Prediction of resting metabolic rate in critically ill patients at the extremes of body mass index

David C. Frankenfield, Christine M. Ashcraft, Dan A. Galvan

Research output: Contribution to journalArticlepeer-review

53 Scopus citations

Abstract

Background: Although estimation of energy needs by mathematical equation is common in practice, there is relatively little validation data for the equations. This is especially true at the upper and lower extremes of body size. The purpose of the current study was to provide validation data for several common equations in underweight and morbidly obese critically ill patients. Methods: In mechanically ventilated, critical care patients with body mass index ≤21.0 or ≥45.0 kg/m2, indirect calorimetry was used to measure resting metabolic rate. Several equation methods were then compared with these measurements, including the Penn State equation, Faisy equation, Ireton-Jones equation, Mifflin-St Jeor equation, Harris-Benedict equation, and American College of Chest Physicians (ACCP) standard using ideal, actual, or metabolically active body weight. Results: Accuracy (percentage of estimates falling within 10% of measured) in the morbidly obese group was highest for the Penn State equation (76%) and lowest for the ACCP standard using actual body weight (0%). For the underweight group, the Penn State equation was accurate 63% of the time, but below a body mass index of 20.5, the accuracy rate dropped to 58%. No other equation was more accurate than this in the underweight patients. Conclusion: The Penn State equation is valid in morbid obesity, but the accuracy rate is much lower in underweight critically ill patients. A modification to the equation is suggested to improve accuracy in this group.

Original languageEnglish (US)
Pages (from-to)361-367
Number of pages7
JournalJournal of Parenteral and Enteral Nutrition
Volume37
Issue number3
DOIs
StatePublished - May 2013

All Science Journal Classification (ASJC) codes

  • Medicine (miscellaneous)
  • Nutrition and Dietetics

Fingerprint

Dive into the research topics of 'Prediction of resting metabolic rate in critically ill patients at the extremes of body mass index'. Together they form a unique fingerprint.

Cite this