Retrospective study comparing predicted and measured resting energy expenditure in burn patients with obesity
Genesy Aickareth1, Suyash Jain1, Shruti Patel1
1School of Medicine, Texas Tech University Health Sciences Center, Lubbock, TX, USA.
Background:
Among burn patients, existing predictive equations for estimating resting energy expenditure (REE) frequently demonstrate limited accuracy, with significant challenges observed in patients with obesity. The objective of this study was to assess whether the discrepancy between REE measured by indirect calorimetry (IC) and estimated through predictive equations is influenced by weight status in patients with burns.
Methods:
A retrospective review of burn patients from 2016 to 2021. Bland-Altman plots and concordance correlation coefficients (CCC) were used to assess the agreement and reproducibility between the measured and calculated REE.
Results:
Of the 88 patients who underwent IC, 40 (46.5 %) were individuals with obesity (OB), and 48 (54.5 %) were not. The mean measured REE was 2644 ± 891 kcal/day (6 post-burn days; Q1-Q3: 4.75-14). REE calculated using the Harris-Benedict x 1.5 (1.5HB), modified 1.5HB, Mifflin-St Jeor, Carlson, and Toronto equations did not show statistically significant differences compared to the measured REE in either group. CCCs indicated poor reproducibility. Bland-Altman plots revealed no significant constant biases; however, proportional biases were present. Varied levels of under- and overestimation were present in both the OB and non-OB groups, with the rest of the equations showing evidence of poor reproducibility and agreement.
Conclusions:
There is universally poor agreement, reduced reproducibility and accuracy, and biases associated with the calculated REE, which is not directly influenced by weight status. However, when IC is unavailable, certain equations (e.g., Harris-Benedict equation, modified Harris-Benedict equation, Mifflin-St Jeor equation, Carlson, and Toronto equations) can be used with limited capacity for both OB and non-OB group patients. However, given the significant biases, clinicians must use these predictive equations with caution and supplement with close clinical monitoring.
