A Single-Center Prospective Observational Study Comparing Resting Energy Expenditure in Different Phases of Critical Illness: Indirect Calorimetry Versus Predictive Equations

Abstract

Objectives: Several predictive equations have been developed for estimation of resting energy expenditure, but no study has been done to compare predictive equations against indirect calorimetry among critically ill patients at different phases of critical illness. This study aimed to determine the degree of agreement and accuracy of predictive equations among ICU patients during acute phase (≤ 5 d), late phase (6-10 d), and chronic phase (≥ 11 d). Design: This was a single-center prospective observational study that compared resting energy expenditure estimated by 15 commonly used predictive equations against resting energy expenditure measured by indirect calorimetry at different phases. Degree of agreement between resting energy expenditure calculated by predictive equations and resting energy expenditure measured by indirect calorimetry was analyzed using intraclass correlation coefficient and Bland-Altman analyses. Resting energy expenditure values calculated from predictive equations differing by ± 10% from resting energy expenditure measured by indirect calorimetry was used to assess accuracy. A score ranking method was developed to determine the best predictive equations. Setting: General Intensive Care Unit, University of Malaya Medical Centre. Patients: Mechanically ventilated critically ill patients. Interventions: None. Measurements and Main Results: Indirect calorimetry was measured thrice during acute, late, and chronic phases among 305, 180, and 91 ICU patients, respectively. There were significant differences (F= 3.447; p = 0.034) in mean resting energy expenditure measured by indirect calorimetry among the three phases. Pairwise comparison showed mean resting energy expenditure measured by indirect calorimetry in late phase (1,878 ± 517 kcal) was significantly higher than during

Do we need different predictive equations for the acute and late phases of critical illness? A prospective observational study with repeated indirect calorimetry measurements

“…Informed consent was obtained from the patients or their legal representative. The inclusion and exclusion criteria are the same as our previous study [ 14 ]. In this study, we further excluded patients with extreme body mass index (BMI), <15 kg/m 2 or > 40 kg/m 2 , as their metabolic requirement might be very different from that of other patients [ 16 ].…”

“…While PEs developed in the critically ill population, such as the Penn State [ 9 ], Swinamer [ 12 ], and Faisy [ 13 ] equations, were developed from Caucasians and data of patients in the acute phase (≤5 days). Our recent study found that none of the commonly used PEs could optimally estimate measured REEs of patients in different phases of critical illness [ 14 ]. Furthermore, we found that REE during the acute phase of critical illness is generally lower than the late phase (6–10 days), while REE in the chronic phase (≥11 days) was not significantly different from the late phase [ 14 ].…”

“…Our recent study found that none of the commonly used PEs could optimally estimate measured REEs of patients in different phases of critical illness [ 14 ]. Furthermore, we found that REE during the acute phase of critical illness is generally lower than the late phase (6–10 days), while REE in the chronic phase (≥11 days) was not significantly different from the late phase [ 14 ]. Based on these results, we hypothesized that the use of separate PE for the acute (≤5 days) and later phases (≥6 days; collectively known as the late phase) would further optimize the nutrition care of critically ill patients, especially in the setting where IC is not available.…”

Energy expenditure and indirect calorimetry in critical illness and convalescence: current evidence and practical considerations

“…The ebb phase lasted minutes to hours after the initial insult and was thought to be characterized by a decline in body temperature and oxygen consumption, aimed at reducing posttraumatic energy depletion [26]. After this brief phase of hypometabolism, Sir Cuthbertson and others recognized a significant increase, or "flow," in metabolism, called traumatic inflammation, or hypermetabolism [28][29][30][31]. Hypermetabolism was thought to result from persistent catabolism, the systemic breakdown of lean tissue mass, and a rise in O 2 consumption to produce endogenous energy substrates to meet the high energy requirements during critical illness [1,2].…”

“…Predictive equations estimate a patient's energy expenditure using anthropometry and vital parameters to estimate EE. All equations are unreliable, as EE is affected by many individual factors unaccounted for in the formulas [1,7,19,31,55,[91][92][93][94][95]. When comparing the results of predictive equations to those of IC, many discrepancies are found [18].…”