Objective: Compare the energy expenditure, predicted by anthropometric equations, with that measured by continuous on-line indirect calorimetry in ventilated, critically ill children during the early postinjury period.
Design: Prospective, clinical study.
Setting: Pediatric intensive care unit of a pediatric university hospital.
Patients: A total of 43 ventilated, critically ill children during the first 6 hrs after injury.
Interventions: An indirect calorimeter was used to continuously measure the energy expenditure for 24 hrs.
Measurements and Main Results: Clinical data collected were age, gender, actual and ideal weight, height, and body surface. Nutritional status was assessed by Waterlow and Shukla Index. Severity of illness was determined by Pediatric Risk of Mortality, Physiologic Stability Index, and Therapeutic Intervention Scoring System. Energy expenditure was measured (MEE) by continuous on-line indirect calorimetry for 24 hrs. Predicted Energy Expenditure (PEE) was calculated using the Harris-Benedict, Caldwell-Kennedy, Schofield, Food and Agriculture/World Health Organization/United Nation Union, Maffeis, Fleisch, Kleiber, Dreyer, and Hunter equations, using the actual and ideal weight. MEE and PEE were compared using paired Student's t-test, linear correlation (r), intraclass correlation coefficient (pI), and the Bland-Altman method. Mean MEE resulted in 674 ± 384 kcal/day. Most of the predictive equations overestimated MEE in ventilated, critically ill children during the early postinjury period. MEE and PEE differed significantly (p < .05) except when the Caldwell-Kennedy and the Fleisch equations were used. r2 ranged from 0.78 to 0.81 (p < .05), and pI was excellent (>.75) for the Caldwell-Kennedy, Schofield, Food and Agriculture/World Health Organization/United Nation Union, Fleisch, and Kleiber equations. The Bland-Altman method showed poor accuracy; the Caldwell-Kennedy equation was the best predictor of energy expenditure (bias, 38 kcal/day; precision, ±179 kcal/day). The accuracy in the medical group was higher (pI range, .71-.94) than in surgical patients (pI range, .18-.75).
Conclusions: Predictive equations do not accurately predict energy expenditure in ventilated, critically ill children during the early postinjury period; if available, indirect calorimetry must be performed.
The outcome of patients admitted to the intensive care unit (ICU) improves when their nutritional requirements are fulfilled. A precise evaluation of caloric requirements is essential to set up individualized support, thus avoiding complications related to over- and underfeeding. Overfeeding has been associated with increased CO2 production, respiratory failure, hyperglycaemia, and fat deposits in the liver, thus impairing recovery; underfeeding can lead to malnutrition, muscle weakness, and impaired immunity. In both cases, mortality and morbidity are increased (1).
Indirect calorimetry is a reliable and noninvasive method that determines energy expenditure at bedside in critically ill patients, including children, although it is expensive, time-consuming, and requires trained personnel. Several metabolic monitors have been validated in vitro and in vivo (2, 3). However, it is known that there are operational and technical limitations in the use of the indirect calorimetry in the ICU (high inspired oxygen concentrations and air leaks). Moreover, ICU care (e.g., endotracheal aspiration and daily toiletry) and clinical patient conditions (hemodynamic instability, agitation, and fever) can also reduce the accuracy of indirect calorimetry when it is performed during a short period of time, leading to contradictory results. Thus, longer indirect calorimetry studies, such as continuous on-line indirect calorimetry performed for 24 hrs, have shown that mechanically ventilated adult patients are in an hypermetabolic state during the early postinjury period (4). Different conclusions have been reached when other investigators have performed shorter indirect calorimetry studies (5-7). Metabolic response in children seems to be faster (8). Some authors have shown that after surgery is performed on neonates, there is only a transient increase in resting energy expenditure (9). However, other studies have failed to show a rise in energy expenditure during the early postinjury period in infants or older children (10, 11).
When indirect calorimetry is not available, the energy expenditure is calculated using predictive equations. Some authors have modified the predictive formulas based on anthropometric parameters (gender, weight, and height) and adding clinical stress factors (12, 13). However, the accuracy of these modified equations remains unknown for the early postinjury period because patients under study were in different phases of the stress response with variable times elapsed since injury. Furthermore, some patients were fasting, whereas other patients were receiving parenteral or enteral nutrition. All these factors could lead to contradictory results.
There is recent interest in setting up precocious nutrition, even in the immediate postoperative period. This requires assessment of energy expenditure during this early postinjury phase. Letton et al. (14) suggested that in the absence of calorimetric measurements, predicted basal metabolic rate should be used to estimate caloric delivery during the early postoperative period, although, to our knowledge, no studies exist that have monitored the accuracy of predictive formulas in critically ill children during the early postoperative period.
The purpose of the present study was to compare the accuracy of available equations to assess nutritional demands in ventilated, critically ill children during the first 24 hrs after pediatric ICU admission. The objective was to contrast the accuracy of predictive formulas with indirect calorimetry measurements for predicting energy expenditure during the early postinjury period. This was carried out by comparing the energy expenditure values measured by continuous, 24-hr indirect calorimetry (measured energy expenditure, MEE) with values obtained using the current anthropometric equations (predicted energy expenditure, PEE).