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The Challenge of Developing a New Predictive Formula to Estimate Energy Requirements in Ventilated Critically Ill Children
Author(s) -
Meyer Rosan,
Kulinskaya Elena,
Briassoulis George,
Taylor Rachel M.,
Cooper Mehrengise,
Pathan Nazima,
Habibi Parviz
Publication year - 2012
Publication title -
nutrition in clinical practice
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.725
H-Index - 71
eISSN - 1941-2452
pISSN - 0884-5336
DOI - 10.1177/0884533612448479
Subject(s) - medicine , critically ill , resting energy expenditure , energy expenditure , energy requirement , observational study , intensive care , feeding tube , energy (signal processing) , intensive care medicine , statistics , regression , mathematics , surgery
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.