Predictive Energy Equations Research Articles

Comparison of a Handheld Indirect Calorimetry Device and Predictive Energy Equations Among Individuals on Maintenance Hemodialysis

Practical methods for determining resting energy expenditure (REE) among individuals on maintenance hemodialysis (MHD) are needed because of the limitations of indirect calorimetry. Two disease-specific predictive energy equations (PEEs) have been developed for this metabolically complex population. The aim of this study was to compare estimated REE (eREE) by PEEs to measured REE (mREE) with a handheld indirect calorimetry device (HICD). A prospective pilot study of adults on MHD (N=40) was conducted at 2 dialysis clinics in Houston and Texas City, Texas. mREE by an HICD was compared with eREE determined by 6 PEEs using Bland-Altman analysis with a band of acceptable agreement of ±10% of the group mean mREE. Paired t-test and the intraclass correlation coefficient were also used to compare the alternate methods of measuring REE. A priori alpha was set at P<.05. The mean (±standard deviation) age was 56.7±12.9years, 52.5% (n=21) were female, and 85% (n=34) were African American. Body mass index (BMI) ranged from 18.1 to 47.1kg/m2, 67.5% were overweight (BMI ≥25kg/m2) and 50% were obese (BMI≥30kg/m2). The Maintenance Hemodialysis Equation-Creatinine version (MHCD-CR) was the most accurate PEE with 52.5% of values within theband of acceptable agreement, followed by the Mifflin-St. Jeor Equation and the Vilar etal. Equation at 45.0% and 42.5%, respectively. When compared with mREE by the HICD, the MHDE-CR was more accurate and precise than other PEEs evaluated; however, this must be interpreted with caution as mREE was consistently lower than eREE from all PEEs. Further research is needed to validate the MHDE-CR and other practical methods for determining REE among individuals on MHD.

Comparison of Subjective Global Assessment and Protein Energy Wasting Score to Nutrition Evaluations Conducted by Registered Dietitian Nutritionists in Identifying Protein Energy Wasting Risk in Maintenance Hemodialysis Patients

To compare the 7-point subjective global assessment (SGA) and the protein energy wasting (PEW) score with nutrition evaluations conducted by registered dietitian nutritionists in identifying PEW risk in stage 5 chronic kidney disease patients on maintenance hemodialysis. This study is a secondary analysis of a cross-sectional study entitled "Development and Validation of a Predictive energy Equation in Hemodialysis". PEW risk identified by the 7-point SGA and the PEW score was compared against the nutrition evaluations conducted by registered dietitian nutritionists through data examination from the original study (reference standard). A total of 133 patients were included for the analysis. The sensitivity, specificity, positive and negative predictive value (PPV and NPV), positive and negative likelihood ratio (PLR and NLR) of both scoring tools were calculated when compared against the reference standard. The patients were predominately African American (n=112, 84.2%), non-Hispanic (n=101, 75.9%), and male (n=80, 60.2%). Both the 7-point SGA (sensitivity=78.6%, specificity=59.1%, PPV=33.9%, NPV=91.2%, PLR=1.9, and NLR=0.4) and the PEW score (sensitivity=100%, specificity=28.6%, PPV=27.2%, NPV=100%, PLR=1.4, and NLR=0) were more sensitive than specific in identifying PEW risk. The 7-point SGA may miss 21.4% patients having PEW and falsely identify 40.9% of patients who do not have PEW. The PEW score can identify PEW risk in all patients, but 71.4% of patients identified may not have PEW risk. Both the 7-point SGA and the PEW score could identify PEW risk. The 7-point SGA was more specific, and the PEW score was more sensitive. Both scoring tools were found to be clinically confident in identifying patients who were actually not at PEW risk.

Comparison of a Handheld Indirect Calorimetry Device and Predictive Energy Equations Among Individuals on Maintenance Hemodialysis

Comparison of a Handheld Indirect Calorimetry Device and Predictive Energy Equations Among Individuals on Maintenance Hemodialysis

Use of Predictive Energy Expenditure Equations in Individuals with Lower Limb Loss at Seated Rest

BackgroundAs a result of the global war on terrorism, there has been a significant increase in young service members with traumatic amputations. Few published data are available on metabolic requirements for young, active individuals after traumatic limb loss, especially lower limb loss. ObjectiveThe purpose of this study was to determine which predictive energy equation best predicted resting energy expenditure (REE) in this population. MethodsOne hundred service members, 50 with at least one traumatic lower limb loss and 50 without limb loss, completed this study. Mean (standard deviation [SD]) age, height, and weight were 27.3 years (±5.3), 178.5 cm (±7.7), 86.5 kg (±15.8) for those with limb loss; and 29.4 years (±5.8), 179.1 cm (±6.7), 85.9 kg (±12.6) for those without. REE was measured using the Oxycon Mobile metabolic system (CareFusion). Measured REE was compared with the following REE equations: Mifflin-St Joer, Harris Benedict, Owen, 25 kcal/kg, and 30 kcal/kg. ResultsAll equations tended to underestimate or overestimate REE for both groups (P<0.001); however, the 25 kcal/kg had a more even distribution of disagreement for individuals with limb loss and without (P=0.100 and P=0.308, respectively), with 52% within ±10%. ConclusionsThe 25 kcal/kg best predicts REE for young, active individuals with or without limb loss. Future studies may determine that more appropriate equations are most useful for different subgroups of this population.

Comparison of Predictive Energy Equations Among Individuals on Maintenance Hemodialysis

Comparison of Predictive Energy Equations Among Individuals on Maintenance Hemodialysis

Clinical Outcomes in Critically Ill Patients Associated With the Use of Complex vs Weight-Only Predictive Energy Equations.

The energy intake goal is important to achieving energy intake in critically ill patients, yet clinical outcomes associated with energy goals have not been reported. This secondary analysis used the Improving Nutrition Practices in the Critically III International Nutrition Surveys database from 2007-2009 to evaluate whether mortality or time to discharge alive is related to use of complex energy prediction equations vs weight only. The sample size was 5672 patients in the intensive care unit (ICU) ≥ 4 days and a subset of 3356 in the ICU ≥ 12 days. Mortality and time to discharge alive were compared between groups by regression, controlling for age, sex, admission type, Acute Physiology and Chronic Health Evaluation II score, ICU geographic region, actual energy intake, and obesity. There was no difference in mortality between the use of complex and weight-only equations (odds ratio [OR], 0.90; 95% confidence interval [CI], 0.86-1.15), but obesity (OR, 0.83; 95% CI, 0.71-0.96) and higher energy intake (OR, 0.65; 95% CI, 0.56-0.76) had lower odds of mortality. Time to discharge alive was shorter in patients fed using weight-only equations (hazard ratio [HR], 1.11; 95% CI, 1.01-1.23) in patients staying ≥ 4 days and with greater energy intake (HR, 1.19; 95% CI, 1.06-1.34) in patients in the ICU ≥ 12 days. These data suggest that higher energy intake is important to survival and time to discharge alive. However, the analysis was limited by actual energy intake <70% of goal. Delivery of full goal intake will be needed to determine the relationship between the method of determining energy goal and clinical outcomes.

Development of a Predictive Energy Equation for Maintenance Hemodialysis Patients: A Pilot Study

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). 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. 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. 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.