Comparison of Resting Energy Expenditure Prediction Methods With Measured Resting Energy Expenditure in Obese,Hospitalized Adults

Background: Several methods are available to estimate caloric needs in hospitalized, obese patients who require specialized nutrition support; however, it is unclear which of these strategies most accurately approximates the caloric needs of this patient population. The purpose of this study was to determine which strategy most accurately predicts resting energy expenditure in this subset of patients. Methods: Patients assessed at high nutrition risk who required specialized nutrition support and met inclusion and exclusion criteria were enrolled in this observational study. Adult patients were included if they were admitted to a medical or surgical service with a body mass index ≥ 30 kg/m². Criteria excluding patient enrollment were pregnancy and intolerance or contraindication to indirect calorimetry procedures. Investigators calculated estimations of resting energy expenditure for each patient using variations on the following equations: Harris‐Benedict, Mifflin–St. Jeor, Ireton‐Jones, 21 kcal/kg body weight, and 25 kcal/kg body weight. For nonventilated patients, the MedGem handheld indirect calorimeter was used. For ventilated patients, the metabolic cart was used. The primary endpoint was to identify which estimation strategy calculated energy expenditures to within 10% of measured energy expenditures. Results: The Harris‐Benedict equation, using adjusted body weight with a stress factor, most frequently estimated resting energy expenditure to within 10% measured resting energy expenditure at 50% of patients. Conclusion: Measured energy expenditure with indirect calorimetry should be employed when developing nutrition support regimens in obese, hospitalized patients, as estimation strategies are inconsistent and lead to inaccurate predictions of energy expenditure in this patient population.     

Measurement of Resting Energy Expenditure in Healthy Children

Background: The role that the components of energy expenditure play in the etiology of childhood obesity has highlighted the need for greater accuracy and standardized protocols for the measurement of resting energy expenditure (REE). However, protocols used to assess REE in children are varied, and consensus on a suitable method for measuring REE in children has not been reached. This study was undertaken to determine the effect of measurement time and measurement device (mask or mouthpiece) on REE in healthy children. Design: Following a 12‐hour fast and abstinence from exercise, 23 children (age, 7–12 years) completed two 35‐minute protocols: one with a face mask and the other with a mouthpiece/noseclip. Energy expenditure was measured continuously via indirect calorimetry, while device acceptability was assessed using a 6‐point comfort rating scale. Results: Repeated measures ANOVA indicated that there was no significant difference in REE when measured after 10, 15, 20, or 25 minutes of rest compared to 30 minutes for either the mask or mouthpiece/noseclip (REE range, 1371–1460 kcal/d). Examination of the percentage coefficient of variation (CV) in energy expenditure for each time period by device showed that the least variation existed after 20 minutes of measurement using the mask (CV 6%). Paired t test analysis indicated significantly less discomfort when wearing the mask compared to the mouthpiece/noseclip. Conclusion: It would appear that a 20‐minute protocol using a mask may increase compliance and prove to be a more practical protocol for measuring REE in children.     

Energy Expenditure in Patients With Esophageal,Gastric, and Colorectal Cancer

Background: Changes in resting energy expenditure (REE) appear to be one of the causes of nutritional depletion in cancer. Assessing REE may be an important tool for providing adequate nutritional therapy to these patients. The aims of this study were to evaluate REE of patients with gastrointestinal tract cancer and to compare it to that of healthy controls. Methods: A total of 20 patients, with esophageal (n = 3), gastric (n = 9), and colorectal (n = 8) cancers, and 20 healthy subjects were included. Indirect calorimetry (IC) was used to measure REE in both groups. The “pocket” equation (30 kcal/kg) and the Harris‐Benedict equation, with correction factors of 1.3 (activity) and 1.1 (injury), were employed for assessment of the estimated total energy expenditure (TEE). Statistics included Mann‐Whitney and paired t tests, Bland Altman analysis, and multivariate regression. Results: The REE of the patients (1,274.5 kcal [1,002.9–2,174.9]) was similar to that of the controls (1,445.5 kcal [1,114.5–1,762.6], not significant), even when corrected for the amount of metabolically active tissue. The pocket equation was effective in predicting the patients' TEE, with a 1.7% (32 kcal) difference being observed in comparison with the IC results corrected with the activity factor (not significant). Conclusions: The patients with digestive tract cancers showed a similar REE to that of the controls. The current formula of 30 kcal/kg is suitable for estimating the TEE of these patients.     

Comparison of Resting Energy Expenditure Between Cancer Subjects and Healthy Controls: A Meta-Analysis

There is conflicting evidence surrounding the extent of changes in resting energy expenditure (REE) in cancer. This meta-analysis aimed to establish the mean difference in REE, as kilojoules per kilogram fat-free mass, among cancer patients when compared to healthy control subjects. The secondary aim was to determine differences among different cancer types. PubMed, Cochrane Library, Medline, Science Direct, Scopus, Web of Science, Wiley Online Library, and ProQuest Central were searched from the earliest records until March 2014. Studies were included if measured REE was reported as kilojoules or kilocalories per kilogram fat-free mass (FFM) in adult subjects with cancer. Twenty-seven studies were included in the meta-analysis. Fourteen studies included both cancer (n = 1453) and control (n = 1145) groups. The meta-analysis shows an average increase in REE of 9.66 (95% confidence interval: 3.34, 15.98) kJ/kgFFM/day in cancer patients when compared to control subjects. Heterogeneity was detected (P < 0.001) which suggest variations in REE among cancer types. Elevations are most noticeable in patients with cancers of metabolically demanding organs.     

静息能量消耗测定在慢性肝病中的应用

慢性肝病患者的静息能量消耗应采用开放式间接测热法测得,合理的能量代谢调整有助于肝功能改善并防止并发症发生,测定静息能量消耗可为慢性肝病患者营养支持治疗个体化提供依据。     

Predicting Resting Energy Expenditure in Healthy Puerto Rican Adults

Equations to predict resting energy expenditure (REE) can be influenced by cultural and climatic factors. The purpose of this cross-sectional study was to evaluate the validity of the Harris-Benedict and Mifflin-St Jeor equations to predict REE in 48 healthy Puerto Rican adults (23 men, 25 women; aged 21 to 60 years, tested between January and March 2007) using indirect calorimetry as the criterion method for comparison. Weight, height, and skinfold thickness were measured. One-way analysis of variance was used to determine differences between the REE measured and predicted with the two equations, and independent t tests were used to detect differences between men and women. Linear and multiple regressions were conducted to determine relationships between the measured and predicted REE and to evaluate factors influencing REE. The REE predicted with Harris-Benedict and Mifflin-St Jeor were not statistically different from the REE measured with indirect calorimetry (mean±standard deviation: 1,555±268, 1,500±285, and 1,633±299 kcal/day, respectively; P=0.08). There was a strong correlation between the REE measured and predicted with Harris-Benedict and Mifflin-St Jeor (r=0.83, 0.87, respectively; P=0.0001). Mean REE was higher in men compared to women, and fat-free mass was the most influencing factor on REE. The Harris-Benedict and Mifflin-St Jeor are both valid equations for the prediction of REE in healthy Puerto Rican adults living in a tropical climate such as Puerto Rico. Both equations are appropriate for dietetics practitioners to use in assessing energy requirements in this population.     

Changes in Resting Energy Expenditure Following Orthotopic Liver Transplantation

Background:There is no consensus whether resting energy expenditure (REE) following orthotopic liver transplantation (OLT) is altered. Methods: The objectives of this investigation were to describe changes in measured REE (mREE) using indirect calorimetry in 25 OLT patients on days 5, 10, and 15 after baseline (within 72 hours following OLT) and compare mREE changes with those calculated with 2 predicted equations for energy expenditure (pREE): the Harris‐Benedict and Schofield equations. Results: Patients were 57 ± 5.4 years of age, 44% were male, 36% were black, and 72% had liver disease of viral etiology. Measured REE (at baseline and days 5, 10, and 15, per kcal/d: 1832 ± 952, 1565 ± 383, 1538 ± 345, 1578 ± 418) and kcal per kilogram of body weight (22.7 ± 12.8, 18.4 ± 4, 18.7 ± 3.8, 21 ± 6.5) did not change over time. In contrast, changes in pREE based on either the Harris‐Benedict (P < .001) or Schofield (P = .006) equation using measured weights at each corresponding time point and lowest body weight during the study to estimate dry weight were significant. Conclusions: Wide ranges in both mREE and mREE expressed per kilogram of body weight at each study time point were observed in contrast to pREE, which declined by day 15. The observed differences in mREE over time suggest indirect calorimetry is indicated if available following OLT. Additional research is warranted to determine the most appropriate predictive equation with suitable stress factors to use when indirect calorimetry is not available.     

Elevated Resting Energy Expenditure in Adolescents With Sickle Cell Anemia

Objective To assess the reliability of standard prediction equations in estimating resting energy expenditure (REE) values in adolescents with sickle cell anemia.Subjects/design Body composition and metabolic measurements were performed in 8 adolescents, aged 11 to 18 years, with homozygous sickle cell anemia. REE was measured by indirect calorimetry under standard conditions, and measurements were compared with 4 prediction formulas (Harris-Benedict, Schofield, Mayo Clinic, and Food and Agriculture Organization/World Health Organization/United Nations University). Fat-free mass was measured to assess REE per unit of actively metabolizing tissue. Fat-free mass was expressed as a mean of values obtained by densitometry, deuterium dilution, ⁴⁰K-counting, and total body electrical conductivity.Statistical analyses Repeated measures analysis of variance was performed to determine whether measured REE values and predicted values differed. The Fischer test was used to identify which predicted values differed significantly from the measured REE.Results All 4 prediction formulas significantly underestimated REE. Group mean values for the prediction formulas ranged from 83% to 89% of the measured value. REE averaged 47.7±10.0 kcal/kg fat-free mass per day, which is 30% to 50% higher than reported values in healthy adolescent populations.Conclusions These data suggest that REE is elevated in adolescents with sickle cell anemia. Standard equations used to predict REE are unreliable in these patients.Applications REE in patients with sickle cell anemia is best determined by indirect or direct measurement of energy expenditure. Clinically useful formulas to estimate REE should be developed for patients with conditions, including sickle cell anemia, where the metabolic rate may be altered.J Am Diet Assoc. 1999;99:195–199.     

Comment on: Measurement of Resting Energy Expenditure in Healthy Children

Background: The role that the components of energy expenditure play in the etiology of childhood obesity has highlighted the need for greater accuracy and standardized protocols for the measurement of resting energy expenditure (REE). However, protocols used to assess REE in children are varied, and consensus on a suitable method for measuring REE in children has not been reached. This study was undertaken to determine the effect of measurement time and measurement device (mask or mouthpiece) on REE in healthy children. Design: Following a 12‐hour fast and abstinence from exercise, 23 children (age, 7–12 years) completed two 35‐minute protocols: one with a face mask and the other with a mouthpiece/noseclip. Energy expenditure was measured continuously via indirect calorimetry, while device acceptability was assessed using a 6‐point comfort rating scale. Results: Repeated measures ANOVA indicated that there was no significant difference in REE when measured after 10, 15, 20, or 25 minutes of rest compared to 30 minutes for either the mask or mouthpiece/noseclip (REE range, 1371–1460 kcal/d). Examination of the percentage coefficient of variation (CV) in energy expenditure for each time period by device showed that the least variation existed after 20 minutes of measurement using the mask (CV 6%). Paired t test analysis indicated significantly less discomfort when wearing the mask compared to the mouthpiece/noseclip. Conclusion: It would appear that a 20‐minute protocol using a mask may increase compliance and prove to be a more practical protocol for measuring REE in children.(JPEN J Parenter Enteral Nutr. 2009;33:640‐645)     

Predictive Equations Underestimate Resting Energy Expenditure in Female Adolescents with Phenylketonuria

Resting energy expenditure (REE) is often used to estimate total energy needs. The Schofield equation based on weight and height has been reported to underestimate REE in female children with phenylketonuria (PKU). The objective of this observational, cross-sectional study was to evaluate the agreement of measured REE with predicted REE for female adolescents with PKU. A total of 36 females (aged 11.5 to 18.7 years) with PKU attending Emory University's Metabolic Camp (June 2002 to June 2008) underwent indirect calorimetry. Measured REE was compared to six predictive equations using paired Student's t tests, regression-based analysis, and assessment of clinical accuracy. The differences between measured and predicted REE were modeled against clinical parameters to determine whether a relationship existed. All six selected equations significantly under predicted measured REE (P<0.005). The Schofield equation based on weight had the greatest level of agreement, with the lowest mean prediction bias (144 kcal) and highest concordance correlation coefficient (0.626). However, the Schofield equation based on weight lacked clinical accuracy, predicting measured REE within ±10% in only 14 of 36 participants. Clinical parameters were not associated with bias for any of the equations. Predictive equations underestimated measured REE in this group of female adolescents with PKU. Currently, there is no accurate and precise alternative for indirect calorimetry in this population.     

Resting Energy Expenditure in Critically Ill Patients With Spontaneous Intracranial Hemorrhage

Background: Data on energy requirements of patients with spontaneous intracranial hemorrhage (SICH) are scarce. The objective of this study was to determine the resting energy expenditure (REE) in critically ill patients with SICH and to compare it with the predicted basal metabolic rate (BMR). Methods: In 30 nonseptic patients with SICH, the REE was measured during the 10 first posthemorrhage days with the use of indirect calorimetry (IC). Predicted BMR was also evaluated by the Harris‐Benedict (HB) equation. Bland‐Altman analysis was used to evaluate the agreement between measured and predicted values. The possible effect of confounding factors (demographics, disease, and severity of illness score) on the evolution of continuous variables was also tested. Results: mean predicted BMR, calculated by the HB equation, was 1580.3 ± 262 kcal/d, while measured REE was 1878.9 ± 478 kcal/d (117.5% BMR). Compared with BMR, measured REE values showed a statistically significant increase at all studied points (P < .005). Measured and predicted values showed a good correlation (r = 0.73, P < .001), but the test of agreement between the 2 methods with the Bland‐Altman analysis showed a mean bias (294.6 ± 265.6 kcal/d) and limits of agreement (–226 to 815.29 kcal/d) that were beyond the clinically acceptable range. REE values presented a trend toward increase over time (P = .077), reaching significance (P < .005) after the seventh day. Significant correlation was found between REE and temperature (P = .002, r = 0.63), as well as between REE and cortisol level (P = .017, r = 0.62) on the 10th day. No correlation was identified between REE and depth of sedation, as well as Acute Physiology and Chronic Health Evaluation II, Glasgow Coma Scale, and Hunt and Hess scores. Conclusions: During the early posthemorrhagic stage, energy requirements of critically ill patients with SICH are increased, presenting a trend toward increase over time. Compared with IC, the HB equation underestimates energy requirements and is inefficient in detecting individual variability of REE in this group of patients.     

风心病瓣膜置换手术对能量代谢的影响

目的观察风湿性心脏病瓣膜置换手术患者围体外循环期静息能量消耗的改变。方法将接受体外循环手术的风心病患者20例分为男、女两组,A组为男性,B组为女性。采用间接能量监测仪测定手术前后的静息能量消耗,手术前后构成自身对照。结果男性患者在术后第1、3、5、7天的静息能量消耗与术前的静息能量消耗之比分别为(1.346±0.004雪、穴1.158±0.001雪、穴1.091±0.001雪和穴0.992±0.001雪;女性患者为穴1.285±0.002雪、穴1.130±0.001雪、穴1.052±0.001雪和穴1.008±0.0003雪;术后前5天明显高于术前穴P<0.01雪,术后第7天与术前无显著性差异。手术对男女性患者静息能量消耗的影响在术后第1天有显著性差异。结论风心病瓣膜置换手术后能量消耗有一定程度的增高。     

Energy Expenditure After Liver Resection: Validation of a Mobile Device for Estimating Resting Energy Expenditure and an Investigation of Energy Expenditure Change After Liver Resection

Background: Resting energy expenditure (REE) is the major component of total energy expenditure. REE is traditionally performed by indirect calorimetry (IC) and is not well investigated after liver surgery. A mobile device (SenseWear Armband [SWA]) has been validated when estimating REE in other clinical settings but not liver resection. The aims of this study are to validate SWA vs IC, quantify REE change following liver resection, and determine factors associated with REE change. Materials and Methods: Patients listed for open liver resection prospectively underwent IC and SWA REE recordings pre‐ and postoperatively. In addition, the SWA was worn continuously postoperatively to estimate daily REE for the first 5 postoperative days. To determine acceptability of the SWA, validation analysis was performed. To assess REE change, peak postoperative REE was compared with preoperative levels. Factors associated with REE change were also analyzed. Results: SWA showed satisfactory validity compared with IC when estimating REE, although postoperatively, the 95% levels of agreement (–5.56 to 3.18 kcal/kg/d) may introduce error. Postoperative REE (median, 23.5 kcal/kg/d; interquartile range [IQR], 22.6–25.7 kcal/kg/d) was significantly higher than predicted REE (median, 19.7 kcal/kg/d; IQR, 19.1–21.0 kcal/kg/d; P < .0001). Median REE rise was 11% (IQR, –1% to 25%). Factors associated with REE rise of >11% were age (P = .017) and length of operation (P = .03). Conclusions: SWA offers a suitable alternative to IC when estimating postoperative REE, but the magnitude of the error (8.74 kcal/kg/d) could hinder its accuracy. REE quantification after liver resection is important to identify patients who could be prone to energy imbalance and therefore malnutrition.     

Cross-Validation of Prediction Equations for Resting Energy Expenditure in Young, Healthy Children

Objective To examine the accuracy of several prediction equations for resting energy expenditure (REE) in children.Design REE was measured in 113 prepubertal children (60 girls and 53 boys aged 3.9 to 7.8 years old, weighing 14.7 to 30.0 kg) using indirect calorimetry and compared with values estimated from the prediction equations of Altman and Dittmer, The Food and Agriculture Organization/World Health Organization/United Nations University (FAO/WHO/UNU), Maffeis et al, and Harris and Benedict.Statistical analysis Measured REE (MREE) was compared with predicted REE (FREE) by means of regression analysis. Prediction equations were considered accurate if the regression of MREE vs FREE was not significantly different from the line of identity (slope=l.0; INTERCEPT=0). Precision was assessed by the multiple correlation coefficient of the regression of MREE vs FREE.Results MREE was 938±119 kcal/day, and FREE was 1,057+224 kcal/day for the Altman and Dittmer equations, 956±84 kcal/day for the FAO/WHO/UNU equations, 948±64 kcal/day for the equations of Maffeis et al, and 954+102 kcal/day for the Harris-Benedict equations. The regression of MREE vs FREE was significantly different from the line of identity for all prediction equations except the FAO/WHO/UNU equations (slope=0.96, P=.735; INTERCEPT=–15 kcal/day, P=.885 for girls and SLOPE=1.08, P=.635; INTERCEPT=-62 kcal/day, P=.635 for boys). None of the equations was precise for MREE vs FREE (for all, R²<.6). For the FAO/WHO/UNU equations, less than half of the predictions were within ±50 kcal/day but 99% were within 200 kcal/day.Conclusion Most prediction equations for REE in children do not accurately or precisely estimate REEs. The exception is the FAO/WHO/UNU equations, which are reasonably accurate and precise for practical purposes. J Am Diet Assoc. 1997;97: 140–145.     

Resting Energy Expenditure of Children and Adolescents With Nonalcoholic Fatty Liver Disease

Background:The mainstay of treatment for pediatric nonalcoholic fatty liver disease (NAFLD) is lifestyle modification, which includes dietary changes that lead to slow but sustained weight loss or weight stabilization in growing children. Accurate estimation of energy requirements is necessary to achieve this goal. The objective of this study was to assess the accuracy of the most commonly used equations in predicting the resting energy expenditure (REE) of children with NAFLD. Methods: This was a retrospective study performed in a single institution. The predictive accuracy of various equations was assessed by comparing their estimates against the measured REE obtained with indirect calorimetry. Accuracy was defined as an estimate within 10% of measured REE. Results: Fifty‐six children (70% male; 52% white and 36% Asian) with a median age of 13 years were included. The median measured REE was 1829 kcal/d. Of the equations studied, the Schofield had the smallest average bias (–32 kcal/d; confidence interval, –121 to 56). The Schofield and Molnar equations were the most accurate, providing REE estimates within 10% of measured in 59% of cases. The remaining equations had lower and variable predictive accuracy. The use of adjusted body weight in predictive equations did not improve the predictive accuracy. Conclusion: In a cohort of children and adolescents with NAFLD, the Schofield and Molnar equations performed best in predicting energy expenditure. However, predictive equations were often inaccurate, suggesting that clinicians should interpret their results with caution and consider using indirect calorimetry when available.