Use of both quantitative and qualitative methods to improve assessment of resting energy expenditure equation performance in hospitalized adults. (April 2018)

• Record Type: Journal Article
• Authors: Teigen, Levi M.; DiCecco, Sara R.; Vock, David M.; Vierow, Kelsey L.; Andrews, Lindsay; Hammel, Kris D.; Sfeir, Jad G.; Miles, John M.; Hurley, Daniel L.
• Is Part Of: Clinical nutrition ESPEN. Volume 24(2018)
• Page Start: 120
• Page End: 126
• Publication Date: 2018-04
• Subjects: Energy expenditure -- Clinical nutrition -- Calories -- Metabolism -- Indirect calorimetry -- Energy expenditure equation; BMI Body mass index -- CI Confidence interval -- EE Energy expenditure -- IC Indirect calorimetry -- ICU Intensive care unit -- LOA Limits of agreement -- REE Resting energy expenditure -- SD Standard deviation; Nutritionally induced diseases -- Periodicals; Metabolism -- Disorders -- Periodicals; 616.39005
• DOI: 10.1016/j.clnesp.2018.01.001
• Languages: English
• ISSNs: 2405-4577

Abstract

Summary: Objective: To introduce the use of qualitative assessment in energy expenditure (EE) equation research to improve the understanding of performance of the equations in the clinical setting. Patients and Methods: Hospitalized individuals who had an indirect calorimetry (IC) measurement during their hospital stay from 2010 to 2012 were included in the study (n = 59). An additional 1000 patients hospitalized during this time were used to limit the IC cohort to a more "clinically relevant" BMI range (n = 46). The following estimation equations were assessed: Harris–Benedict, 25 kcal/kg using actual body weight, Mifflin St. Jeor, Ireton-Jones, Penn State, and Owen. Bland–Altman plots with Loess curves were generated to compare estimated basal caloric needs between EE equations and IC values. Results: This study found a large amount of variability with all EE equations. As the mean calorie level increased, the Harris Benedict, Mifflin St. Jeor, Penn State, and Owen equations all tended to increasingly under-predict caloric need. Conclusion: In a research setting a qualitative assessment of EE equations can provide a more comprehensive understanding of equation performance by complementing traditional quantitative methods. The addition of a Loess curve to the Bland–Altman plot further enhances qualitative assessment.