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pubmed-article:1513430pubmed:abstractTextA high degree of variability in energy expenditure has characterized the metabolic response to traumatic brain injury. A goal of parenteral or enteral repletion in this population is the precise estimation of caloric requirement to avoid complications associated with overfeeding and underfeeding. The first aim of this study was to evaluate three predictive formulas for comparison to measured energy expenditure (MEE) derived from indirect calorimetry in patients with traumatic brain injury. A total of 385 measurements were obtained in 102 patients and were compared concurrently with these predictive formulas. The best predictive method in this phase (bivariate regression) yielded r = 0.39 and P less than 0.001 (231 repeated measures). This best prediction, when compared with MEE, however, was able to capture values within 75 to 125% of MEE in only 56% of measurements. The two remaining formulas yielded r = 0.38 (P less than 0.001) and r = 0.23 (P less than 0.001) in 386 and 267 repeated measures, respectively. The second aim of this study was to evaluate the ability of additional nutritional markers to improve predictive ability. Regression analyses were performed on nutritional markers including indices of severity of injury, concurrent drug therapy, vital signs, neurological status, gluconeogenesis, protein synthesis/excretion, and immune response. The statistical results of the analysis on these multiple nutritional markers showed only heart rate, temperature, and number of days elapsed after injury to be significant predictors of MEE by indirect calorimetry in multiple regression analyses (R = 0.32; P less than 0.001).(ABSTRACT TRUNCATED AT 250 WORDS)lld:pubmed
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pubmed-article:1513430pubmed:pagination246-52; discussion 252-3lld:pubmed
pubmed-article:1513430pubmed:dateRevised2004-11-17lld:pubmed
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pubmed-article:1513430pubmed:year1992lld:pubmed
pubmed-article:1513430pubmed:articleTitleEstimating energy expenditure in traumatic brain injury: comparison of indirect calorimetry with predictive formulas.lld:pubmed
pubmed-article:1513430pubmed:affiliationDivision of Neurosurgery, University of Utah School of Medicine, Salt Lake City.lld:pubmed
pubmed-article:1513430pubmed:publicationTypeJournal Articlelld:pubmed