Biomaterial Database

Accuracy of Resting Energy Expenditure Predictive Equations in Patients With Cancer. 2019

Sarah A Purcell, and Sarah A Elliott, and Vickie E Baracos, and Quincy S C Chu, and Michael B Sawyer, and Marina Mourtzakis, and Jacob C Easaw, and Jennifer L Spratlin, and Mario Siervo, and Carla M Prado

Human Nutrition Research Unit, Department of Agricultural, Food, and Nutritional Science, University of Alberta, Edmonton, Alberta, Canada.

BACKGROUND Our purpose was to assess the accuracy of resting energy expenditure (REE) equations in patients with newly diagnosed stage I-IV non-small cell lung, rectal, colon, renal, or pancreatic cancer. METHODS In this cross-sectional study, REE was measured using indirect calorimetry and compared with 23 equations. Agreement between measured and predicted REE was assessed via paired t-tests, Bland-Altman analysis, and percent of estimations ≤ 10% of measured values. Accuracy was measured among subgroups of body mass index (BMI), stage (I-III vs IV), and cancer type (lung, rectal, and colon) categories. Fat mass (FM) and fat-free mass (FFM) were assessed using dual x-ray absorptiometry. RESULTS Among 125 patients, most had lung, colon, or rectal cancer (92%, BMI: 27.5 ± 5.6 kg/m2 , age: 61 ± 11 years, REE: 1629 ± 321 kcal/d). Thirteen (56.5%) equations yielded REE values different than measured (P < 0.05). Limits of agreement were wide for all equations, with Mifflin-St. Jeor equation having the smallest limits of agreement, -21.7% to 11.3% (-394 to 203 kcal/d). Equations with FFM were not more accurate except for one equation (Huang with body composition; bias, limits of agreement: -0.3 ± 11.3% vs without body composition: 2.3 ± 10.1%, P < 0.001). Bias in body composition equations was consistently positively correlated with age and frequently negatively correlated with FM. Bias and limits of agreement were similar among subgroups of patients. CONCLUSIONS REE cannot be accurately predicted on an individual level, and bias relates to age and FM.

UI	MeSH Term	Description	Entries
D008297	Male		Males
D008875	Middle Aged	An adult aged 45 - 64 years.	Middle Age
D008954	Models, Biological	Theoretical representations that simulate the behavior or activity of biological processes or diseases. For disease models in living animals, DISEASE MODELS, ANIMAL is available. Biological models include the use of mathematical equations, computers, and other electronic equipment.	Biological Model,Biological Models,Model, Biological,Models, Biologic,Biologic Model,Biologic Models,Model, Biologic
D009367	Neoplasm Staging	Methods which attempt to express in replicable terms the extent of the neoplasm in the patient.	Cancer Staging,Staging, Neoplasm,Tumor Staging,TNM Classification,TNM Staging,TNM Staging System,Classification, TNM,Classifications, TNM,Staging System, TNM,Staging Systems, TNM,Staging, Cancer,Staging, TNM,Staging, Tumor,System, TNM Staging,Systems, TNM Staging,TNM Classifications,TNM Staging Systems
D009369	Neoplasms	New abnormal growth of tissue. Malignant neoplasms show a greater degree of anaplasia and have the properties of invasion and metastasis, compared to benign neoplasms.	Benign Neoplasm,Cancer,Malignant Neoplasm,Tumor,Tumors,Benign Neoplasms,Malignancy,Malignant Neoplasms,Neoplasia,Neoplasm,Neoplasms, Benign,Cancers,Malignancies,Neoplasias,Neoplasm, Benign,Neoplasm, Malignant,Neoplasms, Malignant
D011237	Predictive Value of Tests	In screening and diagnostic tests, the probability that a person with a positive test is a true positive (i.e., has the disease), is referred to as the predictive value of a positive test; whereas, the predictive value of a negative test is the probability that the person with a negative test does not have the disease. Predictive value is related to the sensitivity and specificity of the test.	Negative Predictive Value,Positive Predictive Value,Predictive Value Of Test,Predictive Values Of Tests,Negative Predictive Values,Positive Predictive Values,Predictive Value, Negative,Predictive Value, Positive
D001823	Body Composition	The relative amounts of various components in the body, such as percentage of body fat.	Body Compositions,Composition, Body,Compositions, Body
D002149	Energy Intake	Total number of calories taken in daily whether ingested or by parenteral routes.	Caloric Intake,Calorie Intake,Intake, Calorie,Intake, Energy
D002153	Calorimetry, Indirect	Calculation of the energy expenditure in the form of heat production of the whole body or individual organs based on respiratory gas exchange.	Calorimetry, Respiration,Calorimetries, Indirect,Calorimetries, Respiration,Indirect Calorimetries,Indirect Calorimetry,Respiration Calorimetries,Respiration Calorimetry
D003430	Cross-Sectional Studies	Studies in which the presence or absence of disease or other health-related variables are determined in each member of the study population or in a representative sample at one particular time. This contrasts with LONGITUDINAL STUDIES which are followed over a period of time.	Disease Frequency Surveys,Prevalence Studies,Analysis, Cross-Sectional,Cross Sectional Analysis,Cross-Sectional Survey,Surveys, Disease Frequency,Analyses, Cross Sectional,Analyses, Cross-Sectional,Analysis, Cross Sectional,Cross Sectional Analyses,Cross Sectional Studies,Cross Sectional Survey,Cross-Sectional Analyses,Cross-Sectional Analysis,Cross-Sectional Study,Cross-Sectional Surveys,Disease Frequency Survey,Prevalence Study,Studies, Cross-Sectional,Studies, Prevalence,Study, Cross-Sectional,Study, Prevalence,Survey, Cross-Sectional,Survey, Disease Frequency,Surveys, Cross-Sectional