Systematic review of factors associated with energy expenditure in the critically ill

Abstract

Background and aims: Indirect calorimetry is the reference standard for energy expenditure measurement. Predictive formulae that replace it are inaccurate. Our aim was to review the patient and clinical factors associated with energy expenditure in critically ill patients. Methods: We conducted a systematic review of the literature. Eligible studies were those reporting an evaluation of factors and energy expenditure. Energy expenditure and factor associations with p-values were extracted from each study, and each factor was classified as either significantly, indeterminantly, or not associated with energy expenditure. Regression coefficients were summarized as measures of central tendency and spread. Metanalysis was performed on correlations. Results: The search strategy yielded 8521 unique articles, 307 underwent full text review, and 103 articles were included. Most studies were in adults. There were 95 factors with 352 evaluations. Minute volume, weight, age, % body surface area burn, sedation, post burn day, and caloric intake were significantly associated with energy expenditure. Heart rate, fraction of inspired oxygen, respiratory rate, respiratory disease diagnosis, positive end expiratory pressure, intensive care unit days, C-reactive protein, and size were not associated with energy expenditure. Multiple factors (n ¼ 37) were identified with an unclear relationship with energy expenditure and require further evaluation. Conclusions: An important interval step in the development of accurate formulae for energy expenditure estimation is a better understanding of relationships between patient and clinical factors and energy expenditure. The review highlights the limitations of currently available data, and identifies important factors that are not included in current prediction formulae of the critically ill.

The effect of age and body mass index on energy expenditure of critically ill medical patients

“…One study on healthy volunteers did not find a significant difference in resting metabolic rate between men and women when adjusted for lean body mass, and fat mass explained a significant variation of resting metabolic rate in women [28]. A recent systematic review discussed several patient and clinical factors that influence EE [29]. This issue may explain the inconsistencies and contradictions regarding published results.…”

“…The American Society of Parenteral and Enteral Nutrition guideline also favors the use of IC to determine EE. In the absence of IC, this guideline suggests to use either a predictive equation or a simplistic weight-based equation (25)(26)(27)(28)(29)(30) kcal/kg/day) [6]. Since a calorimeter and trained personnel are not widely available, various prediction equations for calculation of EE have been widely used.…”

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

“…Studies have shown that REE is influenced by many factors, including age, sex, body composition, body temperature, body movement, environmental temperature, heart rate, and disease status [ 26 – 28 ]. Pharmacological agents, such as analgesics, sedatives, and muscle relaxant reduce REE, while vasopressors increase REE [ 29 ].…”

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

“…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].…”