Predictive Equations Are Inaccurate in the Estimation of the Resting Energy Expenditure of Children With End‐Stage Liver Disease

Background and Objectives: Malnutrition is common in children with end‐stage liver disease (ESLD) and is associated with increased morbidity and mortality. The inability to accurately estimate energy needs of these patients may contribute to their poor nutrition status. In clinical practice, predictive equations are used to calculate resting energy expenditure (cREE). The objective of this study is to assess the accuracy of commonly used equations in pediatric patients with ESLD. Methods: Retrospective study performed at the Hospital for Sick Children. Clinical, laboratory, and indirect calorimetry data from children listed for liver transplant between February 2013 and December 2014 were reviewed. Calorimetry results were compared with cREE estimated using the Food and Agriculture Organization/World Health Organization/United Nations University (FAO/WHO/UNU), Schofield [weight], and Schofield [weight and height] equations. Results: Forty‐five patients were included in this study. The median age was 9 months, and the most common indication for transplantation was biliary atresia (64%). The Schofield [weight and height], FAO/WHO/UNU, and Schofield [weight] equations were compared with indirect calorimetry and found to have a mean (SD) difference of 48.8 (344.0), 59.3 (229.8), and 206.5 (502.6) kcal/d, respectively. The FAO/WHO/UNU, Schofield [weight], and Schofield [weight and height] equations introduced a mean error of 21%, 38%, and 76%, respectively. The FAO/WHO/UNU equation tended to underestimate, whereas the Schofield equations overestimated the REE. Conclusions: Commonly used predictive equations perform poorly in infants and young children with ESLD. Indirect calorimetry should be used when available to guide energy provision, particularly in children who are already malnourished.     

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.     

Estimation of Resting Energy Expenditure Using Predictive Equations in Critically Ill Children: Results of a Systematic Review

Provision of adequate energy intake to critically ill children is associated with improved prognosis, but resting energy expenditure (REE) is rarely determined by indirect calorimetry (IC) due to practical constraints. Some studies have tested the validity of various predictive equations that are routinely used for this purpose, but no systematic evaluation has been made. Therefore, we performed a systematic review of the literature to assess predictive equations of REE in critically ill children. We systematically searched the literature for eligible studies, and then we extracted data and assigned a quality grade to each article according to guidelines of the Academy of Nutrition and Dietetics. Accuracy was defined as the percentage of predicted REE values to fall within ±10% or ±15% of the measured energy expenditure (MEE) values, computed based on individual participant data. Of the 993 identified studies, 22 studies testing 21 equations using 2326 IC measurements in 1102 children were included in this review. Only 6 equations were evaluated by at least 3 studies in critically ill children. No equation predicted REE within ±10% of MEE in >50% of observations. The Harris–Benedict equation overestimated REE in two‐thirds of patients, whereas the Schofield equations and Talbot tables predicted REE within ±15% of MEE in approximately 50% of observations. In summary, the Schofield equations and Talbot tables were the least inaccurate of the predictive equations. We conclude that a new validated indirect calorimeter is urgently needed in the critically ill pediatric population.)     

Energy Expenditure in Critically Ill Elderly Patients: Indirect Calorimetry vs Predictive Equations

Background: Predictive equations (PEs) are used for estimating resting energy expenditure (REE) when the measurements obtained from indirect calorimetry (IC) are not available. This study evaluated the degree of agreement and the accuracy between the REE measured by IC (REE‐IC) and REE estimated by PE (REE‐PE) in mechanically ventilated elderly patients admitted to the intensive care unit (ICU). Methods: REE‐IC of 97 critically ill elderly patients was compared with REE‐PE by 6 PEs: Harris and Benedict (HB) multiplied by the correction factor of 1.2; European Society for Clinical Nutrition and Metabolism (ESPEN) using the minimum (ESPENmi), average (ESPENme), and maximum (ESPENma) values; Mifflin–St Jeor; Ireton‐Jones (IJ); Fredrix; and Lührmann. Degree of agreement between REE‐PE and REE‐IC was analyzed by the interclass correlation coefficient and the Bland‐Altman test. The accuracy was calculated by the percentage of male and/or female patients whose REE‐PE values differ by up to ±10% in relation to REE‐IC. Results: For both sexes, there was no difference for average REE‐IC in kcal/kg when the values obtained with REE‐PE by corrected HB and ESPENme were compared. A high level of agreement was demonstrated by corrected HB for both sexes, with greater accuracy for women. The best accuracy in the male group was obtained with the IJ equation but with a low level of agreement. Conclusions: The effectiveness of PEs is limited for estimating REE of critically ill elderly patients. Nonetheless, HB multiplied by a correction factor of 1.2 can be used until a specific PE for this group of patients is developed.     

Accuracy of Predictive Equations for Energy Expenditure in Mexicans Living With HIV/AIDS With and Without Antiretroviral Therapy

Introduction: Determination of the resting energy expenditure (REE) is essential for planning nutrition therapy in patients with human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) to help to improve their nutrition status. We aim to evaluate the agreement and accuracy of prediction equations that estimate the REE in a Mexican population with a diagnosis of HIV/AIDS with and without antiretroviral therapy (ART). Methods: A cross‐sectional study in Mexican patients with HIV/AIDS with and without ART. Weight, height, and body composition measured with dual‐energy x‐ray absorptiometry were evaluated. The REE was determined with indirect calorimetry and estimated using the Mifflin–St Jeor (MSJ), Harris‐Benedict (HB), Schofield 1 and 2, Cunningham, Melchior 91, Melchior 93, and Batterham equations. The Bland‐Altman method assessed agreement between the real and estimated values, and the percent difference between these values was used to assess the prediction accuracy. Results: Sixty‐five adults without ART and 102 adults with ART were included. The mean REE (kcal/kg) was 24.8 ± 2.4 and 23.8 ± 3.6 in patients without and with ART, respectively. Good agreement and reliability were observed in the HB (intraclass correlation coefficient [ICC], 0.75; P < .05), Batterham (ICC, 0.79; P < .05), Schofield 1 (ICC, 0.74; P < .05), and Schofield 2 (ICC, 0.78; P < .05) results in individuals without ART. In individuals with ART, good agreement and reliability were observed with the HB equation (ICC, 0.76; P < .05). The MSJ equation showed good agreement with poor reliability (ICC, 0.05; P < .05). Conclusion: The equations with the best agreement and accuracy were Schofield 2, Batterham, and HB in individuals without ART and HB and MSJ in the population with ART.     

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.     

Resting Energy Expenditure Is Not Altered in Children and Adolescents with Obesity. Effect of Age and Gender and Association with Serum Leptin Levels

In children and adolescents, obesity does not seem to depend on a reduction of resting energy expenditure (REE). Moreover, in this young population, the interactions between either age and obesity or between age and gender, or the role of leptin on REE are not clearly understood. To compare the levels of REE in children and adolescents we studied 181 Caucasian individuals (62% girls) classified on the basis of age- and sex-specific body mass index (BMI) percentile as healthy weight (n = 50), with overweight (n = 34), or with obesity (n = 97) and in different age groups: 8–10 (n = 38), 11–13 (n = 50), and 14–17 years (n = 93). REE was measured by indirect calorimetry and body composition by air displacement plethysmography. Statistically significant differences in REE/fat-free mass (FFM) regarding obesity or gender were not observed. Absolute REE increases with age (p < 0.001), but REE/FFM decreases (p < 0.001) and there is an interaction between gender and age (p < 0.001) on absolute REE showing that the age-related increase is more marked in boys than in girls, in line with a higher FFM. Interestingly, the effect of obesity on absolute REE is not observed in the 8–10 year-old group, in which serum leptin concentrations correlate with the REE/FFM (r = 0.48; p = 0.011). In conclusion, REE/FFM is not affected by obesity or gender, while the effect of age on absolute REE is gender-dependent and leptin may influence the REE/FFM in 8–10 year-olds.     

Resting Energy Expenditure in Older Inpatients: A Comparison of Prediction Equations and Measurements

Determining energy requirements are an important component of nutritional support for patients with malnutrition; however, the validity of prediction equations for resting energy expenditure (REE) is disputed in older hospitalized patients. We aimed to assess the validity of these equations in older hospitalized patients in Japan. This was a single-center, cross-sectional study of 100 patients aged ≥70 years, hospitalized between January 2020 and December 2021. REE was measured using an indirect calorimeter and was compared to the predicted values calculated from five REE prediction equations. The mean (95% confidence interval) measured REE was 968.1 (931.0, 1005.3) kcal/day, and the mean predicted REE was higher for the FAO/WHO/UNU (1014.3 [987.1, 1041.6] kcal/day, p = 0.164) and Schofield (1066.0 [1045.8, 1086.2] kcal/day, p < 0.001) equations and lower for the Harris-Benedict (898.6 [873.1, 924.1] kcal/day, p = 0.011), Ganpule (830.1 [790.3, 869.9] kcal/day, p < 0.001), and body weight (kg) × 20 (857.7 [821.9, 893.5] kcal/day, p < 0.001) equations. In the age group analysis, none of the predicted values were within a 10% error for more than 80% of patients aged 70–89 years and ≥90 years. The five REE prediction equations did not provide accurate estimates. Validated REE prediction equations need to be developed for older hospitalized patients.     

Resting Energy Expenditure and Related Factors in 6- to 9-Year-Old Southern African Children of Diverse Population Groups

Lower resting energy expenditure (REE) may partially explain the disproportionate prevalence of overweight/obesity among black African women. As no previous studies have investigated the REE of Southern African (South. Afr.) children, we aimed to determine, by sex and population group, the REE of 6- to 9-year-old urban school children. In a cross-sectional study with quota sampling, REE was measured with indirect calorimetry (IC). Confounders considered were: body composition (BC) (fat-free mass (FFM), FFM index, fat mass (FM), FM index), assessed using multifrequency bioelectrical impedance analysis, and physical activity (PA) measured with a pedometer. Multivariate regression was used to calculate REE adjusted for phenotypes (BC, z-scores of weight-for-age, height-for-age, body mass index-for-age) and PA. Sex and population differences in REE were determined with two-way ANOVA. Ninety-four healthy children (59.6% girls; 52.1% black) with similar socioeconomic status and PA opportunities participated. Despite BC variations, sex differences in REE were not significant (41 kcal/day; P = 0.375). The REE of black participants was lower than of white (146 kcal/day; P = 0.002). When adjusted for FFM and HFA z-score, the differences in REE declined but remained clinically meaningful at 91 kcal/day (P = 0.039) and 82 kcal/day (P = 0.108), respectively. We recommend the development of population-specific REE prediction equations for South. Afr. children.     

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.     

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.     

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。     

Validation of the Society of Critical Care Medicine and American Society for Parenteral and Enteral Nutrition Recommendations for Caloric Provision to Critically Ill Obese Patients

Background:The Society of Critical Care Medicine (SCCM) and American Society for Parenteral and Enteral Nutrition (ASPEN) recommend that obese, critically ill patients receive 11–14 kcal/kg/d using actual body weight (ABW) or 22–25 kcal/kg/d using ideal body weight (IBW), because feeding these patients 50%‐70% maintenance needs while administering high protein may improve outcomes. It is unknown whether these equations achieve this target when validated against indirect calorimetry, perform equally across all degrees of obesity, or compare well with other equations. Methods: Measured resting energy expenditure (MREE) was determined in obese (body mass index [BMI] ≥30 kg/m²), critically ill patients. Resting energy expenditure was predicted (PREE) using several equations: 12.5 kcal/kg ABW (ASPEN‐Actual BW), 23.5 kcal/kg IBW (ASPEN‐Ideal BW), Harris‐Benedict (adjusted‐weight and 1.5 stress‐factor), and Ireton‐Jones for obesity. Correlation of PREE to 65% MREE, predictive accuracy, precision, bias, and large error incidence were calculated. Results: All equations were significantly correlated with 65% MREE but had poor predictive accuracy, had excessive large error incidence, were imprecise, and were biased in the entire cohort (N = 31). In the obesity cohort (n = 20, BMI 30–50 kg/m²), ASPEN‐Actual BW had acceptable predictive accuracy and large error incidence, was unbiased, and was nearly precise. In super obesity (n = 11, BMI >50 kg/m²), ASPEN‐Ideal BW had acceptable predictive accuracy and large error incidence and was precise and unbiased. Conclusions: SCCM/ASPEN‐recommended body weight equations are reasonable predictors of 65% MREE depending on the equation and degree of obesity. Assuming that feeding 65% MREE is appropriate, this study suggests that patients with a BMI 30–50 kg/m² should receive 11–14 kcal/kg/d using ABW and those with a BMI >50 kg/m² should receive 22–25 kcal/kg/d using IBW.