Predictive equations versus measured energy expenditure by indirect calorimetry: A retrospective validation

Abstract

Measuring resting energy expenditure (REE) via indirect calorimetry (IC) in intensive care unit (ICU) patient is the gold standard recommended by guidelines. However technical difficulties hinder its use and predictive equations are largely used instead. We sought to validate commonly used equations using a large cohort of patients. Patients hospitalized from 2003 to 2015 in a 16-bed ICU at a university-affiliated, tertiary care hospital who had IC measurement to assess caloric targets were included. Data was drawn from a computerized system and included REE and other variables required by equations. Measurements were restricted to 5 REE per patient to avoid bias. Equation performance was assessed by comparing means, standard deviations, correlation, concordance and agreement, which was defined as a measurement within 85-115% of measured REE. A total of 8 equations were examined. A total of 3573 REE measurements in 1440 patients were included. Mean patient age was 58 years and 65% were male. A total of 562 (39%) patients had >2 REE measurements. Standard deviation of REE ranged from 430 to 570kcal. The Faisy equation had the least mean difference (90Kcal); Harris-Benedict had the highest correlation (52%) and agreement (50%) and Jolliet the highest concordance (62%). Agreement within 10% of caloric needs was met only in a third of patients. Predictive equations have low performance when compared to REE in ICU patients. We therefore suggest that predictive equations cannot wholly replace indirect calorimetry for the accurate estimation of REE in this population.