ESTIMATING BASAL ENERGY EXPENDITURE IN LIVER TRANSPLANT RECIPIENTS: THE VALUE OF THE HARRIS-BENEDICT EQUATION

Assessment of resting energy expenditure in patients with cirrhosis

BACKGROUND

Malnutrition affects 20% to 50% of patients with cirrhosis. It may be associated with serious complications and has a direct impact on prognosis. Resting energy expenditure (REE) is an important parameter to guide the optimization of therapy and recovery of nutritional status in patients with cirrhosis. However, the REE of patients with cirrhosis is still unclear, casting doubt upon the optimal nutritional management approach.

AIM

To identify the best method that predicts the REE of cirrhotic patients, using indirect calorimetry (IC) as the gold standard.

METHODS

An observational study was performed on 90 patients with cirrhosis. REE was assessed by IC, bioelectrical impedance analysis (BIA), and predictive formulas, which were compared using Bland-Altman plots and the Student’s t-test.

RESULTS

REE values measured by IC (1607.72 ± 257.4 kcal) differed significantly from those determined by all other methods (BIA: 1790.48 ± 352.1 kcal; Harris & Benedict equation: 2373.54 ± 254.9 kcal; IOM equation: 1648.95 ± 185.6 kcal; Cunningham equation: 1764.29 ± 246.2 kcal), except the Food and Agriculture Organization of the United Nations, World Health Organization, and United Nations University (FAO/WHO/UNU) (1616.07 ± 214.6 kcal) and McArdle (1611.30 ± 241.8 kcal) equations. We found no significant association when comparing IC and 24-h dietary recall among different Child-Pugh classes of cirrhosis.

CONCLUSION

The IOM and FAO/WHO/UNU equations have the best agreement with the CI. These results indicate a possibility of different tools for the clinical practice on cirrhotic patients.

Resting energy expenditure in cirrhotic patients with and without hepatocellular carcinoma

BACKGROUND

The diagnosis of malnutrition in patients with independent hepatocellular carcinoma (HCC) varies from 20% to 50%, is related to important complications and has a direct impact on the prognosis. Determination of the resting energy expenditure (REE) has become an important parameter in this population, as it allows therapeutic adjustments to recover their nutritional status. The REE in cirrhosis, with and without HCC, is not clearly defined, and requires the identification and definition of the best nutritional approach.

AIM

To evaluate the REE of patients with cirrhosis, with and without HCC.

METHODS

This is a prospective observational study evaluating the REE of 118 patients, 33 with cirrhosis and hepatocellular carcinoma and a control group of 85 patients with cirrhosis without HCC, using indirect calorimetry (IC), bioimpedance, and predictive formulas.

RESULTS

The REE determined by IC in cirrhotic patients with HCC was 1643 ± 364 and in those without HCC was 1526 ± 277 (P = 0.064). The REE value as assessed by bioimpedance was 1529 ± 501 for those with HCC and 1660 ± 385 for those without HCC (P = 0.136). When comparing the values of REE determined by IC and predictive formulas in cirrhotics with HCC, it was observed that only the formulas of the Food and Agriculture Organization (FAO)/World Health Organization (WHO) (1985) and Cunningham (1980) presented values similar to those determined by IC. When comparing the REE values determined by IC and predictive formulas in cirrhotics without HCC, it was observed that the formulas of Schofield (1985), FAO/WHO (1985), WHO (2000), Institute of Medicine (IOM) (2005) and Katch and McArdie (1996) presented values similar to those determined by IC.

CONCLUSION

The FAO/WHO formula (1985) could be used for cirrhotic patients with or without HCC; as it is the one with the values closest to those obtained by IC in these cirrhotic patients.

New equation to estimate resting energy expenditure in non-critically ill patients

BACKGROUND

Correct measurement of resting energy expenditure (REE) is essential to offer a proper nutritional management during hospital stay. Dietitians are not able to perform an effective dietary treatment if predicted REE values are obtained from invalid equations.

OBJECTIVE

The aim of this study was to develop a more valid method to estimate REE in non-critically ill Portuguese patients.

DESIGN

In this cross-sectional study, REE was measured by indirect calorimetry (IC) in 180 non-critically patients during hospital stay (50 participants were allocated to the validation group by simple randomization and the remaining 130 were allocated to the derivation group). The best accurate equations were derived by multiple linear regression analysis (stepwise) based on anthropometric variables. The equations were tested on the validation group and compared with published predictive equations.

RESULTS

Data was collected from 130 patients, 68 women (52.3%) and 62 men (47.7%), mean age was 58.9 ± 16.8 years and REE-IC was 1918 ± 721 kcal/day. The new best-fit equation REE (kcal/day) = 14.4 (Height) + 52.7 (MUAC) + 453.4 (1 if male, 0 if female) - 371.2 (if Obese) - 2138.3 showed strength of evidence decisive (BF₁₀ = 8008), when compared by Bayesian model, and r² = 0.315. Only estimated REE values obtained using new equations did not present significant difference when compared with measured REE values (kcal/kg).

CONCLUSIONS

In this study, new equations derived from a non-critically ill population showed higher validity in estimating REE than currently used equations. A better estimation of REE may lead to a better nutritional intervention and a decreased risk of undernutrition in hospitalized patients.

Assessment of resting energy demand and body composition in oncological patients undergoing partial resections of the liver

INTRODUCTION

The metabolism of the body is a complicated process. The most important organ of the organism that affects the intensity of changes is the liver. An effective treatment method of primary and metastatic tumours is a partial resection of the organ. The analysis of changes in the body composition of patients undergoing this type of treatment allows identification of problems coexisting with the underlying disease.

AIM

To evaluate changes in the parameters of body composition and the amount of resting metabolism.

MATERIAL AND METHODS

The study group consisted of 87 patients who underwent resection of changes in the liver or thermoablation of focal lesions during hospitalisation.

RESULTS

Analysis of the data showed that the surgical intervention contributes to a statistically significant (p < 0.05) decrease in the value of the phase angle. A significant increase was noted within the extra cellular water content. The amount of resting metabolism in the postoperative period did not differ significantly; however, there was an upward trend in women and a downward trend in men.

CONCLUSIONS

Surgical resection of lesions aimed at extending the survival of patients are performed more and more often, while the consequences of these operations are not sufficiently known. The adverse effect of resection treatments on body composition parameters, mainly imaged by decreasing the phase angle value, should be minimised. Effects on metabolism remain ambiguous because no significant changes have been demonstrated in the postoperative period.

TRUNK BODY MASS INDEX: A NEW REFERENCE FOR THE ASSESSMENT OF BODY MASS DISTRIBUTION

BACKGROUND

Body mass index (BMI) has some limitations for nutritional diagnosis since it does not represent an accurate measure of body fat and it is unable to identify predominant fat distribution.

AIM

To develop a BMI based on the ratio of trunk mass and height.

METHODS

Fifty-seven patients in preoperative evaluation to bariatric surgery were evaluated. The preoperative anthropometric evaluation assessed weight, height and BMI. The body composition was evaluated by bioimpedance, obtaining the trunk fat free mass and fat mass, and trunk height. Trunk BMI (tBMI) was calculated by the sum of the measurements of the trunk fat free mass (tFFM) and trunk fat mass (tFM) in kg, divided by the trunk height squared (m2)). The calculation of the trunk fat BMI (tfBMI) was calculated by tFM, in kg, divided by the trunk height squared (m2)). For the correction and adjustment of the tBMI and tfBMI, it was calculated the relation between trunk extension and height, multiplying by the obtained indexes.

RESULTS

The mean data was: weight 125.3±19.5 kg, height 1.63±0.1 m, BMI was 47±5 kg/m2) and trunk height was 0.52±0,1 m, tFFM was 29.05±4,8 kg, tFM was 27.2±3.7 kg, trunk mass index was 66.6±10.3 kg/m², and trunk fat was 32.3±5.8 kg/m². In 93% of the patients there was an increase in obesity class using the tBMI. In patients with grade III obesity the tBMI reclassified to super obesity in 72% of patients and to super-super obesity in 24% of the patients.

CONCLUSION

The trunk BMI is simple and allows a new reference for the evaluation of the body mass distribution, and therefore a new reclassification of the obesity class, evidencing the severity of obesity in a more objectively way.

Measurement of Total Liver Volume Using the Energy Expenditure: A New Formula

BACKGROUND

The assessment of liver volume (LV) is important before surgical resection or transplantation to reduce the risk of hepatic insufficiency. LV is usually measured using computed tomography or with some formulas. The aim of this study was to develop a new dynamic formula to predict LV.

METHODS

Using computed tomography, LV was calculated in 101 patients without liver disease. LV was correlated with patient metabolic status, calculated with the Harris-Benedict equation for basal energy expenditure (BEE). Activity energy expenditure (AEE) was also calculated. Using linear regression analysis, a new formula was derived and was compared with Heinmann's, Urata's, Emre's, Vauthey's, Yoshizumi's, Yu's, and Hashimoto's formulas.

RESULTS

A new basal formula was established: LV = (0.789 × BEE) + 272. It was found to be the most accurate (R² = 0.39, p < 0.001). Heinmann's, Emre's, and Vauthey's formulas tend to overestimate LV, while Urata's, Yoshizumi's, Yu's, and Hashimoto's formulas tend to underestimate LV. A new AEE formula was also established: LV = (0.789 × AEE) + 272.

CONCLUSIONS

These formulas give a dynamic perspective of LV, which may be influenced by the patient's actual clinical status. Using these formulas, it is possible to estimate an increased value of LV, which may contribute to a reduction in the risk of postoperative hepatic insufficiency.