE in earlier stages of CKD as well as those patients treated with maintenance dialysis. There have been several cross-sectional studies that suggest that the energy requirements of patients with earlier stages of CKD may not be substantially different than healthy adults, but the evidence is limited. Recent research has shown that predictive energy equations specifically designed for patients with CKD on maintenance dialysis have lower bias and greater precision.
Even the best predictive models designed for CKD do not account for the contribution of physical activity or structured exercise. Reliance on current estimates for physical activity may not determine total energy requirements accurately in this population.
Detailed Justification
There were six studies which tested REE equations in CKD patients and compared them to a reference standard of indirect calorimetry (Byham-Gray et al 2014, Dias Rodrigues et al 2014, Kamimura et al 2011, Lee et al 2008, Neyra et al 2003, Vilar et al2014). Two of the six studies used indirect calorimetry data to derive a disease-specific equation (Byham-Gray et al 2014, Vilar et al2014). The .Harris-Benedict equation over-estimated REE in four studies across the spectrum of CKD; e.g., Dias Rodrigues, et al (MHD), Kamimura, et al (non-dialyzed, MHD and PD), Lee, et al. (CAPD ) and Neyra, et al (CRF, MHD and PD), but the Harris-Benedict equation underestimated REE in MHD participants in Vilar, et al (MHD). Similarly, the Schofield equation over-estimated REE in Dias Rodrigues, et al. (MHD) and Kamimura, et al (non-dialyzed, MHD and PD), but under-estimated REE in Vilar, et al (MHD). Byham-Gray, et al demonstrated that the Maintenance Hemodialysis Equation more accurately predicted REE than the Mifflin-St. Joer equation. In Vilar, et al, they too found that their created equation for REE was best predictor of REE when compared to traditional predictive energy equations. Generally, agreement between equations and methods was low-to-moderate. -
Recommendation Strength Rationale
The evidence supporting these recommendation statements is based on Grade III/Grade C evidence and Consensus/expert opinion.
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Minority Opinions
Consensus reached.
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Risks/Harms of Implementing This Recommendation
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Supporting Evidence
The recommendations were created from the evidence analysis on the following questions. To see detail of the evidence analysis, click the blue hyperlinks below (recommendations rated consensus will not have supporting evidence linked).
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References
Byham-Gray L, Parrott J, Ho W, Sundell M, Ikizler T. Development of a predictive energy equation for maintenance hemodialysis patients: a pilot study. Journal of Renal Nutrition : the official journal of the Council on Renal Nutrition of the National Kidney Foundation 2014; 24:32-41
Dias Rodrigues JC, Lamarca F, de Oliveira CL, Cuppari L, Lourenco RA, Avesani CM. Agreement between prediction equations and indirect calorimetry to estimate resting expenditure in elderly patients on hemodialysis. e-SPEN Journal 2014; 9:e91-e96
Kamimura M, Avesani C, Bazanelli A, Baria F, Draibe S, Cuppari L. Are prediction equations reliable for estimating resting energy expenditure in chronic kidney disease patients?. Nephrology, Dialysis, Transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association 2011; 26:544-50
Lee S, Kim H, Kwon H, Son S, Song J, Kim M. Agreements between indirect calorimetry and prediction equations of resting energy expenditure in end-stage renal disease patients on continuous ambulatory peritoneal dialysis. Yonsei Medical Journal 2008; 49:255-64
Neyra R, Chen K, Sun M, Shyr Y, Hakim R, Ikizler T. Increased resting energy expenditure in patients with end-stage renal disease. JPEN. Journal of Parenteral and Enteral Nutrition 2003; 27:36-42
Vilar E, Machado A, Garrett A, Kozarski R, Wellsted D, Farrington K. 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 2014; 24:243-51
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References