Standard equations are not accurate in assessing resting energy expenditure in patients with amyotrophic lateral sclerosis

OBJECTIVE: To assess the utility of standard equations for calculating caloric requirements in patients with amyotrophic lateral sclerosis (ALS). BACKGROUND: Malnutrition substantially increases the risk of death in ALS. Weight loss can be stabilized and survival prolonged with early gastrostomy feeding. However the use of standard nutrition equations has not been validated in this population. We therefore compared measured caloric expenditure to 2 predictive equations in patients with varying stages of ALS. METHODS: Thirty-four patients were studied. Caloric expenditure and respiratory quotient (R) were measured using indirect calorimetry. Results were compared with the Harris-Benedict equation. RESULTS: The prediction error for the Harris-Benedict equation was 18.6 + 14.9%. Limits of agreement showed this equation could overestimate caloric expenditure by 591 kcal/d and underestimate requirements by 677 kcal/d. R was >0.86 in 11 patients, suggesting overfeeding, and <0.8 in 15 patients, suggesting underfeeding. The difference between predicted and measured caloric expenditure did not correlate with disease severity, disease duration, or body mass index. Mechanically ventilated patients had higher than predicted energy expenditure. CONCLUSIONS: We found that standard equations used to calculate energy expenditure were not valid for patients with ALS. Moreover, the majority of our patients were either overfed or underfed. As underfeeding can cause diaphragm impairment, and overfeeding can increase ventilatory load, indirect calorimetry should be considered in ALS patients to determine optimal caloric requirement.     

The challenge of developing a new predictive formula to estimate energy requirements in ventilated critically ill children

Background: Traditionally, energy requirements have been calculated using predictive equations. These methods have failed to calculate energy expenditure accurately. Routine indirect calorimetry has been suggested, but this method is technically demanding and costly. This study aimed to develop a new predictive equation to estimate energy requirements for critically ill children. Methods: This prospective, observational study on ventilated children included patients with an endotracheal tube leak of <10% and fractional inspired oxygen of <60%. An indirect calorimetry energy expenditure measurement was performed and polynomial regression analysis was used to develop new predictive equations. The new formulas were then compared with existing prediction equations. Results: Data from 369 measurements were included in the formula design. Only weight and diagnosis influenced energy expenditure significantly. Three formulas (A, B, C) with an R (2) > 0.8 were developed. When we compared the new formulas with commonly used equations (Schofield, Food and Agriculture Organization/World Health Organization/United Nations University, and White equation), all formulas performed very similar, but the Schofield equation seemed to have the lowest SD. Conclusions: All 3 new pediatric intensive care unit equations have R (2) values of >0.8; however, the Schofield equation still performed better than other predictive methods in predicting energy expenditure in these patients. Still, none of the predictive equations, including the new equations, predicted energy expenditure within a clinically accepted range, and further research is required, particularly for patients outside the technical scope of indirect calorimetry.     

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

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.     

Energy expenditure in children with severe head injury: lack of agreement between measured and estimated energy expenditure.

BACKGROUND: The purpose of this study was to test the hypotheses that estimates of resting energy expenditure (REE) vary significantly from measured energy expenditure in a population of head-injured children and are not accurate for use in determining nutrition needs in this population. METHODS: This is a retrospective study of 30 children with severe head injury, with Glasgow Coma Scale (GCS) score of <8 and needing mechanical ventilation. Measured REE was obtained using indirect calorimetry. Estimated REEs were calculated using Harris-Benedict, World Health Organization (WHO), Schofield, and White formulas. Severity of illness was calculated using Pediatric Risk of Mortality (PRISM) score. Agreement between measured REE and estimated REE was tested using the Bland-Altman method. Correlation coefficient between PRISM score and measured REE was calculated using Spearman test. RESULTS: More than half of the estimates of REE differed from measured REE by >10%. Significant disagreement between estimated REE and measured REE was demonstrated using the Bland-Altman method. There was no correlation between severity of illness and measured REE to explain the inaccuracies of REE estimates. CONCLUSION: Energy expenditure in critically ill children cannot be estimated accurately; hence, nutrition for critically ill children with head injury should be provided according to measurement of REE to avoid the consequences of overfeeding or malnutrition.     

Energy expenditure is very high in extremely obese women

To test the hypothesis that total energy expenditure (TEE) and resting energy expenditure (REE) are low in extremely obese individuals, factors that could contribute to maintenance of excess weight, a cross-sectional study was conducted in 30 weight stable, extremely obese women [BMI (mean +/- SEM) 48.9 +/- 1.7 kg/m(2)]. TEE was measured over 14 d using the doubly labeled water method, REE and the thermic effect of feeding (TEF) were measured using indirect calorimetry, and activity energy expenditure (AEE) was calculated as TEE - (REE + TEF). Body composition was determined using a 3-compartment model. Subjects were divided into tertiles of BMI (37.5-45.0; 45.1-52.0; and 52.1-77.0 kg/m(2)) for data analysis. TEE and REE increased with increasing BMI tertile: TEE, 12.80 +/- 0.5, 14.67 +/- 0.5, and 16.10 +/- 0.9 MJ/d (P < 0.01); REE, 7.87 +/- 0.2, 8.78 +/- 0.3, and 9.94 +/- 0.6 MJ/d (P < 0.001), and these values were 29-38% higher than published means of measured TEE in nonobese individuals. No significant differences were observed among BMI tertiles for AEE, TEF, or physical activity level (PAL = TEE/REE, overall mean 1.64 +/- 0.16). The Harris-Benedict and WHO equations provided the closest estimates of REE (within 3%), whereas the obese-specific equations of Ireton-Jones overpredicted (40%) and Bernstein underpredicted (21%) REE. Extremely obese individuals have high absolute values for TEE and REE, indicating that excess energy intake contributes to the maintenance of excess weight. Standard equations developed for nonobese populations provided the most accurate estimates of REE for the obese individuals studied here. REE was not accurately predicted by equations developed in obese populations.     

Resting energy expenditure is lower than predicted in people taking atypical antipsychotic medication

Resting energy expenditure (REE) is lower than predicted in persons taking atypical antipsychotic medication, and weight management is a significant clinical challenge for some of them. However, to date there have been no published guidelines to assist clinicians in choosing appropriate prediction equations to estimate energy expenditure in persons taking atypical antipsychotic medications. The objectives of this study were to measure REE in a group of men taking the atypical antipsychotic clozapine and to determine whether REE can be accurately predicted for this population using previously published regression equations. REE was measured using indirect calorimetry via a ventilated hood on eight men who had completed at least 6 months of treatment with clozapine. Comparisons between measured REE and predicted REE using five different equations were undertaken. The commonly-used Harris-Benedict and Schofield equations systematically overestimated REE. Predictions of REE from other equations were too variable for clinical use. When estimating energy requirements as part of a weight-management program in men who have been taking clozapine for 6 months, predictions of REE from the equations of Harris-Benedict and Schofield should be reduced by 280 kcal/day.     

An evaluation of resting energy expenditure in hospitalized, severely underweight patients.

A prospective trial was conducted with 14 hospitalized patients who were severely underweight with a mean weight of 40.9+/-5.1 kg and 70.7+/-7.8% of ideal body weight, to compare estimates of resting energy expenditure (REE) with measured values. The 9 women and 3 men, whose mean age was 66.5+/-13.9 y, underwent nutritional assessment and measurement of their REE by indirect calorimetry using the Sensormedics Deltatrac MBM100 indirect calorimeter. Their REE was also estimated by the Harris-Benedict formula (mean 1032+/-66 kcal/d) as well as a previously established empirical formula where REE = 25 x body weight in kg (mean 1023+/-129 kcal/d). Results by both estimates were significantly lower than the measured resting energy expenditure (MREE) in this group of patients (P<0.0001). The percentage difference between MREE and estimated REE by the Harris-Benedict formula was 18.4+/-9.4% and 20.9+/-7.5% by the empirical formula. The MREE exceeded the estimated REE in each individual. The correlation between MREE and body weight (r2 = 0.558, r = 0.005) was better than that between MREE and estimated REE by Harris-Benedict formula (r2 = 0.275, P = 0.08) suggesting that weight was the principal determinant rather than the other components (height, age, sex) of the Harris-Benedict formula. Our data shows that commonly employed formulae routinely underestimate the energy needs of severely underweight patients below 50 kg in body weight. The Harris-Benedict equation had limited predictive value for the individual, explaining approximately 25% of the variance in energy expenditure. Given the particular importance of matching energy intake to needs in this group of patients with limited reserves, many of whom are critically ill, we suggest an empirical equation using 30-32 kcal/kg be used to estimate the energy requirements of severely underweight patients when direct measurements are unavailable or clinically less imperative.     

Comparison of energy prediction equations with measured resting energy expenditure in children with sickle cell anemia

OBJECTIVE: To determine the accuracy of energy prediction equations when compared with measured resting energy expenditure (REE) in children with sickle cell anemia. To develop a modified equation that more accurately estimates the energy needs of children with sickle cell anemia and to cross-validate these on a different set of patients (test patients). DESIGN: REE was measured in children using indirect calorimetry and compared with predicted values using the Harris-Benedict and the Food and Agriculture Organization/World Health Organization/United Nations University equations (WHO). SUBJECTS/SETTING: Eighteen patients participated in the original sample that compared predicted with measured energy expenditure. The modified equations were developed using the original 18 patients. A test population of 20 different patients was used to validate the modified equations. STATISTICAL ANALYSIS: Wilcoxon signed-rank test was performed to compare measured with predicted REE. The correlation analysis method and multiple linear regression method were used to develop 2 modified versions for the Harris-Benedict and WHO prediction equations. RESULTS: When compared with the mean predicted REE using the Harris-Benedict and WHO equations, the mean measured REE was 14% and 12% greater than both (P=.005 and P=.014, respectively). Two modified equations were developed from the Harris-Benedict and WHO equations. Based on the data from the test patients, the mean measured REE was 15% greater than the mean predicted REE based on the Harris-Benedict and WHO equations (P=.0001 for both). When the modified Harris-Benedict and WHO equations were used, there was almost no difference in the mean measured REE and the mean predicted REE (mean difference using Harris-Benedict = 14, P = .9273; mean difference using WHO = -13, P = .6215). CONCLUSION: Both energy prediction equations underestimated REE in children with sickle cell anemia. The 2 modified versions of the energy prediction equations that we propose predicted the energy needs of these children much more accurately; however, the modified equations need to be validated through application to other children with sickle cell anemia.     

Indirect Calorimetry Reveals That Better Monitoring of Nutrition Therapy in Pediatric Intensive Care Is Needed

Background: Monitoring nutrition therapy is essential in the care of critically ill children, but the risk of nutrition failure seems to remain. The aims of the present study were to examine the prevalence of underfeeding, adequate feeding, and overfeeding in mechanically ventilated children and to identify barriers to the delivery of nutrition support. Materials and Methods: Children aged 0–14 years who fulfilled the criteria for indirect calorimetry were enrolled in this prospective, observational study and were studied for up to 5 consecutive days. Actual energy intake was recorded and compared with the required energy intake (measured energy expenditure plus 10%); energy intake was classified as underfeeding (<90% of required energy intake), adequate feeding (90%?110%), or overfeeding (>110%). The reasons for interruptions to enteral and parenteral nutrition were recorded. Results: In total, 104 calorimetric measurements for 140 total days were recorded for 30 mechanically ventilated children. Underfeeding, adequate feeding, and overfeeding occurred on 21.2%, 18.3%, and 60.5% of the 104 measurement days, respectively. There was considerable variability in the measured energy expenditure between children (median, 37.2 kcal/kg/d; range, 16.81?66.38 kcal/kg/d), but the variation within each child was small. Respiratory quotient had low sensitivity of 21% and 27% for detecting underfeeding and overfeeding, respectively. Fasting for procedures was the most frequent barrier that led to interrupted nutrition support. Conclusion: The high percentage of children (~61%) who were overfed emphasizes the need to measure energy needs by using indirect calorimetry.     

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 over-predictions. 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.     

Validation of a new method for estimating resting energy expenditure of non-ambulatory tube-fed patients with severe neurodevelopmental disabilities

OBJECTIVE: We assessed the bias and precision of the Arlington Developmental Center (ADC) equations derived from our previous study and the Harris-Benedict equations for estimating resting energy expenditure in non-ambulatory, tube-fed patients with severe neurodevelopmental disabilities. METHODS: Fifteen non-ambulatory patients with neurodevelopmental disabilities referred to the nutrition consult service for evaluation of enteral tube feeding via a permanent ostomy who had a steady-state resting energy expenditure measurement performed by indirect calorimetry were included in the study. The predicted energy expenditure values were compared with the measured resting energy expenditure values and evaluated for bias and precision. RESULTS: Both ADC equations were more precise (95% confidence interval [CI]: 9-22% and 10-18% error, respectively) for the total population than the Harris-Benedict equations (95% CI: 17-40% error). The ADC-2 equation was precise (95% CI: 7-15% error) and unbiased (95% CI: -5 to 139 kcal/d) in contrast to the Harris-Benedict equations (95% CI: 23-54% error; bias, +230 to 365 kcal/d) for patients with cerebral palsy and fixed upper extremity contractures. The Harris-Benedict equations were precise and unbiased (95% CI: 3-14% error; bias, -182 to 39 kcal/d) for patients with cerebral palsy with preservation of upper body movement, whereas the ADC equations were biased toward underprediction and associated with greater error (95% CI: -367 to -73 kcal/d and 7-26% error; 95% CI: -379 to -109 kcal/d and 9-27% error, respectively). CONCLUSIONS: The ADC-2 equation was unbiased and more precise in non-ambulatory adult patients with severe neurodevelopmental disabilities and fixed upper extremity contractures, whereas the Harris-Benedict equations were more precise and unbiased for those with preservation of limited functional and non-functional upper extremity movement.     

Adequate feeding and the usefulness of the respiratory quotient in critically ill children

OBJECTIVE: We determined incidences of underfeeding and overfeeding in children who were admitted to a multidisciplinary tertiary pediatric intensive care and evaluated the usefulness of the respiratory quotient (RQ) obtained from indirect calorimetry to assess feeding adequacy. METHODS: Children 18 y and younger who fulfilled the criteria for indirect calorimetry entered our prospective, observational study and were studied until day 14. Actual energy intake was recorded, compared with required energy intake (measured energy expenditure plus 10%), and classified as underfeeding (<90% of required), adequate feeding (90% to 110% of required), or overfeeding (>110% of required). We also evaluated the adequacy of a measured RQ lower than 0.85 to identify underfeeding, and an RQ higher than 1.0 to identify overfeeding. RESULTS: Ninety-eight children underwent 195 calorimetric measurements. Underfeeding, adequate feeding, and overfeeding occurred on 21%, 10%, and 69% of days, respectively. An RQ lower than 0.85 to identify underfeeding showed low sensitivity (63%), high specificity (89%), and high negative predictive value (90%). An RQ higher than 1.0 to indicate overfeeding showed poor sensitivity (21%), but a high specificity (97%) and a high positive predictive value (93%). Food composition, notably high-carbohydrate intake, was responsible for an RQ exceeding 1.0 in the overfed group. CONCLUSION: Children admitted to the intensive care unit receive adequate feeding on only 10% of measurement days during the first 2 wk of admission. The usefulness of RQ to monitor feeding adequacy is limited to identifying (carbohydrate) overfeeding and excluding underfeeding.     

Measured versus predicted energy expenditure in children with inactive Crohn's disease

INTRODUCTION: The aim of the study was to assess if the estimated average requirements for energy for normal children (EAR) and the Schofield equation could reliably predict energy requirements in children with inactive Crohn's disease (CD). METHODS: Twenty-three children with inactive CD were studied, median age 14.3 years (range 7.8-16.9). Resting energy expenditure (REE) was measured by indirect calorimetry and compared with that predicted using the Schofield equation (BMR). Total energy expenditure (TEE) was measured using REE and a 3-day activity diary and compared with EAR. RESULTS: REE ranged from 79% to 136% of BMR. Mean REE was not significantly greater than mean BMR (P=0.25 2-tailed t-test). TEE ranged from 72% to 163% of estimated average requirements for energy for children of that weight (EARw). EARw tended to underestimate TEE in large children and overestimate TEE in small children (Bland-Altman plot R=0.5, P=0.002). EARw was a poor predictor of TEE (R=0.35, P=0.1). EAR underestimated energy requirements by >500 kcal/day in 40% of the children. CONCLUSIONS: The Schofield equation and EAR are unreliable methods of predicting total energy requirements in children with inactive CD with a significant potential to underestimate energy needs. When energy requirements were greater than EAR it was due to physical activity and body habitus rather than raised REE.     

Predicting resting energy expenditure in patients with musculoskeletal deformities

The aim of this study was to assess the validity of the commonly used equations (Harris-Benedict (HB), Schofield (S) and equations based on midarm circumference (MAC) and midarm muscle circumference (MAMC) in predicting resting energy expenditure (REE) in a population of patients with musculoskeletal deformities. 20 kyphoscoliotic patients (15 female (F); 5 male (M); mean age 59.6 years) and 10 controls (7 F; 3M; 59.8 years) were studied. REE measured by indirect calorimetry (IC) with a ventilated canopy system (Deltatrac metabolic monitor) was not significantly different between patients and controls (Mean (SD) REE (MJ/24 h): Patients: 5.48 (1.1); controls: 5.28(0.8)). In patients with deformities the Schofield equation gave values which were closest to measured REE (mean difference and limits of agreement IC vs S: 0.098 MJ/24 h; -0.822 and 1.018). The Harris-Benedict equation using height (Ht) and armspan (AS) in lieu of height also gave acceptable results (IC vs HB (Ht): 0.34; -0.638 and 1.318; IC vs HB (AS): 0.255; -0.683 and 1.253). Equations based on MAC and MAMC compared poorly (IC vs MAC equation: 0.398; -1.530 and 2.326; IC vs MAMC equation 0.687; -0.911 and 2.285). On regression analysis the equation REE = 0.295 (MAMC) + 0.0483 (AS) -0.0324 (age) -6.25 predicted REE best in the patient population (r(2) = 0.861).     

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.     

Place de la calorimétrie indirecte et des formules estimant la dépense énergétique des malades de réanimation

Energy expenditure in critically ill patients is variable according to initial injury, severity, nutritional status and comorbidities. Hypermetabolism progressively increases over the first week following the onset of sepsis. And many other determinants of energy expenditure (pharmacotherapy, mechanical ventilation, nutrition) are described. It is very difficult to estimate energy expenditure daily in critically ill patients. Of importance, a cumulative negative caloric balance of more than 10 000 kcal is associated with a worsened outcome in severely ill patients. And overfeeding could also affect outcome in intensive care units. Predictive equations for estimating energy requirements were developed with anthropometric parameters, like Harris-Benedict equation, or with specific or ventilatory parameters, like Ireton-Jones equation. Finally, indirect calorimetry is considered to be the gold standard for determining energy expenditure in individuals. It could be used for “extreme” situations like in morbidly obese, in post-injury period, or with new therapeutics. However, complex access to indirect calorimetry restricts its use. International guidelines (caloric requirements between 20–30 kcal/kg BW per day) could be applied in 75 % of our patients.     

Measured energy expenditure of tube-fed patients with severe neurodevelopmental disabilities

OBJECTIVE: To determine measured resting energy expenditure (REE) of nonambulatory tube-fed patients with severe neurological neurodevelopmental disabilities. METHODS: Twenty patients were prospectively studied. Only steady state indirect calorimetry measurements were taken. All measurements were conducted using a canopy system. Nutritional needs were met entirely by enteral feedings via a permanent ostomy. RESULTS: REE was widely distributed from 16 kcals/kg/day to 39 kcals/kg/day. The mean REE (888+/-176 kcals/day) of the patients was significantly (p<0.01) lower than predicted as estimated by the Harris-Benedict equations (1081+/-155 kcals/day) and World Health Organization equations (1194+/-167 kcals/day). Fat-free mass (FFM) was the best parameter for predicting REE. Two predictive equations were developed that are not significantly biased and more precise (< or =15% error) than conventional predictive formulas. CONCLUSION: Conventional formulas for estimating energy expenditure are inaccurate and generally overestimate measured energy expenditure of nonambulatory patients with severe developmental disabilities.     

Prediction of resting energy requirements in people taking weight-inducing antipsychotic medications

Aim: The purpose of the present study was to determine whether the regression equations published by Mifflin are suitable for the prediction of resting metabolic rate in people taking weight-inducing antipsychotic medications. Methods: Resting metabolic rate was measured using indirect calorimetry in 45 people with psychotic illnesses who had been taking atypical antipsychotic medications for more than six months. Predicted resting metabolic rate was calculated using the Mifflin, Harris-Benedict and Schofield equations. The limits of agreement method were used to compare measured and predicted resting metabolic rate. Results: In men, the prediction equations significantly overestimated resting metabolic rate. There was a systematic variation in the bias using the Mifflin equation. The limits of agreement using the Harris-Benedict and Schofield equations were 2793 and 3014 kJ/day, respectively. In women, predictions of resting metabolic rate using the Harris-Benedict equation had the lowest limits of agreement (1008 kJ/day) compared with 1978 kJ for the Mifflin and 3157 kJ/day for the Schofield equations, respectively. Conclusion: The present study does not support the recent suggestion that the Mifflin equation is the most suitable prediction equation to determine resting metabolic rate in people taking antipsychotic medications. In men, it may be advisable to reduce the estimate provided using the Harris-Benedict or Schofield equations by 840 kJ/day. The Harris-Benedict equation provided the ‘best estimate’ of resting energy requirements in women taking antipsychotic medications.