Validation of predictive equations for resting energy expenditure in adult outpatients and inpatients

P.J.M. Weijs, H.M. Kruizenga, A.E. Van Dijk, B.S. van der Meij, J.A.E. Langius, D.L. Knol, R.J.M. Strack v schijndel, M.A.E. van Bokhorst-de van der Schueren

    Research output: Contribution to JournalArticleAcademicpeer-review

    Abstract

    Background & aims: When individual energy requirements of adult patients cannot be measured by indirect calorimetry, they have to be predicted with an equation. The aim of this study was to analyze which resting energy expenditure (REE) predictive equation was the best alternative to indirect calorimetry in adult patients. Methods: Predictive equations were included when based on weight, height, gender and/or age. REE was measured with indirect calorimetry. The mean squared prediction error was used to evaluate how well the equations fitted the REE measurement. Results: Eighteen predictive equations were included. Indirect calorimetry data were available for 48 outpatients and 45 inpatients. Also a subgroup of 42 underweight patients (BMI<18.5) was analyzed. The mean squared prediction error was 233-426 kcal/d and the percentage of patients with acceptable prediction was 28-52% for adult patients depending on the equation used. The FAO/WHO/UNU (1985) equation including both weight and height had the smallest prediction error in adult patients (233 kcal/d), outpatients (182 kcal/d), inpatients (277 kcal/d) as well as underweight patients (219 kcal/d). Conclusions: The REE of adult outpatients, inpatients and underweight patients can best be estimated with the FAO/WHO/UNU equation including weight and height, when indirect calorimetry is not available. © 2007 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism.
    Original languageEnglish
    Pages (from-to)150-157
    JournalClinical Nutrition
    Volume27
    Issue number1
    DOIs
    Publication statusPublished - 2008

    Fingerprint Dive into the research topics of 'Validation of predictive equations for resting energy expenditure in adult outpatients and inpatients'. Together they form a unique fingerprint.

    Cite this