Literature DB >> 20663791

Are prediction equations reliable for estimating resting energy expenditure in chronic kidney disease patients?

Maria A Kamimura1, Carla M Avesani, Ana P Bazanelli, Flavia Baria, Sergio A Draibe, Lilian Cuppari.   

Abstract

BACKGROUND: The determination of resting energy expenditure (REE) is the primary step for estimating the energy requirement of an individual. Although numerous equations have been formulated for predicting metabolic rates, there is a lack of studies addressing the reliability of those equations in chronic kidney disease (CKD). Thus, the aim of this study was to evaluate whether the main equations developed for estimating REE can be reliably applied for CKD patients.
METHODS: A total of 281 CKD patients (124 non-dialysis, 99 haemodialysis and 58 peritoneal dialysis) and 81 healthy control individuals were recruited. Indirect calorimetry and blood sample collection were performed after a 12-h fasting. Two most traditionally used equations for estimating REE were chosen for comparison with the REE measured by indirect calorimetry: (i) the equation proposed by Harris and Benedict, and (ii) the equation proposed by Schofield that is currently recommended by the FAO/WHO/UNU.
RESULTS: Schofield's equation exhibited higher REE [1492±220 kcal/day (mean±SD)] in relation to Harris and Benedict's equation (1431±214 kcal/day; P<0.001), and both prediction equations showed higher REE in comparison with the reference indirect calorimetry (1352±252 kcal/day; P<0.001). In patients with diabetes, inflammation or severe hyperparathyroidism, the REE estimated by the Harris and Benedict equation was equivalent to that measured by indirect calorimetry. The intraclass correlation of the REE measured by indirect calorimetry with the Schofield's equation was r=0.48 (P<0.001) and with the Harris and Benedict's equation was r=0.58 (P<0.001). According to the Bland and Altman analysis, there was a large limit of agreement between both prediction equations and the reference method. Acceptable prediction of REE (90-110% adequacy) was found in 47% of the patients by using the Harris and Benedict's equation and in only 37% by using the Schofield's equation.
CONCLUSIONS: The most traditionally used prediction equations overestimated the REE of CKD patients, and the errors were minimized in the presence of comorbidities. There is a need to develop population-specific equations in order to adequately estimate the energy requirement of these patients.

Entities:  

Mesh:

Year:  2010        PMID: 20663791     DOI: 10.1093/ndt/gfq452

Source DB:  PubMed          Journal:  Nephrol Dial Transplant        ISSN: 0931-0509            Impact factor:   5.992


  9 in total

1.  Modeling a Predictive Energy Equation Specific for Maintenance Hemodialysis.

Authors:  Laura D Byham-Gray; J Scott Parrott; Emily N Peters; Susan Gould Fogerite; Rosa K Hand; Sean Ahrens; Andrea Fleisch Marcus; Justin J Fiutem
Journal:  JPEN J Parenter Enteral Nutr       Date:  2017-12-19       Impact factor: 4.016

2.  Development of a predictive energy equation for maintenance hemodialysis patients: a pilot study.

Authors:  Laura Byham-Gray; J Scott Parrott; Wai Yin Ho; Mary B Sundell; T Alp Ikizler
Journal:  J Ren Nutr       Date:  2014-01       Impact factor: 3.655

3.  Nutritional Status and Other Clinical Variables Are Associated to the Resting Energy Expenditure in Patients With Chronic Kidney Disease: A Validity Study.

Authors:  Samuel Ramos-Acevedo; Luis Rodríguez-Gómez; Sonia López-Cisneros; Ailema González-Ortiz; Ángeles Espinosa-Cuevas
Journal:  Front Nutr       Date:  2022-05-18

4.  Resting Energy Expenditure during Breastfeeding: Body Composition Analysis vs. Predictive Equations Based on Anthropometric Parameters.

Authors:  Agnieszka Bzikowska-Jura; Adriana Szulińska; Dorota Szostak-Węgierek
Journal:  Nutrients       Date:  2020-04-30       Impact factor: 5.717

5.  Energy Requirement of Patients Undergoing Hemodialysis: A Cross-Sectional Study in Multiple Centers.

Authors:  Pei-Yu Wu; Yu-Tong Chen; Te-Chih Wong; Hsi-Hsien Chen; Tzen-Wen Chen; Tso-Hsiao Chen; Yung-Ho Hsu; Sheng-Jeng Peng; Ko-Lin Kuo; Szu-Chun Hung; Shwu-Huey Yang
Journal:  Biochem Res Int       Date:  2020-03-21

6.  Current methods for developing predictive energy equations in maintenance dialysis are imprecise.

Authors:  Alainn Bailey; Rebecca Brody; Joachim Sackey; J Scott Parrott; Emily Peters; Laura Byham-Gray
Journal:  Ann Med       Date:  2022-12       Impact factor: 4.709

7.  Decrease in irisin in patients with chronic kidney disease.

Authors:  Ming-Shien Wen; Chao-Yung Wang; Shuei-Liong Lin; Kuo-Chun Hung
Journal:  PLoS One       Date:  2013-05-07       Impact factor: 3.240

8.  A practical approach to dietary interventions for nondialysis-dependent CKD patients: the experience of a reference nephrology center in Brazil.

Authors:  Lilian Cuppari; Fabiana Baggio Nerbass; Carla Maria Avesani; Maria Ayako Kamimura
Journal:  BMC Nephrol       Date:  2016-07-16       Impact factor: 2.388

Review 9.  Indirect Calorimetry in Clinical Practice.

Authors:  Marta Delsoglio; Najate Achamrah; Mette M Berger; Claude Pichard
Journal:  J Clin Med       Date:  2019-09-05       Impact factor: 4.241

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.