Resting Energy Expenditure in the Elderly: Systematic Review and Comparison of Equations in an Experimental Population

Nutrients. 2021 Jan 29;13(2):458. doi: 10.3390/nu13020458.

Abstract

Elderly patients are at risk of malnutrition and need an appropriate assessment of energy requirements. Predictive equations are widely used to estimate resting energy expenditure (REE). In the study, we conducted a systematic review of REE predictive equations in the elderly population and compared them in an experimental population. Studies involving subjects older than 65 years of age that evaluated the performance of a predictive equation vs. a gold standard were included. The retrieved equations were then tested on a sample of 88 elderly subjects enrolled in an Italian nursing home to evaluate the agreement among the estimated REEs. The agreement was assessed using the intraclass correlation coefficient (ICC). A web application, equationer, was developed to calculate all the estimated REEs according to the available variables. The review identified 68 studies (210 different equations). The agreement among the equations in our sample was higher for equations with fewer parameters, especially those that included body weight, ICC = 0.75 (95% CI = 0.69-0.81). There is great heterogeneity among REE estimates. Such differences should be considered and evaluated when estimates are applied to particularly fragile populations since the results have the potential to impact the patient's overall clinical outcome.

Keywords: elderly; energy requirements; estimating equations; predictive equation; systematic review; web tool.

Publication types

  • Comparative Study
  • Systematic Review

MeSH terms

  • Aged
  • Aged, 80 and over
  • Anthropometry
  • Basal Metabolism
  • Calorimetry, Indirect
  • Energy Intake
  • Energy Metabolism
  • Female
  • Geriatric Assessment / methods
  • Geriatric Assessment / statistics & numerical data*
  • Homes for the Aged
  • Humans
  • Male
  • Malnutrition / diagnosis*
  • Nursing Homes
  • Nutrition Assessment*
  • Nutritional Requirements
  • Predictive Value of Tests
  • Reproducibility of Results
  • Rest / physiology
  • Statistics, Nonparametric