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

Area of Science:

• Nephrology
• Clinical Nutrition
• Metabolic Research

Background:

• Accurate estimation of resting energy expenditure (REE) is vital for nutritional management in patients undergoing maintenance dialysis to prevent protein-energy wasting.
• Predictive energy equations (PEEs) offer a practical approach for calculating REE in this population.
• Existing PEEs, including the Maintenance Haemodialysis Equation (MHDE REE), Vilar REE, and Cuppari REE, were developed using different demographic samples.

Purpose of the Study:

• To compare the precision of three dialysis-specific PEEs (MHDE REE, Vilar REE, Cuppari REE) against measured REE (mREE) in a US cohort.
• To assess the performance of these equations when stratified by body mass index (BMI) subgroups, particularly at the extremes of the weight distribution.

Main Methods:

• Indirect calorimetry was used to measure REE (mREE) in 113 participants from the Rutgers Nutrition and Kidney Database.
• Estimated REE (eREE) was calculated using MHDE REE, Vilar REE, and Cuppari REE.
• Agreement between eREE and mREE was defined as >50% of values within ±10%. Reliability was assessed using intraclass correlation (ICC) and Bland Altman plots.

Main Results:

• The MHDE REE achieved 58.4% agreement with mREE within ±10%, outperforming Cuppari REE (47.8%) and Vilar REE (46.0%).
• Reliability was good for MHDE REE (ICC=0.826) and Cuppari REE (ICC=0.801), and moderate for Vilar REE (ICC=0.642).
• All tested equations demonstrated poor performance at the lowest and highest BMI categories.

Conclusions:

• Dialysis-specific energy equations exhibit variable accuracy, with MHDE REE showing superior performance in the studied US cohort.
• The equations' imprecision at extreme BMI values highlights a significant limitation for clinical application in vulnerable patient subgroups.
• Further innovation is required to develop more accurate PEEs that account for disease-specific factors and improve REE prediction in chronic kidney disease (CKD) patients on dialysis.