OBJECTIVE: Use comparison with indirect calorimetry to confirm the ability of our previously described equation to predict resting energy expenditure in mechanically ventilated patients. DESIGN: Prospective, validation study. SETTING: Eighteen-bed, medical intensive care unit at a teaching hospital. PATIENTS: All adult patients intubated >24 hrs were assessed for eligibility. Exclusion criteria were clinical situations that could contribute to erroneous calorimetric measurements. INTERVENTIONS: Resting energy expenditure was calculated using the original Harris-Benedict equations and those corrected for usual stress factors, the Swinamer equation, the Fusco equation, the Ireton-Jones equation, and our equation: resting energy expenditure (kcal/day) = 8 x weight (kg) + 14 x height (cm) + 32 x minute ventilation (L/min) + 94 x temperature (degrees C) - 4834. MEASUREMENTS AND MAIN RESULTS: Resting energy expenditure was measured by indirect calorimetry for the 45 included patients. Resting energy expenditure calculated with our predictive model correlated with the measured resting energy expenditure (r2 = .62, p < .0001), and Bland-Altman analysis showed a mean bias of -192 +/- 277 kcal/day, with limits of agreement ranging from -735 to 351 kcal/day. Resting energy expenditure calculated with the Harris-Benedict equations was more weakly correlated with measured resting energy expenditure (r2 = .41, p < .0001), with Bland-Altman analysis showing a mean bias of 279 +/- 346 kcal/day between them and the limits of agreement ranging from -399 to 957 kcal/day. Applying usual stress-correction factors to the Harris-Benedict equations generated wide variability, and the correlation with measured resting energy expenditure was poorer (r2 = .18, p < .0001), with Bland-Altman analysis showing a mean bias of -357 +/- 750 kcal/day and limits of agreement ranging from -1827 to 1113 kcal/day. The use of the Swinamer, Fusco, or Ireton-Jones predictive methods yielded weaker correlation between calculated and measured resting energy expenditure (r2 = .41, p < .0001; r2 = .38, p < .0001; r2 = .39, p < .0001, respectively) than our equation, and Bland-Altman analysis showed no improvement in agreement and variability between methods. CONCLUSIONS: The Faisy equation, based on static (height), less stable (weight), and dynamic biometric variables (temperature and minute ventilation), provided precise and unbiased resting energy expenditure estimations in mechanically ventilated patients.
OBJECTIVE: Use comparison with indirect calorimetry to confirm the ability of our previously described equation to predict resting energy expenditure in mechanically ventilated patients. DESIGN: Prospective, validation study. SETTING: Eighteen-bed, medical intensive care unit at a teaching hospital. PATIENTS: All adult patients intubated >24 hrs were assessed for eligibility. Exclusion criteria were clinical situations that could contribute to erroneous calorimetric measurements. INTERVENTIONS: Resting energy expenditure was calculated using the original Harris-Benedict equations and those corrected for usual stress factors, the Swinamer equation, the Fusco equation, the Ireton-Jones equation, and our equation: resting energy expenditure (kcal/day) = 8 x weight (kg) + 14 x height (cm) + 32 x minute ventilation (L/min) + 94 x temperature (degrees C) - 4834. MEASUREMENTS AND MAIN RESULTS: Resting energy expenditure was measured by indirect calorimetry for the 45 included patients. Resting energy expenditure calculated with our predictive model correlated with the measured resting energy expenditure (r2 = .62, p < .0001), and Bland-Altman analysis showed a mean bias of -192 +/- 277 kcal/day, with limits of agreement ranging from -735 to 351 kcal/day. Resting energy expenditure calculated with the Harris-Benedict equations was more weakly correlated with measured resting energy expenditure (r2 = .41, p < .0001), with Bland-Altman analysis showing a mean bias of 279 +/- 346 kcal/day between them and the limits of agreement ranging from -399 to 957 kcal/day. Applying usual stress-correction factors to the Harris-Benedict equations generated wide variability, and the correlation with measured resting energy expenditure was poorer (r2 = .18, p < .0001), with Bland-Altman analysis showing a mean bias of -357 +/- 750 kcal/day and limits of agreement ranging from -1827 to 1113 kcal/day. The use of the Swinamer, Fusco, or Ireton-Jones predictive methods yielded weaker correlation between calculated and measured resting energy expenditure (r2 = .41, p < .0001; r2 = .38, p < .0001; r2 = .39, p < .0001, respectively) than our equation, and Bland-Altman analysis showed no improvement in agreement and variability between methods. CONCLUSIONS: The Faisy equation, based on static (height), less stable (weight), and dynamic biometric variables (temperature and minute ventilation), provided precise and unbiased resting energy expenditure estimations in mechanically ventilated patients.
Authors: Hassan Siddiki; Marija Kojicic; Guangxi Li; Murat Yilmaz; Taylor B Thompson; Rolf D Hubmayr; Ognjen Gajic Journal: Crit Care Date: 2010-07-29 Impact factor: 9.097
Authors: Peter J M Weijs; Wilhelmus G P M Looijaard; Albertus Beishuizen; Armand R J Girbes; Heleen M Oudemans-van Straaten Journal: Crit Care Date: 2014-12-14 Impact factor: 9.097