The agreement between measured and predicted resting energy expenditure in patients with pancreatic cancer — A pilot study

Abstract

Objective To compare measured resting energy expenditure to resting energy expenditure predicted from eight published prediction equations in a sample of patients with pancreatic cancer.Design Cross-sectional study.Setting Ambulatory patients of a tertiary private hospital.Participants Eight patients with pancreatic cancer (5 males, 3 females; age: 62.0±5.2 years; BMI: 24.4±3.2 kg/m 2 ; weight loss: 12.1±6.0%; mean±SD). MethodsResting energy expenditure was measured using indirect calorimetry and predicted from eight published prediction methods (Harris-Benedict with no injury factor, Harris-Benedict with 1.3 injury factor, Schofield, Owen, Mifflin, Cunningham, and Wang equations and the 20 kcal/kg ratio). Body composition was assessed by deuterium oxide dilution technique. Statistical analysis was performed by using the method of Bland and Altman, and the Student's t-test. ResultsThe Harris-Benedict equations with an injury factor of 1.3 resulted in a significantly higher mean predicted resting energy expenditure compared to measured resting energy expenditure, while there was no significant difference between mean measured and predicted resting energy expenditure and the other 7 methods. At an individual level, the limits of agreement are wide for all equations. The best combination of low bias and narrowest limits of agreement was observed in the prediction of resting energy expenditure from the Wang equation (based on fat free mass) and the HarrisBenedict equation (based on weight and height). ConclusionAt a group level, there is agreement between mean measured and predicted resting energy expenditure with the exception of the Harris-Benedict equation with an injury factor of 1.3. The results of this pilot study suggest that, for an individual, the limits of agreement are wide, and clinically important differences in resting energy expenditure would be obtained. Clinicians need to be aware of the limitations of the use of resting energy expenditure prediction equations for individuals.

Pilot Study to Explore the Accuracy of Current Prediction Equations in Assessing Energy Needs of Patients with Newly Diagnosed Glioblastoma Multiforme

“…It is also the most accurate in older individuals (≥ 70 years old) (36). In a pilot study similar to our own, Bauer et al (5) found in pancreatic cancer patients Mifflin-St Jeor had the greatest underestimation, but was still accurate at the group level. The differences in our conclusions and those of Bauer et al (5) could be related to the narrower age range (55-70 years), the current weight status of their patients (currently normal weight or overweight with a weight loss of ≥5% in the last six months), or the repeated observations on four participants.…”

“…If the mean difference was close to zero and not statistically significant, we would conclude that cREE is a reasonably accurate representation of mREE. The Bland-Altman approach (5, 28) was used to assess the agreement between mREE and each cREE. This method calculates bias, which is defined as the mean of the differences between the predicted and measured REE, and the limits of agreement, which are defined as ± 2 standard deviations from the mean difference.…”

“…Numerous equations have been developed from variables easily attained in the clinical setting (i.e., height, weight, age). Over the last few decades, more attention has been paid to the metabolic rate of specific disease states, including cancer (5-11) and other neurological diseases, such as amyotrophic lateral sclerosis (12). Individualized nutrition approaches can improve dietary intake and quality of life of cancer patients (13) and calorie needs are often the foundation for these recommendations (14).…”

“…Individualized nutrition approaches can improve dietary intake and quality of life of cancer patients (13) and calorie needs are often the foundation for these recommendations (14). Previous research in other cancer populations yields mixed results of whether the REE of cancer patients differs from individuals without malignancies (8, 9) and if prediction equations underestimate (6, 8, 10), overestimate (5), or accurately predict (5) actual needs. It is likely these conclusions will vary by type of cancer, phase of the disease, and the treatment strategy.…”

The 13C bicarbonate dilution technique to determine energy expenditure in young bulls validated by indirect calorimetry

“…Empiric formulas to predict energy expenditure from body weight, body surface and body height have been developed for humans (e.g. Bauer et al, 2004;Cunningham, 1991;Kien and Ugrasbul, 2004;Schofield, 1985). However, these approaches are only adequate to predict mean values for particular populations of humans or animals.…”