Predictive versus measured energy expenditure using limits-of-agreement analysis in hospitalized, obese patients

Accuracy of predictive formulae is crucial for therapeutic planning because indirect calorimetry measurement is not always possible or cost effective. Energy requirements are more difficult to predict in the acutely ill obese patient compared with lean patients because of an increased resting energy expenditure per lean body mass and a variable stress response to illness.

A retrospective review of 726 patients identified 57 patients (32 spontaneous breathing, S; 25 ventilator dependent, V) with body mass indexes of 30-50 kg/m2. Limits-of-agreement analysis determined bias (the mean difference between measured and predicted values) and precision (the standard deviation of the bias) to evaluate the accuracy of predictive formulae compared with measured resting energy expenditure (MREE) by a Deltatrac Metabolic Monitor. Predictive accuracy was determined within+/-10% MREE. The predictive formulae examined were: variations of the Harris-Benedict equations using ideal, adjusted weights of 25% and 50% and actual weights with stress factors ranging from 1.0 to 1.5; the Ireton-Jones equation for obesity; the Ireton-Jones equations for hospitalized patients (S and V); and the ratio of 21 kcalories per kilogram actual weight.

The mean MREE was 21 kcal/kg actual weight. The adjusted Harris-Benedict average weight equation was optimal for predicting MREE for the combined S and V sets (bias = 182+/-123; 67%+/-10% MREE), as well as the S subset (bias = 159+/-112; 69%+/-10% MREE).

The Harris-Benedict equations using the average of actual and ideal weight and a stress factor of 1.3 most accurately predicted MREE in acutely ill, obese patients with BMIs of 30-50 kg/m2. Predictive formulae were least accurate for obese, ventilator-dependent patients.