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Allometric Prediction of Energy Expenditure in Infants and Children
Predicting energy needs in children is complicated by the wide range of patient sizes, confusing traditional estimation equations, nonobjective stress-activity factors, and so on. These complications promote errors in bedside estimates of nutritional needs by rendering the estimation methods functio...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3255515/ https://www.ncbi.nlm.nih.gov/pubmed/22308194 http://dx.doi.org/10.1177/1941406411414416 |
Sumario: | Predicting energy needs in children is complicated by the wide range of patient sizes, confusing traditional estimation equations, nonobjective stress-activity factors, and so on. These complications promote errors in bedside estimates of nutritional needs by rendering the estimation methods functionally unavailable to bedside clinicians. Here, the authors develop a simple heuristic energy prediction equation that requires only body mass (not height, age, or sex) as input. Expert estimation of energy expenditure suggested a power-law relationship between mass and energy. A similar mass-energy expenditure relationship was derived from published pediatric echocardiographic data using a Monte Carlo model of energy expenditure based on oxygen delivery and consumption. A simplified form of the equation was compared with energy required for normal growth in a cohort of historical patients weighing 2 to 70 kg. All 3 methods demonstrate that variation in energy expenditure in children is dominated by mass and can be estimated by the following equation: Power(kcal/kg/d) = 200 × [Mass(kg)((−0.4))]. This relationship explains 85% of the variability in energy required to maintain expected growth over a broad range of surgical clinical contexts. A simplified power-law equation predicts real-world energy needs for growth in patients over a wide range of body sizes and clinical contexts, providing a more useful bedside tool than traditional estimators. |
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