Factors Related to the Assessment of Resting Metabolic Rate in Critically Ill Patients

Abstract

Background Predicting resting metabolic rate (RMR) in mechanically ventilated, critically ill patients is an important part of the nutrition care in such patients. Methods RMR and associated clinical data from various studies of mechanically ventilated, critically ill patients were combined, and the impact of body size, age, reason for admission, and sedation level were analyzed along with prediction methods of RMR (the American Society for Parenteral and Enteral Nutrition [ASPEN] standards and the Penn State equation). Results Among 826 measurements, trauma patients had a higher RMR than surgical and medical patients (2077 ± 290 vs 1987 ± 282 kcal/d; P < .0001). RMR was not different in sedated vs unsedated patients. Wide ranges of weight (27–374 kg) and age (18–95 years) were captured. The relationships between weight and RMR and RMR and age were curvilinear. For weight‐based ratio methods of RMR prediction, <50% of predictions were within the range in which they were designed to work. The accuracy of the Penn State equation was better in some weight categories than others. New equations based on a wider range of body weights and ages are presented. Conclusions Curvilinear functions exist for weight and age in relation to RMR, but extraordinary levels of each are required for the curve to become apparent. The ASPEN energy standards (kcal/kg body weight) fail to predict RMR because the relationship is more complex than a simple ratio. The Penn State equations are better able to model these relationships. The new versions of the equation presented here await validation.

The effect of age and body mass index on energy expenditure of critically ill medical patients

“…Our results are consistent with prior findings that the ACCP recommendations are generally inadequate [11,12,30,34]. Frankenfield has suggested new generation of potential prediction equations to better account for BMI extremes [24]. However, these equations have yet to be validated.…”

“…This study on a large number of critically ill adult medical patients showed a decrease in measured EE with increasing age. Frankenfield has described in his recent detailed retrospective analysis of data from 826 critically ill patients that aging is associated with a nonlinear decrease in resting EE [24]. We have also observed that increasing BMI and female gender were independent determinants of an increase in EE adjusted for IBW after adjustment for several clinical factors.…”

Do we need different predictive equations for the acute and late phases of critical illness? A prospective observational study with repeated indirect calorimetry measurements

“…In the present study, weight and age were the ‘static’ variables selected for use in the prediction models of both the acute and late phases. Older patients may have lower REEs partly because of age-associated changes in body composition and the relative size of fat-free mass (FFM) components [ 27 , 34 , 35 ]. Many studies have shown that body weight and FFM (the metabolizing mass of the body) correlate with REE [ 35 – 37 ].…”

Importancia de la evaluación y terapia nutricional en pacientes críticos y no críticos con COVID-19

“…Por esta razón, la predicción es el método más utilizado en la práctica clínica para determinar el gasto energético (GE). Existen numerosos factores que tienen una influencia en el GE de estos pacientes: tamaño y composición corporal, edad, sedación, tipo de alimentación, patología de base y la temperatura corporal, entre otros 40 . En la tabla 1 se muestran los requerimientos energéticos y proteicos de pacientes con COVID-19 no críticos de acuerdo con las distintas guías internacionales.…”