Resting energy expenditure in clinical pediatrics: Measured versus prediction equations

Kaplan, Aaron S.; Zemel, Babette S.; Neiswender, Kristin M.; Stallings, Virginia A.

doi:10.1016/s0022-3476(95)70295-4

Cited by 87 publications

(48 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This observation has also been noted in other studies (Bandini et al, 1990, andco-workers 1995;Dietz et al, 1991;Firouzbaksh et al, 1993;Kaplan et al 1995). However, (Molna Âr 1992(Molna Âr , 1995 found that the FAOaWHOaUNU equations overestimated BMR in their sample of adolescents, while Brown et al (1996) showed that in 9-and 10-year-old boys predictions were inaccurate in pre-pubertal boys.…”

Section: Basal Metabolic Rate In Children Cjk Henry Et Alsupporting

confidence: 76%

New equations to estimate basal metabolic rate in children aged 10–15 years

1999

View full text Add to dashboard Cite

Objective: To develop new equations for the estimation of basal metabolic rate in children aged 10 ± 15 years, and to evaluate the impact of including pubertal stage into the equations. Design: Mixed longitudinal. Setting: The children were recruited from schools in Oxford, and the measurements were made in the schools. Subjects: 195 school children, aged 10 ± 15 years, were recruited in three cohorts. The gender distribution of the subjects was 40% boys and 60% girls. Methods: Basal metabolic rate (BMR) was measured, by indirect calorimetry, at 6-monthly intervals for 3 years. Anthropometric data, height, weight, body breadths and skinfold measurements (biceps, triceps, subscapular, suprailiac and medial calf) were collected on each occasion. Fat and fat-free mass was calculated from the skinfold measurements. Pubertal development was also assessed on annually by paediatricians. Pubic hair (PH) and gonad (G) development was assessed in boys and breast (B) development in girls. The girls were questioned about menarche. Stepwise multiple regression analysis was used to develop and assess new formulae for BMR that also incorporate pubertal development. 554). Weight was the most important factor in developing the regression equations for the calculation of BMR in both sexes (R 2 0.61 and 0.52 for boys and girls, respectively). Stepwise multiple regression analyses, with independent variables such as gender, weight, height, puberty stage and skinfolds, allowed several BMR regression equations to be developed. The inclusion of the menarche status in the regression equations signi®cantly (P`0.05) improved BMR estimation in the pre-menarche girls. Boys, pubertal stage as assessed by Pubic Hair (PH) and Gonadal Stage (G) did not contribute to a signi®cant improvement in BMR estimation, except for 11-year-olds. Conclusions: The inclusion of pubertal stage afforded only minor improvements in the derivation of regression equations for the estimation of BMR of children aged between 10 and 15 years.

show abstract

Section: Basal Metabolic Rate In Children Cjk Henry Et Alsupporting

confidence: 76%

New equations to estimate basal metabolic rate in children aged 10–15 years

1999

View full text Add to dashboard Cite

show abstract

“…There have been a number of published studies of SMR measurements in healthy infants in this age group (Thompson et al, 1995;Wells et al, 1996) and in infants with chronic disease (Kaplan et al, 1995;Thompson et al, 1995;Sentongo et al, 2000). Our study confirms and extends these studies which found that none of the prediction equations could be relied upon to give an accurate estimate of actual metabolic rate although the Schofield weight and height equation gives on average the best estimate.…”

Section: Comparison Of Smr and Bmr Ca Reichman Et Alsupporting

confidence: 76%

Comparison of measured sleeping metabolic rate and predicted basal metabolic rate during the first year of life: evidence of a bias changing with increasing metabolic rate

et al. 2002

View full text Add to dashboard Cite

Objective: To compare measurements of sleeping metabolic rate (SMR) in infancy with predicted basal metabolic rate (BMR) estimated by the equations of Schofield. Methods: Some 104 serial measurements of SMR by indirect calorimetry were performed in 43 healthy infants at 1.5, 3, 6, 9 and 12 months of age. Predicted BMR was calculated using the weight only (BMR-wo) and weight and height (BMR-wh) equations of Schofield for 0 -3-y-olds. Measured SMR values were compared with both predictive values by means of the Bland -Altman statistical test. Results: The mean measured SMR was 1.48 MJ=day. The mean predicted BMR values were 1.66 and 1.47 MJ=day for the weight only and weight and height equations, respectively. The Bland -Altman analysis showed that BMR-wo equation on average overestimated SMR by 0.18 MJ=day (11%) and the BMR-wh equation underestimated SMR by 0.01 MJ=day (1%). However the 95% limits of agreement were wide: 7 0.64 to þ 0.28 MJ=day (28%) for the former equation and 7 0.39 to þ 0.41 MJ=day (27%) for the latter equation. Moreover there was a significant correlation between the mean of the measured and predicted metabolic rate and the difference between them. Conclusions: The wide variation seen in the difference between measured and predicted metabolic rate and the bias probably with age indicates there is a need to measure actual metabolic rate for individual clinical care in this age group.

show abstract

“…Several prediction equations have been developed that are more convenient in clinical practice (Firouzbakhsh et al, 1993;Bandini et al, 1995;Kaplan et al, 1995). The FAO/WHO/UNU equations result from a study of more than 7500 people 3-18 years of age (Firouzbakhsh et al, 1993;Bandini et al, 1995;Kaplan et al, 1995). The reasons for the discrepancy between measured and estimated REE in our population is not clear.…”

Section: Discussionmentioning

confidence: 99%

“…Indirect calorimetry is useful in clinical research, but it is time consuming and requires rigorous procedure. For these reasons, indirect calorimetry is not convenient for the evaluation of energy requirements in clinical practice (Firouzbakhsh et al, 1993;Bandini et al, 1995;Kaplan et al, 1995). Several prediction equations have been developed that are more convenient in clinical practice (Firouzbakhsh et al, 1993;Bandini et al, 1995;Kaplan et al, 1995).…”

Section: Discussionmentioning

confidence: 99%

“…For these reasons, indirect calorimetry is not convenient for the evaluation of energy requirements in clinical practice (Firouzbakhsh et al, 1993;Bandini et al, 1995;Kaplan et al, 1995). Several prediction equations have been developed that are more convenient in clinical practice (Firouzbakhsh et al, 1993;Bandini et al, 1995;Kaplan et al, 1995). The FAO/WHO/UNU equations result from a study of more than 7500 people 3-18 years of age (Firouzbakhsh et al, 1993;Bandini et al, 1995;Kaplan et al, 1995).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Comparison of resting energy expenditure in bronchopulmonary dysplasia to predicted equation

et al. 2006

View full text Add to dashboard Cite

Objective: Children with bronchopulmonary dysplasia (BPD) often suffer from growth failure because of disturbances in energy balance with an increase of resting energy expenditure (REE). Evaluation of REE is a useful tool for nutritional management. Indirect calorimetry is an elective method for measuring REE, but it is time consuming and requires rigorous procedure. The objective of this study was to test accuracy of prediction equation to evaluate REE in BPD children. Patients and methods: Fifty-two children aged 4-10 years with BPD (30 boys and 22 girls) and 30 healthy lean children (20 boys and 10 girls) were enrolled. In this study, indirect calorimetry was compared to four prediction equations (Schoffield-W, Schoffield-HW, Harris-Benedict and Food and Agriculture Organization equation) using Bland-Altman pair wise comparison. Results: The Harris-Benedict equation was the best equation to predict REE in children with BPD, and Schoffield-W was the best in healthy children. For the children with chronic lung disease of prematurity the Harris-Benedict equation showed the lowest mean predicted REEÀREE measured by indirect calorimetry difference (difference ¼ 15 kcal/day; limits of agreement À266 and 236 kcal/day; 95% confidence interval for the bias À207 to 177 kcal/day), and graphically, the best agreement. For the group of healthy children, it was the Schofield-W equation (À2.9 kcal/day; limits of agreement À275 and 269 kcal/day; 95% confidence interval for the bias À171 to 165 kcal/day), and graphically, the best agreement. Conclusion: Differences in prediction equation are minimal compared to calorimetry. Prediction equation could be useful in the management of children with BPD.

show abstract

Resting energy expenditure in clinical pediatrics: Measured versus prediction equations

Cited by 87 publications

References 14 publications

New equations to estimate basal metabolic rate in children aged 10–15 years

New equations to estimate basal metabolic rate in children aged 10–15 years

Comparison of measured sleeping metabolic rate and predicted basal metabolic rate during the first year of life: evidence of a bias changing with increasing metabolic rate

Comparison of resting energy expenditure in bronchopulmonary dysplasia to predicted equation

Contact Info

Product

Resources

About