Validation of a predictive method for an accurate assessment of resting energy expenditure in medical mechanically ventilated patients

• Published in: Critical care medicine Vol. 36; no. 4; pp. 1175 - 1183
• Main Authors: Savard, Jean-François, Faisy, Christophe, Lerolle, Nicolas, Guerot, Emmanuel, Diehl, Jean-Luc, Fagon, Jean-Yves
• Format: Journal Article
• Language: English
• Published: Hagerstown, MD by the Society of Critical Care Medicine and Lippincott Williams & Wilkins 01-04-2008; Lippincott; Lippincott, Williams & Wilkins
• Subjects: Anesthesia. Intensive care medicine. Transfusions. Cell therapy and gene therapy; Anthropometry; APACHE; Basal Metabolism; Biological and medical sciences; Calorimetry, Indirect - methods; Critical Care; Emergency and intensive care: techniques, logistics; Female; Humans; Intensive care medicine; Intensive care unit. Emergency transport systems. Emergency, hospital ward; Intensive Care Units; Life Sciences; Male; Medical sciences; Middle Aged; Reproducibility of Results; Respiration, Artificial; Human; Intensive care; nutrition; Energetic cost; critical illness; predictive method; energy expenditure; Mechanical ventilation; Resuscitation
• ISSN: 0090-3493; 1530-0293
• DOI: 10.1097/CCM.0b013e3181691502

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 equationresting energy expenditure (kcal/day) = 8 × weight (kg) + 14 × height (cm) + 32 × minute ventilation (L/min) + 94 × temperature (°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 (r = .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 (r = .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 (r = .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 (r = .41, p < .0001; r = .38, p < .0001; r = .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.