Resting Energy Expenditure Prediction Equations in the Pediatric Population: A Systematic Review.
ABSTRACT: Background and Aims: The determination of energy requirements is necessary to promote adequate growth and nutritional status in pediatric populations. Currently, several predictive equations have been designed and modified to estimate energy expenditure at rest. Our objectives were (1) to identify the equations designed for energy expenditure prediction and (2) to identify the anthropometric and demographic variables used in the design of the equations for pediatric patients who are healthy and have illness. Methods: A systematic search in the Medline/PubMed, EMBASE and LILACS databases for observational studies published up to January 2021 that reported the design of predictive equations to estimate basal or resting energy expenditure in pediatric populations was carried out. Studies were excluded if the study population included athletes, adult patients, or any patients taking medications that altered energy expenditure. Risk of bias was assessed using the Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. Results: Of the 769 studies identified in the search, 39 met the inclusion criteria and were analyzed. Predictive equations were established for three pediatric populations: those who were healthy (n = 8), those who had overweight or obesity (n = 17), and those with a specific clinical situation (n = 14). In the healthy pediatric population, the FAO/WHO and Schofield equations had the highest R2 values, while in the population with obesity, the Molnár and Dietz equations had the highest R2 values for both boys and girls. Conclusions: Many different predictive equations for energy expenditure in pediatric patients have been published. This review is a compendium of most of these equations; this information will enable clinicians to critically evaluate their use in clinical practice. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=226270, PROSPERO [CRD42021226270].
Project description:<h4>Introduction</h4>Accurate assessment of resting energy expenditure (REE) can guide optimal nutritional prescription in critically ill children. Indirect calorimetry (IC) is the gold standard for REE measurement, but its use is limited. Alternatively, REE estimates by predictive equations/formulae are often inaccurate. Recently, predicting REE with artificial neural networks (ANN) was found to be accurate in healthy children. We aimed to investigate the role of ANN in predicting REE in critically ill children and to compare the accuracy with common equations/formulae.<h4>Study methods</h4>We enrolled 257 critically ill children. Nutritional status/vital signs/biochemical values were recorded. We used IC to measure REE. Commonly employed equations/formulae and the VCO<sub>2</sub>-based Mehta equation were estimated. ANN analysis to predict REE was conducted, employing the TWIST system.<h4>Results</h4>ANN considered demographic/anthropometric data to model REE. The predictive model was good (accuracy 75.6%; R<sup>2</sup> = 0.71) but not better than Talbot tables for weight. After adding vital signs/biochemical values, the model became superior to all equations/formulae (accuracy 82.3%, R<sup>2</sup> = 0.80) and comparable to the Mehta equation. Including IC-measured VCO<sub>2</sub> increased the accuracy to 89.6%, superior to the Mehta equation.<h4>Conclusions</h4>We described the accuracy of REE prediction using models that include demographic/anthropometric/clinical/metabolic variables. ANN may represent a reliable option for REE estimation, overcoming the inaccuracies of traditional predictive equations/formulae.
Project description:<h4>Background</h4>Energy expenditure prediction equations are used to estimate energy intake based on general population measures. However, when using equations to compare with a disease cohort with known metabolic abnormalities, it is important to derive one's own equations based on measurement conditions matching the disease cohort.<h4>Objective</h4>We aimed to use newly developed prediction equations based on a healthy pediatric population to describe and predict resting energy expenditure (REE) in a cohort of pediatric patients with thyroid disorders.<h4>Methods</h4>Body composition was measured by DXA and REE was assessed by indirect calorimetry in 201 healthy participants. A prediction equation for REE was derived in 100 healthy participants using multiple linear regression and z scores were calculated. The equation was validated in 101 healthy participants. This method was applied to participants with resistance to thyroid hormone (RTH) disorders, due to mutations in either thyroid hormone receptor ? or ? (?: female n = 17, male n = 9; ?: female n = 1, male n = 1), with deviation of REE in patients compared with the healthy population presented by the difference in z scores.<h4>Results</h4>The prediction equation for REE = 0.061 * Lean soft tissue (kg) - 0.138 * Sex (0 male, 1 female) + 2.41 (R2 = 0.816). The mean ± SD of the residuals is -0.02 ± 0.44 kJ/min. Mean ± SD REE z scores for RTH? patients are -0.02 ± 1.26. z Scores of -1.69 and -2.05 were recorded in male (n = 1) and female ( n = 1) RTH? patients.<h4>Conclusions</h4>We have described methodology whereby differences in REE between patients with a metabolic disorder and healthy participants can be expressed as a z score. This approach also enables change in REE after a clinical intervention (e.g., thyroxine treatment of RTH?) to be monitored.
Project description:<h4>Aim</h4>We aimed to identify the optimal method to estimate total energy expenditure (TEE) in mitochondrial disease (MD) patients.<h4>Methods</h4>Resting energy expenditure (REE) was measured in MD patients carrying the m3243A>G mutation using indirect calorimetry (IC) and compared with results of 21 predictive equations (PEs) for REE and with REE-IC measurements in healthy controls. Physical activity level (PAL) was measured using accelerometery (SenseWear) and compared with a fixed average PAL (1.4) as well as patients' self-estimated activity levels. TEE was calculated as REE-IC × PAL SenseWear and compared with usual care and energy recommendations for healthy adults.<h4>Results</h4>Thirty-eight MD patients (age: 48 ± 13 years; body mass index 24 ± 4 kg/m<sup>2</sup> ; male 20%) and 25 matched controls were included. The accuracy of most PEs was between 63% and 76%. The difference in REE-IC in healthy controls (1532 ± 182 kcal) and MD patients (1430 ± 221) was borderline not significant (P = .052). Patients' estimations PAL were 18%-34% accurate at the individual level. The fixed activity factor was 53% accurate. Patients overestimated their PAL. Usual care predicted TEE accurately in only 32% of patients.<h4>Conclusion</h4>TEE is lower in these MD patients than the recommendations for healthy adults because of their lower physical activity. In MD patients, 6 PEs for REE provide a reliable alternative for IC, with an accuracy of 71%-76%. As PAL is highly variable and not reliably estimated by patients, measurement of PAL using accelerometery is recommended in this population.
Project description:Elderly patients are at risk of malnutrition and need an appropriate assessment of energy requirements. Predictive equations are widely used to estimate resting energy expenditure (REE). In the study, we conducted a systematic review of REE predictive equations in the elderly population and compared them in an experimental population. Studies involving subjects older than 65 years of age that evaluated the performance of a predictive equation vs. a gold standard were included. The retrieved equations were then tested on a sample of 88 elderly subjects enrolled in an Italian nursing home to evaluate the agreement among the estimated REEs. The agreement was assessed using the intraclass correlation coefficient (ICC). A web application, equationer, was developed to calculate all the estimated REEs according to the available variables. The review identified 68 studies (210 different equations). The agreement among the equations in our sample was higher for equations with fewer parameters, especially those that included body weight, ICC = 0.75 (95% CI = 0.69-0.81). There is great heterogeneity among REE estimates. Such differences should be considered and evaluated when estimates are applied to particularly fragile populations since the results have the potential to impact the patient's overall clinical outcome.
Project description:Having valid and reliable resting energy expenditure (REE) estimations is crucial to establish reachable goals for dietary and exercise interventions. However, most of the REE predictive equations were developed some time ago and, as the body composition of the current population has changed, it is highly relevant to assess the validity of REE predictive equations in contemporary young adults. In addition, little is known about the role of sex and weight status on the validity of these predictive equations. Therefore, this study aimed to investigate the role of sex and weight status in congruent validity of REE predictive equations in young adults. A total of 132 young healthy adults (67.4% women, 18?26 years old) participated in the study. We measured REE by indirect calorimetry strictly following the standard procedures, and we compared it to 45 predictive equations. The most accurate equations were the following: (i) the Schofield and the "Food and Agriculture Organization of the United Nations/World Health Organization/United Nations" (FAO/WHO/UNU) equations in normal weight men; (ii) the Mifflin and FAO/WHO/UNU equations in normal weight women; (iii) the Livingston and Korth equations in overweight men; (iv) the Johnstone and Frankenfield equations in overweight women; (v) the Owen and Bernstein equations in obese men; and (vi) the Owen equation in obese women. In conclusion, the results of this study show that the best equation to estimate REE depends on sex and weight status in young healthy adults.
Project description:<h4>Background</h4>Accurate assessment of energy expenditure may support weight-management recommendations. Measuring energy expenditure for each postpartum woman is unfeasible; therefore, accurate predictive equations are needed.<h4>Objectives</h4>This study compared measured with predicted resting energy expenditure (REE) and total energy expenditure (TEE) in postpartum women.<h4>Methods</h4>This was a longitudinal observational study. REE was measured at 3 mo postpartum (n = 52) and 9 mo postpartum (n = 49), whereas TEE was measured once at 9 mo postpartum (n = 43) by whole body calorimetry (WBC). Measured REE (REEWBC) was compared with 17 predictive equations; measured TEE plus breast milk energy output (ERWBC) was compared with the estimated energy requirements/Dietary Reference Intakes equation (EERDRI). Fat and fat-free mass were measured by dual-energy X-ray absorptiometry. Group-level agreement was assessed by the Pearson correlation, paired t test, and Bland-Altman (bias) analyses. Individual-level accuracy was assessed with the use of Bland-Altman limits of agreement, and by the percentage of women with predicted energy expenditure within 10% of measured values ("accuracy").<h4>Results</h4>The cohort was primarily Caucasian (90%). At a group level, the best equation predicting REEWBC was the DRI at 3 mo postpartum (-7 kcal, -0.1%; absolute and percentage bias, respectively), and the Harris-Benedict at 9 mo postpartum (-17 kcal, -0.5%). At an individual level, the Food and Agriculture Organization/World Health Organization/United Nations University (FAO/WHO/UNU) height and weight equation was the most accurate at 3 mo postpartum (100% accuracy) and 9 mo postpartum (98% accuracy), with the smallest limits of agreement. Equations including body composition variables were not more accurate. Compared with ERWBC, EERDRI bias was -36 kcal, with inaccurate predictions in 33% of women.<h4>Conclusions</h4>Many REE predictive equations were accurate for group assessment, with the FAO/WHO/UNU height and weight equation having the highest accuracy for individuals. EERDRI performed well at a group level, but inaccurately for 33% of women. A greater understanding of the physiology driving energy expenditure in the postpartum period is needed to better predict TEE and ultimately guide effective weight-management recommendations.
Project description:<h4>Background</h4>Contemporary energy expenditure data are crucial to inform and guide nutrition policy in older adults to optimize nutrition and health.<h4>Objective</h4>The aim was to determine the optimal method of estimating total energy expenditure (TEE) in adults (aged ?65 y) through 1) establishing which published predictive equations have the closest agreement between measured resting metabolic rate (RMR) and predicted RMR and 2) utilizing the RMR equations with the best agreement to predict TEE against the reference method of doubly labeled water (DLW).<h4>Methods</h4>A database consisting of international participant-level TEE data from DLW studies was developed to enable comparison with energy requirements estimated by 17 commonly used predictive equations. This database included 31 studies comprising 988 participant-level RMR data and 1488 participant-level TEE data. Mean physical activity level (PAL) was determined for men (PAL = 1.69, n = 320) and women (PAL = 1.66, n = 668). Bland-Altman plots assessed agreement of measured RMR and TEE with predicted RMR and TEE in adults aged ?65 y, and subgroups of 65-79 y and ?80 y. Linear regression assessed proportional bias.<h4>Results</h4>The Ikeda, Livingston, and Mifflin equations most closely agreed with measured RMR and TEE in all adults aged ?65 y and in the 65-79 y and ?80 y subgroups. In adults aged ?65 y, the Ikeda and Livingston equations overestimated TEE by a mean ± SD of 175 ± 1362 kJ/d and 86 ± 1344 kJ/d, respectively. The Mifflin equation underestimated TEE by a mean ± SD of 24 ± 1401 kJ/d. Proportional bias was present as energy expenditure increased.<h4>Conclusions</h4>The Ikeda, Livingston, or Mifflin equations are recommended for estimating energy requirements of older adults. Future research should focus on developing predictive equations to meet the requirements of the older population with consideration given to body composition and functional measures.
Project description:<h4>Background</h4>Data on the influence of age and body mass index (BMI) on energy metabolism of the critically ill are heterogeneous. Due to the increasingly aging critically ill population, investigation on age- and BMI-specific energy metabolism is relevant.<h4>Methods</h4>A total of 394 indirect calorimetry measurements were conducted on 348 critically ill adult medical patients, including 46 repeat measurements after 3.6 ± 4.3 days. Measured resting energy expenditure (MREE) was compared for age groups, BMI, and gender. Predicted energy expenditure (PEE) using the Penn State, Swinamer, and Ireton-Jones equations and the ACCP recommendations was also compared with MREE.<h4>Results</h4>The patients were 65.6 ± 14.5 years old. Their mean Acute Physiology and Chronic Health Evaluation II score was 27.6 ± 7.8. Mean BMI was 27.8 ± 8.4 kg/m<sup>2</sup>, and 25.6% were obese. MREE adjusted for ideal body weight decreased with increasing age, while it increased with increasing BMI. Age, BMI, and gender are independent determinants of MREE after adjusting for clinical factors (R<sup>2</sup> = 0.34). All four prediction equations showed a proportional bias, with the Penn State equation performing acceptably. In 46 patients with repeat indirect calorimetry, there was no significant difference between the first and second MREE (p = 0.62).<h4>Conclusions</h4>Age, BMI, and gender are independent determinants of resting energy expenditure in critically ill adults. Variations between measured and predicted energy expenditure are considerable. Should prediction equations be used, their performance in the specific population should be taken into consideration. Repeat indirect calorimetry may not always be necessary. However, this may depend on the length of stay and the extent of stress.
Project description:Background:Indirect calorimetry (IC) is the gold standard for determining energy requirement. Due to lack of availability in many institutions, predictive equations are used to estimate energy requirements. The purpose of this study is to determine the accuracy of predictive equations (ie, Harris-Benedict equation (HBE), Mifflin-St Jeor equation (MSJ), and Penn State University equation (PSU)) used to determine energy needs for critically ill, ventilated patients compared with measured resting energy expenditure (mREE). Methods:The researchers examined data routinely collected as part of clinical care for patients within intensive care units (ICUs). The final sample consisted of 68 patients. All studies were recorded during a single inpatient stay within an ICU. Results:Patients, on average, had an mREE of 33.9 kcal/kg using IC. The estimated energy requirement when using predictive equations was 24.8 kcal/kg (HBE×1.25), 24.0 kcal/kg (MSJ×1.25), and 26.8 kcal/kg (PSU). Discussion:This study identified significant differences between mREE and commonly used predictive equations in the ICU. Level of evidence:III.
Project description:Malnutrition is associated with significant morbidity and mortality in cirrhosis. An accurate nutrition prescription is an essential component of care, often estimated using time-efficient predictive equations. Our aim was to compare resting energy expenditure (REE) estimated using predictive equations (predicted REE, pREE) versus REE measured using gold-standard, indirect calorimetry (IC) (measured REE, mREE). We included full-text English language studies in adults with cirrhosis comparing pREE versus mREE. The mean differences across studies were pooled with RevMan 5.3 software. A total of 17 studies (1883 patients) were analyzed. The pooled cohort was comprised of 65% men with a mean age of 53 ± 7 years. Only 45% of predictive equations estimated energy requirements to within 90?110% of mREE using IC. Eighty-three percent of predictive equations underestimated and 28% overestimated energy needs by ±10%. When pooled, the mean difference between the mREE and pREE was lowest for the Harris?Benedict equation, with an underestimation of 54 (95% CI: 30?137) kcal/d. The pooled analysis was associated with significant heterogeneity (I2 = 94%). In conclusion, predictive equations calculating REE have limited accuracy in patients with cirrhosis, most commonly underestimating energy requirements and are associated with wide variations in individual comparative data.