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

Abstract

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 via similar methods in different demographic samples; the Maintenance Haemodialysis Equation (MHDE REE), Vilar et al. Equation (Vilar REE) and the Fernandes et al. Equation (Cuppari REE). We compared them in a US cohort and assessed precision relative to measured REE (mREE) from indirect calorimetry. Because of expected imprecision at the extremes of the weight distribution, we also assessed the PEEs stratified by body mass index (BMI) subgroups. Methods This analysis comprised of 113 individuals from the Rutgers Nutrition and Kidney Database. Estimated REE (eREE) was calculated for each PEE, and agreement with mREE was set at > 50% of values within the limits of ±10%. Reliability and accuracy were determined using intraclass correlation (ICC) and a Bland Altman plot, which analysed the percentage difference of eREE form mREE. Results Participants were 58.4% male and 81.4% African American. Mean age was 55.8 ± 12.2 years, and the median BMI was 28.9 (IQR = 25.3 − 34.4) kg/m 2 . The MHDE REE achieved 58.4% of values within ±10% from mREE; Cuppari REE achieved 47.8% and Vilar REE achieved 46.0% agreement. Reliability was good for the MHDE REE (ICC = 0.826) and Cuppari REE (ICC = 0.801), and moderate for the Vilar REE (ICC = 0.642) ( p < .001 for all). The equations performed poorly at the lowest and highest BMI categories. Conclusion Dialysis-specific energy equations showed variable accuracy. When categorized by BMI, the equations performed poorly at the extremes, where individuals are most vulnerable. Innovation is needed to understand these variances and correct the imprecision in PEEs for clinical practice. KEY MESSAGES Potentially impacting over millions of patients worldwide, our long-term goal is to understand energy expenditure (EE) across the spectrum of CKD (stages 1–5) in adults and children being treated with dialysis or transplantation, with the intent of providing tools for the health professional that will improve the delivery of quality care. Our research has identified and focussed on disease-specific factors which account for 60% of the variance in predicting EE in patients on MHD, but significant gaps remain. Thus, our central hypotheses are that (1) there are unique disease-specific determinants of EE and (2) prediction of EE for individuals diagnosed with CKD can be vastly improved with a model that combines these factors with more sophisticated approaches.

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

“…To date, dialysis-specific EEs have improved predictive accuracy by achieving around 60% precision (± 10% of zero difference from mREE) when tested in samples of people with similar characteristics to the development group [ 20–25 ]. However, our group has demonstrated that disease-specific EEs may not perform well when transferred to different geographical or demographic samples, or to those people with outlying characteristics, such as very low or high body mass index (BMI) [ 27 ]. Arguably, such individuals may be the most vulnerable.…”

“…The Rutgers Nutrition and Kidney Database (RNKD) is a large renal database in the USA, containing over 600 clinical and demographic variables gathered for 210 hemodialysis patients over 4 separate studies undertaken between 2012 and 2018 [ 27 ]. Using this database, the primary objective of this pilot study was to ascertain if a machine learning approach alone can generate a more precise estimation of REE than previous statistical methods using linear and logistical regression only [ 20–25 ].…”

An updated clinical prediction model of protein-energy wasting for hemodialysis patients

“…CKD progresses to a terminal stage termed as end-stage renal disease (ESRD), requiring renal replacement therapy (hemodialysis, peritoneal dialysis, and kidney transplantation). According to the Renal Data System, the number of patients with ESRD requiring renal replacement therapy is increasing year by year in the United States, reaching nearly 7,50,000 in 2019 ( 1 ). Sixty-three percent of these individuals receive maintenance hemodialysis (MHD) ( 1 ).…”

“…According to the Renal Data System, the number of patients with ESRD requiring renal replacement therapy is increasing year by year in the United States, reaching nearly 7,50,000 in 2019 ( 1 ). Sixty-three percent of these individuals receive maintenance hemodialysis (MHD) ( 1 ). Despite improving survival rates, only 57% of MHD patients are still alive 3 years after their first treatment ( 1 ).…”

“…Sixty-three percent of these individuals receive maintenance hemodialysis (MHD) ( 1 ). Despite improving survival rates, only 57% of MHD patients are still alive 3 years after their first treatment ( 1 ). The main reasons for such a high-mortality rate are cardiovascular diseases ( 2 ).…”