Resting energy expenditure in critically ill patients: Evaluation methods and clinical applications

Estimates of resting energy expenditure using predictive equations in adults with severe burns: A systematic review and meta-analysis

BACKGROUND

Many equations to estimate the resting energy expenditure (REE) of patients with burns are currently available, but which of them provides the best guide to optimize nutrition support is controversial. This review examined the bias and precision of commonly used equations in patients with severe burns.

METHODS

A systematic search of the PubMed, Web of Science, Embase, and Cochrane Library databases was undertaken on June 1, 2023, to identify studies comparing predicted REE (using equations) with measured REE (by indirect calorimetry [IC]) in adults with severe burns. Meta-analyses of bias and calculations of precisions were performed in each predictive equation, respectively.

RESULTS

Nine eligible studies and 12 eligible equations were included. Among the equations, the Toronto equation had the lowest bias (26.1 kcal/day; 95% CI, -417.0 to 469.2), followed by the Harris-Benedict equation × 1.5 (1.5HB) and the Milner equation. The Ireton-Jones equation (303.4 kcal/day; 95% CI, 224.5-382.3) acceptably overestimated the REE. The accuracy of all of the equations was <50%. The Ireton-Jones equation had the relatively highest precision (41.2%), followed by the 1.5HB equation (37.0%) and the Toronto equation (34.7%).

CONCLUSION

For adult patients with severe burns, all of the commonly used equations for the prediction of REE are inaccurate. It is recommended to use IC for accurate REE measurements and to use the Toronto equation, 1.5HB equation, or Ireton-Jones equation as a reference when IC is not available. Further studies are needed to propose more accurate REE predictive models.

Estimated versus measured energy expenditure in ventilated surgical-trauma critically Ill patients

BACKGROUND

The American and European guidelines recommend measuring resting energy expenditure (REE) using indirect calorimetry (IC). Predictive equations (PEs) are used to estimate REE, but there is limited evidence for their use in critically ill patients. The aim of this study is to evaluate the degree of agreement and accuracy between IC-REE and ten different PEs in mechanically ventilated surgical-trauma critically ill patients who met their estimated caloric requirement.

METHODS

IC-REE was retrospectively compared to PE-REE by 10 PEs. Degree of agreement between REE-PE and REE-IC was analyzed by the Bland-Altman test (BAt) and the Concordance Correlation Coefficient (CCC). The accuracy was calculated by the percentage of patients whose REE-PE values differ by up to ±10% in relation to REE-IC. All analyses were stratified by gender and BMI (< 25 vs ≥ 25).

RESULTS

104 patients were analyzed and the closest estimate to IC-REE was the modified Harris-Benedict equation (mHB) by the BAt with a mean difference of 49.2 overall, 61.6 for males, 28.5 for females, 67.5 for BMI < 25 and 42.5 for BMI ≥ 25. The overall CCC between the IC-REE and mHB was 0.652; 0.560 for males; 0.496 for females; 0.570 for BMI < 25; and 0.598 for BMI ≥ 25. Modified HB equation was the most accurate with overall accuracy of 44.2%.

CONCLUSIONS

Effectiveness of PEs for estimating REE of mechanically ventilated surgical-trauma critically ill patients is limited. Nonetheless, of all the 10 equations examined, the closest to IC measured REE was the modified HB equation. This article is protected by copyright. All rights reserved.

A Comparison of the Indirect Calorimetry and Different Energy Equations for the Determination of Resting Energy Expenditure of Patients With Renal Transplantation

OBJECTIVE

We aimed to evaluate the agreement between the resting energy expenditure (REE) obtained by indirect calorimetry and eight prediction equations in adult patients with renal transplantation and a newly developed REE prediction equation for use in patients with renal transplantation in the clinic.

METHODS

A total of 51 patients (30 males and 21 females) were involved in the study. The REE was measured by indirect calorimetry and compared with the previous prediction equations. The agreement was assessed by the interclass correlation coefficient and by Bland-Altman plot analysis.

RESULTS

No significant difference was found in terms of age and body mass index between the genders. Differences between the predicted and measured REEs were maximum in the Bernstein equation (-478 kcal) and minimum in the Cunningham equation (-69 kcal). It was found that underprediction values varied from 27.5% (chronic kidney disease equation) to 98.0% (Bernstein equation). The highest overprediction value was found in the Schofield equation (17.7%). The Cunningham equation and the new equation had the lowest root mean square error (265 kcal/day). In this study, fat-free mass (FFM) was found to be the most significant variable in multiple regression analysis (r²: 0.55). The new specific equation based on FFM was generated as 424.2 + 24.7∗FFM (kg). Besides that, it was found that the new equation and Cunningham equation were distributed randomly according to Bland-Altman analysis. A supplementary new equation based on available anthropometric measurements was developed as -1996.8 + 19.1∗height (cm) + 7.2∗body weight (kg).

CONCLUSION

This study showed that most of the predictive equations significantly underestimated REE. In patients with renal transplantation, if the REE is not measurable by indirect calorimetry, the use of the proposed equations will be more accurate.

Energy Requirements in Critically Ill Patients

During the management of critical illness, optimal nutritional support is an important key for achieving positive clinical outcomes. Compared to healthy people, critically ill patients have higher energy expenditure, thereby their energy requirements and risk of malnutrition being increased. Assessing individual nutritional requirement is essential for a successful nutritional support, including the adequate energy supply. Methods to assess energy requirements include indirect calorimetry (IC) which is considered as a reference method, and the predictive equations which are commonly used due to the difficulty of using IC in certain conditions. In this study, a literature review was conducted on the energy metabolic changes in critically ill patients, and the implications for the estimation of energy requirements in this population. In addition, the issue of optimal caloric goal during nutrition support is discussed, as well as the accuracy of selected resting energy expenditure predictive equations, commonly used in critically ill patients.