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

Purpose: Approximately 700,000 people in the USA have chronic kidney disease requiring dialysis. Protein-energy wasting (PEW), a condition of advanced catabolism, contributes to three-year survival rates of 50%. PEW occurs at all levels of Body Mass Index (BMI) but is devastating for those people at the extremes.

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

Purpose: For individuals receiving maintenance dialysis, estimating accurate resting energy expenditure (REE) is essential for achieving energy balance, and preventing protein-energy wasting. Dialysis-specific, predictive energy equations (PEEs) offer a practical way to calculate REE. Three PEEs have been formulated similar methods in different demographic samples; the Maintenance Haemodialysis Equation (MHDE REE), Vilar et al.