External Validation of Equations to Estimate Resting Energy Expenditure in Critically Ill Children and Adolescents with and without Malnutrition: A Cross-Sectional Study

Abstract

We evaluated the validity of sixteen predictive energy expenditure equations for resting energy expenditure estimation (eREE) against measured resting energy expenditure using indirect calorimetry (REEIC) in 153 critically ill children. Predictive equations were included based on weight, height, sex, and age. The agreement between eREE and REEIC was analyzed using the Bland–Altman method. Precision was defined by the 95% limits of the agreement; differences > ±10% from REEIC were considered clinically unacceptable. The reliability was assessed by the intraclass correlation coefficient (Cronbach’s alpha). The influence of anthropometric, nutritional, and clinical variables on REEIC was also assessed. Thirty (19.6%) of the 153 enrolled patients were malnourished (19.6%), and fifty-four were overweight (10.5%) or obese (24.8%). All patients received sedation and analgesia. Mortality was 3.9%. The calculated eREE either underestimated (median 606, IQR 512; 784 kcal/day) or overestimated (1126.6, 929; 1340 kcal/day) REEIC compared with indirect calorimetry (928.3, 651; 1239 kcal/day). These differences resulted in significant biases of −342 to 592 kcal (95% limits of agreement (precision)−1107 to 1380 kcal/day) and high coefficients of variation (up to 1242%). Although predicted equations exhibited moderate reliability, the clinically acceptable ±10% accuracy rate ranged from only 6.5% to a maximum of 24.2%, with the inaccuracy varying from −31% to +71.5% of the measured patient’s energy needs. REEIC (p = 0.017) and eREE (p < 0.001) were higher in the underweight compared to overweight and obese patients. Apart from a younger age, malnutrition, clinical characteristics, temperature, vasoactive drugs, neuromuscular blockade, and energy intake did not affect REEIC and thereby predictive equations’ accuracy. Commonly used predictive equations for calculating energy needs are inaccurate for individual patients, either underestimating or overestimating REEIC compared with indirect calorimetry. Altogether these findings underscore the urgency for measuring REEIC in clinical situations where accurate knowledge of energy needs is vital.

Determining energy and protein needs in critically ill pediatric patients: A scoping review

“…The Schofield equation, with weight and height, showed the best performance for the entire group and subgroups according to age, sex, nutrition status, and organ failure. Among 16 tested equations in 153 children (median age: 7.5 years), 32 the majority, especially the Recommended Dietary Allowances, overestimated REE measured by E‐COVX. In contrast to the above findings, the Schofield weight and height equation overestimated REE with one of the highest bias, and the HB equations had a smaller bias but still had wide limits of agreement.…”

“…40 In terms of factors that may affect the accuracy of predictive equations, two recent studies, involving one-third of overweight or obese patients, showed that apart from age, inaccuracy of the equations was not correlated to factors such as the presence of overweight or obesity, diagnostic categories including trauma, or disease severity. 32,33 Factors impacting EE (subquestion 1c)…”

“…The relationship between EE and diagnosis remains indeterminate, but several studies do not show an association. 32,35,57 These findings may explain why predictive equations specifically developed for critically ill children, that is, the equations of Meyer et al 45 and White et al, 67 Findings regarding sedation, neuromuscular blockade, inotropes, vasopressors, and caloric intake were not completely consistent. Most studies have observed a positive correlation between body temperature and EE and a negative association for neuromuscular blockade and sedation.…”

“…As expected, the available data showed positive correlations between weight and EE and age and EE. 5,32,34,35,44 These variables are included in most, if not all, of the standard predictive equations of REE for children and adults. Among the dynamic clinical factors, there was good agreement for the severity of illness among the included studies, concluding in the absence of an association with EE.…”

Oxygen Consumption Predicts Long-Term Outcome of Patients with Left Ventricular Assist Devices

“…Our study has several limitations due to the retrospective analysis of prospectively and automatically collected data. Moreover, we assessed VO 2 via the PAC and did not use indirect calorimetry, which is known as the gold standard for measuring VO 2 , carbon dioxide production and resting energy expenditure [ 20 , 21 ]. Studies correlating VO 2 values measured via the PAC in comparison to indirect calorimetry remain outstanding.…”