Predictive Energy Equations Research Articles

A retrospective study of resting energy expenditure in children hospitalized with different nutritional status.

Resting energy expenditure (REE) refers to the energy consumption of the body in a resting state without skeletal muscle activity. This study aimed to examine the REE among children hospitalized with varying nutritional status. This was a retrospective study. We enrolled 109 pediatric cases that underwent indirect calorimetry (IC) and divided into four groups: mild malnutrition group (15 cases), moderate malnutrition group (30 cases), severe malnutrition group (32 cases), and obesity group (32 cases). We compared and analyzed the measured REE (mREE) using IC with the predicted REE (pREE) using five energy equations. The paired t-test was used to compare the results of two samples. Pearson analysis was used to assess the correlation between two values. The agreement analysis was performed using the Bland-Altman method. There was no significant difference in mREE between the mild, moderate, and severe malnutrition groups, but each differed significantly from the obesity group. All populations exhibited significant correlation between the mREEs and all five energy equations, and the equation with the highest predictive accuracy was the Schofield equation, which achieved an accuracy of 47.7%. In subgroup analysis, there was no significant difference between mREE and pREE for each of the five equations in the mild, moderate malnutrition groups. Only the prediction result of the Liu equation was not significantly different from the mREE in the severe malnutrition group. The prediction accuracy of the Liu equation was relatively the highest (34.4%). However, in the obese group, there were significant differences in pREE and mREE between the Liu equation and Mifflin equation. Under different nutritional statuses, the results of the Bland-Altman analysis suggested that deviation values between REEs predicted by each equation and mREE were greater than ±10%. There were differences in REE among children with different nutritional status. The results obtained from the five predictive energy equations deviated from the IC results. When REE cannot be measured by IC, it is essential to choose an appropriate predictive energy equation based on the nutritional status of the individual.

Evaluation of Effective Energy Values of Six Protein Ingredients Fed to Beagles and Predictive Energy Equations for Protein Feedstuff.

This study evaluated the nutrition composition, the nutrient digestibility, and the energy value of six protein ingredients used in pet food by the difference method in six beagles within a 7 × 6 incomplete Latin square design. The results showed that the apparent total tract digestibility of gross energy (GE) and organic matter (OM) in beagles fed the fish meal (FM) and corn gluten meal (CGM) diets was higher than for those fed the meat and bone meal (MBM), soybean meal (SBM), mealworm meal (MM), and yeast extract (YE) diets (p < 0.05). The digestible energy (DE), metabolizable energy (ME), and net energy (NE) of the MM diet were greater than the other diets, and MBM was the lowest (p < 0.05). The ME of protein ingredients was positively correlated with organic matter and negatively correlated with the ash content. The NE of protein ingredients was positively correlated with the crude protein content and negatively correlated with the ash content. The study resulted in predictive energy equations for protein ingredients that were more accurate than the NRC's predictive equation of ME when the ash content of the ingredient was more than 30% DM. In conclusion, the nutrient digestibility and energy value of corn gluten meal were similar to those of fish meal and those of soybean meal were similar to yeast extract. All predictive energy equations for six protein feedstuffs had slight differences with measured energy values.

Comparison of Predictive Energy Equations and a Handheld Indirect Calorimetric Device in Obstructive Among Individuals with Sleep Apnea

Comparison of Predictive Energy Equations and a Handheld Indirect Calorimetric Device in Obstructive Among Individuals with Sleep Apnea

A critical view of the use of predictive energy equations for the identification of hypermetabolism in motor neuron disease: A pilot study

People living with motor neuron disease (MND) frequently struggle to consume an optimal caloric intake. Often compounded by hypermetabolism, this can lead to dysregulated energy homeostasis, prompting the onset of malnutrition and associated weight loss. This is associated with a poorer prognosis and reduced survival. It is therefore important to establish appropriate nutritional goals to ensure adequate energy intake. This is best done by measuring resting energy expenditure (mREE) using indirect calorimetry. However, indirect calorimetry is not widely available in clinical practice, thus dietitians caring for people living with MND frequently use energy equations to predict resting energy expenditure (pREE) and estimate caloric requirements. Energy prediction equations have previously been shown to underestimate resting energy expenditure in over two-thirds of people living with MND. Hypermetabolism has previously been identified using the metabolic index. The metabolic index is a ratio of mREE to pREE, whereby an increase of mREE by≥110% indicates hypermetabolism. We aim to critically reflect on the use of the Harris-Benedict (1919) and Henry (2005) energy prediction equations to inform a metabolic index to indicate hypermetabolism in people living with MND. mREE was derived using VO₂ and VCO₂ measurements from a GEMNutrition indirect calorimeter. pREE was estimated by Harris-Benedict (HB) (1919), Henry (2005) and kcal/kg/day predictive energy equations. The REE variation, described as the percentage difference between mREE and pREE, determined the accuracy of pREE ([pREE-mREE]/mREE) x 100), with accuracy defined as≤± 10%. A metabolic index threshold of ≥110% was used to classify hypermetabolism. All resting energy expenditure data are presented as kcal/24hr. Sixteen people living with MND were included in the analysis. The mean mREE was 1642 kcal/24hr ranging between 1110 and 2015 kcal/24hr. When REE variation was analysed for the entire cohort, the HB, Henry and kcal/kg/day equations all overestimated REE, but remained within the accuracy threshold (mean values were 2.81% for HB, 4.51% for Henry and 8.00% for kcal/kg/day). Conversely, inter-individual REE variation within the cohort revealed HB and Henry equations both inaccurately reflected mREE for 68.7% of participants, with kcal/kg/day inaccurately reflecting 41.7% of participants. Whilst the overall cohort was not classified as hypermetabolic (mean values were 101.04% for HB, 98.62% for Henry and 95.64% for kcal/kg/day), the metabolic index ranges within the cohort were 70.75%-141.58% for HB, 72.82%-127.69% for Henry and 66.09%-131.58% for kcal/kg/day, indicating both over- and under-estimation of REE by these equations. We have shown that pREE correlates with body weight (kg), whereby the lighter the individual, the greater the underprediction of REE. When applied to the metabolic index, this underprediction biases towards the classification of hypermetabolism in lighter individuals. Whilst predicting resting energy expenditure using the HB, Henry or kcal/kg/day equations accurately reflects derived mREE at group level, these equations are not suitable for informing resting energy expenditure and classification of hypermetabolism when applied to individuals in clinical practice.

Agreement between measured energy expenditure and predictive energy equations in paediatric oncology

Purpose: Optimal nutritional support in childhood cancer relies on the adequate provision of energy. This study investigated the impact of chemotherapy on resting energy expenditure (REE) during the first six months of treatment and the accuracy of predictive equations in calculating said requirements of newly diagnosed children with cancer. Methods: REE was measured at diagnosis utilising a validated bioelectrical impedance analysis (BIA) mobile unit and compared with three predictive equations (Schofield 1985, World Health Organization [WHO] 1985 and the Recommended Dietary Allowance [RDA] 1989). Agreement and accuracy of these equations were tested by determining bias and agreement rates and displayed using the Bland–Altman plot. Baseline values were plotted against monthly follow-up measurements over time. Statistical significance was 5% and a priori limits of agreement set between 90% and 110% of measured REE. Results: Forty-three newly diagnosed children with median age 4 years (IQR 2.0–7.6) were measured prior to chemotherapy initiation. Compared with measured REE (mean ± SD) 719.53 ± 206.29 kcal/day, all predictive equations significantly overestimated REE: WHO 1985 (889.75 ± 323.31 kcal/day; 23% overestimation), Schofield 1985 (899.62 ± 336.10 kcal/day; 25% overestimation) and RDA (1647.67 ± 481.06 kcal/day; 129% overestimation) (p < 0.001). Despite significant proportionate bias in all three equations (p < 0.001), the intra-class consistency coefficient showed good reliability for the Schofield 1985 (0.864) and WHO 1985 (0.849) equations. Though statistically significant (chi-square = 23.11, p < 0.003), the overall 1 kcal/kg (1.3%) increase for all cancer types at six months may not be clinically significant. Conclusion: Existing predictive equations are unable to calculate REE accurately at childhood cancer diagnosis, highlighting the need for future investigations into the development of cancer-specific equations.

Comparison of Various Predictive Energy Equations for Female University Students With Measured Basal Metabolic Rate

The aim of this study was to compare basal metabolic rate (BMR) calculated with various equations and BMR measured using an indirect calorimeter. The study was carried out on second-year university female students (n = 48) with a body mass index of less than 30. Indirect calorimetry with a ventilated hood was accepted as the gold standard and 11 predictive equations were used. Among the equations, Mifflin-St Jeor had the highest correlation (r = 0.435), but Bernstein (66.7%) and Owen (56.3%) were the most accurate equations. According to Bland-Altman analysis, the lowest bias and the highest explanation were obtained with the Bernstein and Owen equations. More comprehensive studies are needed in different groups to develop new equations with higher accuracy.

Predictive Energy Equations Are Correlated with Indirect Calorimetry in Critical Care: Is Correlation Good Enough?

Predictive Energy Equations Are Correlated with Indirect Calorimetry in Critical Care: Is Correlation Good Enough?

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 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/m2. 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.

An Examination of the Sex-Specific Nature of Nutrition Assessment within the Nutrition Care Process: Considerations for Nutrition and Dietetics Practitioners Working with Transgender and Gender Diverse Clients

An Examination of the Sex-Specific Nature of Nutrition Assessment within the Nutrition Care Process: Considerations for Nutrition and Dietetics Practitioners Working with Transgender and Gender Diverse Clients

Comparison of equations to predict the metabolisable energy content as applied to the vertical strata and plant parts of forage sorghum (

Context Nutritive values, particularly energy content of tropical forages, need to be accurately assessed so that rations can be more precisely formulated. Aims The research aimed to collate and compare equations used to predict metabolisable energy content in forage sorghum (Sorghum bicolor (L.) Moench) to ascertain the effect of vertical strata on metabolisable energy content to assist in producing silage of defined quality. Methods Twenty-four predictive metabolisable energy equations derived from international feeding standards were compared using forage sorghum samples grown under fertiliser and growth stage treatments. Samples were separated into leaf, stem and seed heads (where present) over four vertical strata. Key results Equations based on digestibility with crude protein were robust in the prediction of metabolisable energy and had application to routine laboratory use. Conclusions The current study suggests that predictions based on digestibility and crude protein content are best placed for metabolisable energy application. Such equations should be originally based on measured metabolisable energy content to establish a regression so as to be used for predictive purposes, and satisfy the biological requirement of in vivo and the laboratory measurement relationship with acceptable statistical error. Chemical composition relationships predicted different metabolisable energy contents. Implications Improved accuracy of the prediction of metabolisable energy content in tropical forages will provide better application of production models and more accurate decisions in ration formulation.

Prolonged progressive hypermetabolism during COVID-19 hospitalization undetected by common predictive energy equations.

Background & aimsIndirect calorimetry (IC) is the gold-standard for determining measured resting energy expenditure (mREE) in critical illness. When IC is not available, predicted resting energy expenditure (pREE) equations are commonly utilized, which often inaccurately predict metabolic demands leading to over- or under-feeding. This study aims to longitudinally assess mREE via IC in critically ill patients with SARS-CoV-2 (COVID-19) infection throughout the entirety of, often prolonged, intensive care unit (ICU) stays and compare mREE to commonly utilized pREE equations.MethodsThis single-center prospective cohort study of 38 mechanically ventilated COVID-19 patients from April 1, 2020 to February 1, 2021. The Q-NRG® Metabolic Monitor was used to obtain IC data. The Harris-Benedict (HB), Mifflin St-Jeor (MSJ), Penn State University (PSU), and weight-based equations from the American Society of Parenteral and Enteral Nutrition – Society of Critical Care Medicine (ASPEN-SCCM) Clinical Guidelines were utilized to assess the accuracy of common pREE equations and their ability to predict hypo/hypermetabolism in COVID-19 ICU patients.ResultsThe IC measures collected revealed a relatively normometabolic or minimally hypermetabolic mREE at 21.3 kcal/kg/d or 110% of predicted by the HB equation over the first week of mechanical ventilation (MV). This progressed to significant and uniquely prolonged hypermetabolism over successive weeks to 28.1 kcal/kg/d or 143% of HB predicted by MV week 3, with hypermetabolism persisting to MV week 7. Obese individuals displayed a more truncated response with significantly lower mREE versus non-obese patients in MV week 1 (19.5 ± 1.0 kcal/kg/d vs 25.1 ± 1.8 kcal/kg/d, respectively; p < 0.01), with little change in weeks 2–3 (19.5 ± 1.5 kcal/kg/d vs 28.0 ± 2.0 kcal/kg/d; p < 0.01). Both ASPEN-SCCM upper range and PSU pREE equations provided close approximations of mREE yet, like all pREE equations, occasionally over- and under-predicted energy needs and typically did not predict late hypermetabolism.ConclusionsStudy results show a truly unique metabolic response in COVID-19 ICU patients, characterized by significant and prolonged, progressive hypermetabolism peaking at 3 weeks’ post-intubation, persisting for up to 7 weeks in ICU. This pattern was more clearly demonstrated in non-obese versus obese patients. This response is unique and distinct from any previously described model of ICU stress response in its prolonged hypermetabolic nature. This data reaffirms the need for routine, longitudinal IC measures to provide accurate energy targets in COVID-19 ICU patients. The PSU and ASPEN-SCCM equations appear to yield the most reasonable estimation to IC-derived mREE in COVID-19 ICU patients, yet still often over-/under-predict energy needs. These findings provide a practical guide for caloric prescription in COVID-19 ICU patients in the absence of IC.

Comparison of Estimated Energy Requirements in Lung Transplant Candidates

Purpose Calculation of estimated energy requirements (EER) is critical in achieving nutritional targets in pre-lung transplantation (LT) candidates. This study compared bedside prediction of EER using simple equations with a more complex technology based EER using a multisensor device (Sensewear v8.1, SW). SW estimated energy requirements was then compared to estimated energy intake (EEI) at pre-LT assessment. Methods Single institution retrospective cross-sectional observational study of adult patients assessed for LT between Oct 2016 and Apr 2019. Patients wore the SW device on their left tricep for a minimum of 3 days. Patients with poor compliance, Results 117 patients were included. 55% male, mean age 51.8 ±13.8years, BMI 25.1±4.5kg/m2. 4 patients were assessed as underweight, 53 healthy weight, 44 overweight and 17 obese. 17% of patients were assessed as malnourished (SGA-B, mild-moderately malnourished). Respiratory disease: CF (15%), COPD (41%), IPF (31%), other (13%). Food record data existed for 82% of candidates (n=96). There was strong agreement between all predictive equations and SW. Schofield had the strongest correlation (0.836, p Conclusion Whilst all EER equations correlated well with SW as the reference standard, Schofield had the strongest agreement with SW, while larger variations were observed with other equations (over/underestimation) which may be clinically relevant for some patients. EER predictive energy equations provide a simple, rapid and accurate assessment of nutritional requirements in pre-lung transplantation.

Utilization of indirect calorimetry to assist in determining undiagnosed hypothyroidism in a patient on mechanical ventilation.

Published predictive equations are required when indirect calorimetry (IC) is unavailable in the clinical setting. Several medical conditions that are not accounted for by published predictive equations can impact a patient's resting energy expenditure, such as adrenal changes or alterations in thyroid-stimulating hormone (TSH). TSH levels significantly impact a patient's resting energy expenditure, with hypothyroidism decreasing and hyperthyroidism increasing energy requirements. Clinical hypothyroidism has been correlated with increased ventilator dependency in patients with critical illness and malnutrition. The following case study describes the utilization of IC to trigger a full evaluation for the diagnosis of hypothyroidism in an adult patient with multiple myeloma who was mechanically ventilated. IC results for this patient were 39% lower than estimated by predictive energy equations. TSH, thyroxine, and triiodothyronine serum assays were obtained to rule out hypothyroidism. Based on elevated TSH and low thyroxine, the patient was found to have undiagnosed hypothyroidism. Appropriate pharmaceutical and nutrition interventions were made based upon these results. This case demonstrates the impact hormonal changes can have on resting energy expenditure and how the utilization of IC can provide additional information other than energy requirements.

Predictive energy equations for spinal muscular atrophy type I children

Knowledge on resting energy expenditure (REE) in spinal muscular atrophy type I (SMAI) is still limited. The lack of a population-specific REE equation has led to poor nutritional support and impairment of nutritional status. To identify the best predictors of measuredREE (mREE) among simple bedside parameters, to include these predictors in population-specific equations, and to compare such models with the commonpredictive equations. Demographic, clinical, anthropometric, and treatment variables were examined as potential predictors of mREE by indirect calorimetry (IC) in 122 SMAI children consecutively enrolled in an ongoing longitudinal observational study. Parameters predicting REE were identified, and prespecified linear regression models adjusted for nusinersen treatment (discrete: 0=no; 1=yes) were used to develop predictive equations, separately in spontaneously breathing and mechanically ventilated patients. In naïve patients, the median (25th, 75th percentile) mREE was 480 (412, 575) compared with 394 (281, 554) kcal/d in spontaneously breathing and mechanically ventilated patients, respectively (P=0.009).In nusinersen-treated patients, the median (25th, 75th percentile) mREE was 609 (592, 702) compared with 639 (479, 723) kcal/d in spontaneously breathing and mechanically ventilated patients, respectively (P=0.949).Both in spontaneously breathing and mechanically ventilated patients, the best prediction of REE was obtained from 3 models, all using as predictors: 1 body size related measurement and nusinersen treatment status. Nusinersen treatment was correlated with higher REE both in spontaneously breathing and mechanically ventilated patients. The population-specific equations showed a lower interindividual variability of the bias than the other equation tested, however, they showed a high root mean squared error. We demonstrated that ventilatory status, nusinersen treatment, demographic, and anthropometric characteristics determine energy requirements in SMAI. Our SMAI-specific equations include variables available in clinical practice and were generally more accurate than previously published equations. At the individual level, however, IC is strongly recommended for assessing energy requirements. Further research is needed to externally validate these predictive equations.

An approach for the application of energy-based liquefaction procedure using field case history data

This paper presents an overview to the applicability of the “energy-based liquefaction approach” with regards to the new developments in the subject. The method involves comparing the strain energy for the soil liquefaction (capacity) with the strain energy imparted to the soil layer during an earthquake (demand). The performance of the method was evaluated by using a large database of SPT-based liquefaction case history. The energy-based method and the more commonly used stressbased method were compared in their capability to assess liquefaction potential under the same damaging historic earthquakes and geotechnical site conditions. In the procedure, the predictive strain energy equations were used to estimate the capacity energy values. These empirical equations have been developed based on the initial effective soil parameters. As for the energy of any given strong ground motion, it was computed from a velocity-time history of the ground motion and the unit mass of soil through utilization of kinetic energy concepts. The proposed energy-based method has effective way in evaluating the liquefaction potential based on the seismological parameters, contrary to the stress-based approach, where only peak ground acceleration (PGA) is considered.

Comparison of Predictive Energy Equations for Estimating Total Energy Expenditure Among Patients on Maintenance Hemodialysis

Comparison of Predictive Energy Equations for Estimating Total Energy Expenditure Among Patients on Maintenance Hemodialysis

Predictive energy equations are inaccurate for determining energy expenditure in adult burn injury: a retrospective observational study.

Severe burn injuries are associated with hypermetabolism. This study aimed to compare the measured energy expenditure (mEE) with predicted energy requirements (pERs), and to correlate energy expenditure (EE) with clinical parameters in adults with severe burn injury. Data were retrospectively analysed on 29 burn patients (median (interquartile range) age: 46 (28-61) years, % total body surface area burn: 37% (18-46%)) admitted to an intensive care unit. Indirect calorimetry was performed on 1-4 occasions per patient to measure EE. mEE was compared with pER calculated using four prediction equations. Bland-Altman and correlation analyses were performed. Mean ± SD mEE was 9752 ± 2089 kJ/day (143 ± 32% of predicted basal metabolic rate). Bland-Altman analysis demonstrated clinically important overestimation for three of the four prediction equations and wide 95% limits of agreement for all equations. Overestimation of EE was more marked early post-burn. mEE correlated with day post-burn (r = 0.42, P = 0.004) and number of operations prior to first EE measurement (r = 0.34, P = 0.016), but not with % total body surface area (r = 0.02, P = 0.9). Patients with severe burn injury exhibit hypermetabolism. The observed poor agreement between pER and mEE at an individual level indicates the value of indirect calorimetry in determining EE in burn injury.

Validation of predictive equations to assess energy expenditure in acute spinal cord injury.

Acute spinal cord injury (SCI) is devastating with morbidities compounded by inadequate nutrition. The American Society for Parenteral and Enteral Nutrition recommends indirect calorimetry (IC) to evaluate energy needs in SCI because no predictive energy equations have been validated. We sought to determine the accuracy of predictive equations to predict measured energy expenditure (MEE). A retrospective review was performed over 2 years. Patients 18 years or older with cervical SCI who received IC were included. Height, weight, maximum temperature and minute ventilation on day of IC, plus MEE and VCO2 from IC were obtained. Predicted energy expenditure (PEE) was calculated using Harris-Benedict (HB), Penn State (PS), Mifflin St. Jeor (MSJ), Weir, Ireton-Jones (IJ), and 25 kcal/kg formulas. MEE was then compared to the PEE of each method. Thirty-nine IC studies were completed for 20 patients. Weir had the strongest correlation to MEE (r = 0.98), followed by PS (r = 0.82). Correlations were similar among HB (r = 0.78), MSJ (r = 0.75), and IJ (r = 0.73), and weakest with 24 kcal/kg (r = 0.55). All had a p value <0.001. Deming regression confirmed strong correlations between Weir and PS to MEE, with coefficients of 1.03 and 1.515 (p < 0.001), respectively. Other formulas had comparatively higher coefficients and standard errors. Bland-Altman analysis confirmed Weir had the narrowest range of difference, with a mean difference of 25.5 kcal/day, followed by PS (-336.1 kcal/day). Weir is the best predictive energy equation, with all statistical tests demonstrating a strong correlation between MEE and Weir. The second best predictive equation is the Penn State formula, which predicts actual MEE measured by IC with high accuracy. Diagnostic study, level III.

Patients receiving dialysis do not have increased energy needs compared with healthy adults.

Patients receiving dialysis are thought to have increased energy needs due to stress from the dialysis process or uraemic toxins. However, certain conditions may induce hypometabolic states potentially leading to unwanted weight gains when increased calorie intake is recommended. Since there is conflicting research, this study aims to assess total energy expenditure (TEE) of patients receiving dialysis through analysis of resting energy expenditure (REE) and physical activity levels. Comprehensive review of the current literature on REE and physical activity levels. Two electronic databases (PubMed and CINAHL) were searched using keywords to find papers published within the last 10 years for physical activity studies and within the last five years for REE. Adults undergoing long-term dialysis treatments who do not have comorbidities that influence energy expenditure such as inflammation, hyperthyroidism or cancers. Participants were also required to ambulate without assistance. Only seven of the 325 studies found were included in this review. Most studies were controlled trials with one being a prospective study. Patients receiving dialysis had rest energy expenditures comparable to healthy adults. There was not a consensus between studies as to which predictive energy equation produced accurate energy recommendations. Overall, patients receiving dialysis were significantly less active compared with healthy adults and this related to muscle mass. Patients receiving dialysis tend to be sedentary or lightly active and do not have increased energy expenditure compared with healthy adults. Therefore, stable patients should not be prescribed increased calorie intake.

Modeling a Predictive Energy Equation Specific for Maintenance Hemodialysis.

Hypermetabolism is theorized in patients diagnosed with chronic kidney disease who are receiving maintenance hemodialysis (MHD). We aimed to distinguish key disease-specific determinants of resting energy expenditure to create a predictive energy equation that more precisely establishes energy needs with the intent of preventing protein-energy wasting. For this 3-year multisite cross-sectional study (N = 116), eligible participants were diagnosed with chronic kidney disease and were receiving MHD for at least 3 months. Predictors for the model included weight, sex, age, C-reactive protein (CRP), glycosylated hemoglobin, and serum creatinine. The outcome variable was measured resting energy expenditure (mREE). Regression modeling was used to generate predictive formulas and Bland-Altman analyses to evaluate accuracy. The majority were male (60.3%), black (81.0%), and non-Hispanic (76.7%), and 23% were ≥65 years old. After screening for multicollinearity, the best predictive model of mREE (R2 = 0.67) included weight, age, sex, and CRP. Two alternative models with acceptable predictability (R2 = 0.66) were derived with glycosylated hemoglobin or serum creatinine. Based on Bland-Altman analyses, the maintenance hemodialysis equation that included CRP had the best precision, with the highest proportion of participants' predicted energy expenditure classified as accurate (61.2%) and with the lowest number of individuals with underestimation or overestimation. This study confirms disease-specific factors as key determinants of mREE in patients on MHD and provides a preliminary predictive energy equation. Further prospective research is necessary to test the reliability and validity of this equation across diverse populations of patients who are receiving MHD.