Predictive Equations For Resting Energy Expenditure Research Articles

Reliability of energy expenditure prediction equations in the weight management clinic

Few weight management clinics have access to indirect calorimetry with which to measure energy expenditure. Instead, they use energy expenditure prediction equations, which were not designed for use in obesity. We aimed to establish the extent to which such equations overestimate and underestimate resting energy expenditure (REE) in overweight and obese individuals. We compared the Schofield, Harris & Benedict, James & Lean and World Health Organisation (WHO) REE prediction equations with the clinical gold standard of indirect calorimetry in 28 males and 168 females, with a mean (SD) age of 28.9 (6.4) years and body mass index (BMI) of 19-67 kg m(-2). The mean REE estimated by indirect calorimetry, and the Schofield, Harris & Benedict, James & Lean and WHO equations were 8.09, 8.30, 8.09, 8.37 and 8.23 MJ day(-1) (1934, 1983, 1933, 2001 and 1966 kcal day(-1)), respectively. Although rising BMI exerted only a small effect on the mean differences between indirect calorimetry and the predicted REE [Schofield: +272 kJ (+65 kcal)/10 units BMI, P = 0.02; Harris & Benedict: +42 kJ (+10 kcal)/10 units BMI, P = 0.69; James & Lean: +217 kJ (+52 kcal) 10 units BMI, P = 0.06 and WHO: +42 kJ (+10 kcal) BMI, P = 0.11], the variance among overweight and obese patients of BMI >25 was substantially higher compared to that among normal weight subjects of BMI <25, on whom the equations were based. The estimated REE by Schofield for an individual of BMI 35 kg m(-2), for example, could lie anywhere from 2.78 MJ (661 kcal) above the indirect calorimetry value to 2.59 MJ (618) kcal below it. Prediction equations offer a quick assessment of energy needs for hypocaloric diets although, in reality, they run the random risk of excessive restriction or further weight gain.

How Accurate Are Resting Energy Expenditure Prediction Equations in Obese Trauma and Burn Patients?

While the prevalence of obesity continues to increase in our society, outdated resting energy expenditure (REE) prediction equations may overpredict energy requirements in obese patients. Accurate feeding is essential since overfeeding has been demonstrated to adversely affect outcomes. The first objective was to compare REE calculated by prediction equations to the measured REE in obese trauma and burn patients. Our hypothesis was that an equation using fat-free mass would give a more accurate prediction. The second objective was to consider the effect of a commonly used injury factor on the predicted REE. A retrospective chart review was performed on 28 patients. REE was measured using indirect calorimetry and compared with the Harris-Benedict and Cunningham equations, and an equation using type II diabetes as a factor. Statistical analyses used were paired t test, +/-95% confidence interval, and the Bland-Altman method. Measured average REE in trauma and burn patients was 21.37 +/- 5.26 and 21.81 +/- 3.35 kcal/kg/d, respectively. Harris-Benedict underpredicted REE in trauma and burn patients to the least extent, while the Cunningham equation underpredicted REE in both populations to the greatest extent. Using an injury factor of 1.2, Cunningham continued to underestimate REE in both populations, while the Harris-Benedict and Diabetic equations overpredicted REE in both populations. The measured average REE is significantly less than current guidelines. This finding suggests that a hypocaloric regimen is worth considering for ICU patients. Also, if an injury factor of 1.2 is incorporated in certain equations, patients may be given too many calories.

New resting energy expenditure prediction equations for patients with rheumatoid arthritis

Resting energy expenditure (REE), one of the main components of total energy expenditure, can be measured via indirect calorimetry and/or predicted from equations. The latter may be misleading in RA, as they do not take into account the metabolic alterations occurring in RA. The objectives of this study are to evaluate the accuracy of widely used REE-predictive equations in RA patients against measured REE and to develop RA-specific equations. We assessed REE (via indirect calorimetry and several predictive equations), fat-free mass (FFM; via bioelectrical impedance) and disease activity (CRP) in RA patients and healthy controls. Data from 60 RA patients (experimental group) were used to assess the accuracy of existing REE equations and to develop new equations. The new equations were validated in an independent cross-validation group of 22 RA patients. These two groups were merged and two final equations were developed. All equations significantly under-predicted measured REE (from 15% to 18.2%, all at P < 0.001) in the RA experimental group, but not in the control group. After both equations demonstrated a high validity in the cross-validation group, the new final REE prediction equations developed from the total RA sample (n = 82) were: Model 1: REE (kcal/day) = 126.1 x FFM(0.638) x CRP(0.045) (R(2) = 0.70) and Model 2: REE (kcal/day) = 598.8 x weight(0.47) x age(-0.29) x CRP(0.066) (R(2) = 0.62). The new equations provide an accurate prediction of REE in RA patients and could be used for clinical monitoring of resting metabolism of these patients without the requirement for specialized personnel.

Accurate Determination of Energy Needs in Hospitalized Patients

Objective To evaluate the accuracy of seven predictive equations, including the Harris-Benedict and the Mifflin equations, against measured resting energy expenditure (REE) in hospitalized patients, including patients with obesity and critical illness. Design A retrospective evaluation using the nutrition support service database of a patient cohort from a similar timeframe as those used to develop the Mifflin equations. Subjects/Setting All patients with an ordered nutrition assessment who underwent indirect calorimetry at our institution over a 1-year period were included. Intervention Available data was applied to REE predictive equations, and results were compared to REE measurements. Main Outcome Measures Accuracy was defined as predictions within 90% to 110% of the measured REE. Differences >10% or 250 kcal from REE were considered clinically unacceptable. Statistical Analyses Performed Regression analysis was performed to identify variables that may predict accuracy. Limits-of-agreement analysis was carried out to describe the level of bias for each equation. Results A total of 395 patients, mostly white (61%) and African American (36%), were included in this analysis. Mean age±standard deviation was 56±18 years (range 16 to 92 years) in this group, and mean body mass index was 24±5.6 (range 13 to 53). Measured REE was 1,617±355 kcal/day for the entire group, 1,790±397 kcal/day in the obese group (n=51), and 1,730±402 kcal/day in the critically ill group (n=141). The most accurate prediction was the Harris-Benedict equation when a factor of 1.1 was multiplied to the equation (Harris-Benedict 1.1), but only in 61% of all the patients, with significant under- and overpredictions. In the patients with obesity, the Harris-Benedict equation using actual weight was most accurate, but only in 62% of patients; and in the critically ill patients the Harris-Benedict 1.1 was most accurate, but only in 55% of patients. The bias was also lowest with Harris-Benedict 1.1 (mean error −9 kcal/day, range +403 to −421 kcal/day); but errors across all equations were clinically unacceptable. Conclusions No equation accurately predicted REE in most hospitalized patients. Without a reliable predictive equation, only indirect calorimetry will provide accurate assessment of energy needs. Although indirect calorimetry is considered the standard for assessing REE in hospitalized patients, several predictive equations are commonly used in practice. Their accuracy in hospitalized patients has been questioned. This study evaluated several of these equations, and found that even the most accurate equation (the Harris-Benedict 1.1) was inaccurate in 39% of patients and had an unacceptably high error. Without knowing which patient’s REE is being accurately predicted, indirect calorimetry may still be necessary in difficult to manage hospitalized patients.

Influence of methods used in body composition analysis on the prediction of resting energy expenditure

There are considerable differences in published prediction algorithms for resting energy expenditure (REE) based on fat-free mass (FFM). The aim of the study was to investigate the influence of the methodology of body composition analysis on the prediction of REE from FFM. In a cross-sectional design measurements of REE and body composition were performed. The study population consisted of 50 men (age 37.1+/-15.1 years, body mass index (BMI) 25.9+/-4.1 kg/m2) and 54 women (age 35.3+/-15.4 years, BMI 25.5+/-4.4 kg/m2). REE was measured by indirect calorimetry and predicted by either FFM or body weight. Measurement of FFM was performed by methods based on a 2-compartment (2C)-model: skinfold (SF)-measurement, bioelectrical impedance analysis (BIA), Dual X-ray absorptiometry (DXA), air displacement plethysmography (ADP) and deuterium oxide dilution (D2O). A 4-compartment (4C)-model was used as a reference. When compared with the 4C-model, REE prediction from FFM obtained from the 2C methods were not significantly different. Intercepts of the regression equations of REE prediction by FFM differed from 1231 (FFM(ADP)) to 1645 kJ/24 h (FFM(SF)) and the slopes ranged between 100.3 kJ (FFM(SF)) and 108.1 kJ/FFM (kg) (FFM(ADP)). In a normal range of FFM, REE predicted from FFM by different methods showed only small differences. The variance in REE explained by FFM varied from 69% (FFM(BIA)) to 75% (FFM(DXA)) and was only 46% for body weight. Differences in slopes and intercepts of the regression lines between REE and FFM depended on the methods used for body composition analysis. However, the differences in prediction of REE are small and do not explain the large differences in the results obtained from published FFM-based REE prediction equations and therefore imply a population- and/or investigator specificity of algorithms for REE prediction.

Prediction equations for resting energy expenditure in overweight and normal-weight black and white children

Accurate estimation of children's resting energy expenditure (REE) is important for planning dietary therapy. Our objective was to compare the utility of 5 REE prediction equations in a diverse sample of young children. REE was obtained in 502 black and white girls and boys aged 6-11 y by using indirect calorimetry at 4 US sites. Measured REE and REE predicted from the equations were compared. None of the equations provided both accurate and unbiased estimates of REE. Two new sets of sex-specific equations including race as a factor were generated and evaluated. One set used easily measured variables-females: REE = 0.046 x weight - 4.492 x 1/height(2) - 0.151 x race + 5.841; males: REE = 0.037 x weight - 4.67 x 1/height(2) - 0.159 x race + 6.792-and accounted for 72% and 69%, respectively, of REE variance. The other set used body-composition variables-females: REE = 0.101 x fat-free mass + 0.025 x fat mass + 0.293 x height(3) - 0.185 x race + 1.643; males: REE = 0.078 x fat-free mass + 0.026 x fat mass - 2.646 x 1/height(2) - 0.244 x race + 4.8-and accounted for 75% and 71%, respectively, of REE variance. When split by race and adiposity, the small bias generated could be corrected to within 0.25 MJ (60 kcal) of the mean measured value. Sex-specific equations must take race into account to predict REE adequately in children.

The agreement between measured and predicted resting energy expenditure in patients with pancreatic cancer — A pilot study

To compare measured resting energy expenditure to resting energy expenditure predicted from eight published prediction equations in a sample of patients with pancreatic cancer. Cross-sectional study. Ambulatory patients of a tertiary private hospital. Eight patients with pancreatic cancer (5 males, 3 females; age: 62.0+/- 5.2 years; BMI: 24.4+/- 3.2 kg/m2; weight loss: 12.1+/- 6.0%; mean+/- SD). Resting energy expenditure was measured using indirect calorimetry and predicted from eight published prediction methods (Harris-Benedict with no injury factor, Harris-Benedict with 1.3 injury factor, Schofield, Owen, Mifflin, Cunningham, and Wang equations and the 20 kcal/kg ratio). Body composition was assessed by deuterium oxide dilution technique. Statistical analysis was performed by using the method of Bland and Altman, and the Student's t-test. The Harris-Benedict equations with an injury factor of 1.3 resulted in a significantly higher mean predicted resting energy expenditure compared to measured resting energy expenditure, while there was no significant difference between mean measured and predicted resting energy expenditure and the other 7 methods. At an individual level, the limits of agreement are wide for all equations. The best combination of low bias and narrowest limits of agreement was observed in the prediction of resting energy expenditure from the Wang equation (based on fat free mass) and the Harris-Benedict equation (based on weight and height). At a group level, there is agreement between mean measured and predicted resting energy expenditure with the exception of the Harris-Benedict equation with an injury factor of 1.3. The results of this pilot study suggest that, for an individual, the limits of agreement are wide, and clinically important differences in resting energy expenditure would be obtained. Clinicians need to be aware of the limitations of the use of resting energy expenditure prediction equations for individuals.

Use of bioelectrical impedance analysis to predict energy expenditure of hospitalized patients receiving nutrition support.

The energy requirement of a patient receiving nutrition support is typically estimated by calculating the basal energy expenditure (BEE) using the Harris-Benedict equations and multiplying by stress and activity factors. Because fat-free mass (FFM) and fat mass (FM) are important determinants of BEE, we hypothesized that body composition estimates derived from bioelectrical impedance analysis (BIA) could be used to develop predictive equations for resting energy expenditure (REE) that were more accurate than those calculated using the Harris-Benedict equations. Seventy-six adults referred to the nutrition support service were studied. REE was measured by indirect calorimetry, and single-frequency BIA was used to estimate FFM and FM. Using the first 20 male and 20 female patients, predictive equations for REE were developed by multiple regression analysis, using BIA-derived body composition values, age, and gender. The next 36 patients were used to compare the accuracy of these equations with the Harris-Benedict equations in estimating REE. Using BLA-derived body composition values, gender, and age, predictive equations were developed for REE that explained approximately 65% of the variance. Inclusion of other BIA or anthropometric parameters did not improve the equations. When compared with the Harris-Benedict equations, the equations developed in this study were significantly more accurate, providing an REE estimate that was closer to the measured value in about 75% of patients. These results indicate that BLA-derived body composition estimates may be used to more accurately predict the energy requirements of patients receiving nutrition support than calculations based on the Harris-Benedict equations.

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, R 2<.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.