Laura Byham-Gray1, J Scott Parrott2, Wai Yin Ho3, Mary B Sundell4, T Alp Ikizler4. 1. Department of Nutritional Sciences, Graduate Programs in Clinical Nutrition, School of Health Professions, Rutgers University, Stratford & Newark, New Jersey.. Electronic address: laura.byham-gray@shrp.rutgers.edu. 2. Department of Interdisciplinary Studies, School of Health Related Professions, Rutgers University, Newark, New Jersey. 3. Department of Nutritional Sciences, Rutgers University, Newark, New Jersey. 4. Division of Nephrology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee.
Abstract
OBJECTIVE: The study objectives were to explore the predictors of measured resting energy expenditure (mREE) among a sample of maintenance hemodialysis (MHD) patients, to generate a predictive energy equation (MHDE), and to compare such models to another commonly used predictive energy equation in nutritional care, the Mifflin-St. Jeor equation (MSJE). DESIGN AND METHODS: The study was a retrospective, cross-sectional cohort design conducted at the Vanderbilt University Medical Center. Study subjects were adult MHD patients (N = 67). Data collected from several clinical trials were analyzed using Pearson's correlation and multivariate linear regression procedures. Demographic, anthropometric, clinical, and laboratory data were examined as potential predictors of mREE. Limits of agreement between the MHDE and the MSJE were evaluated using Bland-Altman plots. The a priori α was set at P < .05. The main outcome measure was mREE. RESULTS: The mean age of the sample was 47 ± 13 years. Fifty participants (75.6%) were African American, 7.5% were Hispanic, and 73.1% were males. Fat-free mass (FFM), serum albumin (ALB), age, weight, serum creatinine (CR), height, body mass index, sex, high-sensitivity C-reactive protein (CRP), and fat mass (FM) were all significantly (P < .05) correlated with mREE. After screening for multi-collinearity, the best predictive model (MHDE-lean body mass [LBM]) of mREE included (R(2) = 0.489) FFM, ALB, age, and CRP. Two additional models (MHDE-CRP and MHDE-CR) with acceptable predictability (R(2) = 0.460 and R(2) = 0.451) were derived to improve the clinical utility of the developed energy equation (MHDE-LBM). Using Bland-Altman plots, the MHDE over- and underpredicted mREE less often than the MSJE. CONCLUSIONS: Predictive models (MHDE) including selective demographic, clinical, and anthropometric data explained less than 50% variance of mREE but had better precision in determining energy requirements for MHD patients when compared with MSJE. Further research is necessary to improve predictive models of mREE in the MHD population and to test its validity and clinical application.
OBJECTIVE: The study objectives were to explore the predictors of measured resting energy expenditure (mREE) among a sample of maintenance hemodialysis (MHD) patients, to generate a predictive energy equation (MHDE), and to compare such models to another commonly used predictive energy equation in nutritional care, the Mifflin-St. Jeor equation (MSJE). DESIGN AND METHODS: The study was a retrospective, cross-sectional cohort design conducted at the Vanderbilt University Medical Center. Study subjects were adult MHD patients (N = 67). Data collected from several clinical trials were analyzed using Pearson's correlation and multivariate linear regression procedures. Demographic, anthropometric, clinical, and laboratory data were examined as potential predictors of mREE. Limits of agreement between the MHDE and the MSJE were evaluated using Bland-Altman plots. The a priori α was set at P < .05. The main outcome measure was mREE. RESULTS: The mean age of the sample was 47 ± 13 years. Fifty participants (75.6%) were African American, 7.5% were Hispanic, and 73.1% were males. Fat-free mass (FFM), serum albumin (ALB), age, weight, serum creatinine (CR), height, body mass index, sex, high-sensitivity C-reactive protein (CRP), and fat mass (FM) were all significantly (P < .05) correlated with mREE. After screening for multi-collinearity, the best predictive model (MHDE-lean body mass [LBM]) of mREE included (R(2) = 0.489) FFM, ALB, age, and CRP. Two additional models (MHDE-CRP and MHDE-CR) with acceptable predictability (R(2) = 0.460 and R(2) = 0.451) were derived to improve the clinical utility of the developed energy equation (MHDE-LBM). Using Bland-Altman plots, the MHDE over- and underpredicted mREE less often than the MSJE. CONCLUSIONS: Predictive models (MHDE) including selective demographic, clinical, and anthropometric data explained less than 50% variance of mREE but had better precision in determining energy requirements for MHD patients when compared with MSJE. Further research is necessary to improve predictive models of mREE in the MHD population and to test its validity and clinical application.
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