scholarly journals Energy Expenditure in Infants in Health and Disease

1997 ◽  
Vol 11 (1) ◽  
pp. 101-104 ◽  
Author(s):  
Ross Shepherd
Keyword(s):  
Cystic Fibrosis ◽  
Energy Expenditure ◽  
Energy Requirement ◽  
Cell Mass ◽  
Resting Energy Expenditure ◽  
Normal Growth ◽  
Theoretical Concept ◽  
Energy Requirements ◽  
Predictive Equations ◽  
Health And Disease

Measurement of energy balance represents a basic theoretical concept in the determination of nutritional and fluid requirements in humans in health and disease. Infants have special nutrient requirements, more limited reserves and relative immaturity of organ function. Energy requirements of infants have been based either retrospectively on intakes required to achieve normal growth or on equations derived from energy expenditure studies performed early this century. Recently, improved techniques for studying resting energy expenditure (REE), total energy expenditure (TEE) and metabolically active body compartments in infants have facilitated more accurate estimates of energy requirements. Such studies indicated that current reference values for energy requirements are overestimates, and that compared with measured values, predicted values vary markedly between the various predictive equations with wide co-efficients of variation. In disease states with altered body composition, such as cystic fibrosis and end-stage liver disease, predictive equations markedly underestimate both energy and fluid requirements. In cystic fibrosis, both TEE and REE are 25% higher than values in healthy infants. In extrahepatic biliary atresia, energy expenditure per unit body cell mass is markedly elevated, suggesting that this is a catabolic condition in infants. Current estimates of energy and fluid requirements in both health and disease in infants need reappraisal. Bedside and free living energy expenditure methodology should be used to define accurately components of energy requirement in individual infants.

scholarly journals Are Predictive Energy Expenditure Equations Accurate in Cirrhosis?

Nutrients ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 334 ◽  
Author(s):  
Tannaz Eslamparast ◽  
Benjamin Vandermeer ◽  
Maitreyi Raman ◽  
Leah Gramlich ◽  
Vanessa Den Heyer ◽  
...  
Keyword(s):  
Energy Expenditure ◽  
English Language ◽  
Resting Energy Expenditure ◽  
Pooled Analysis ◽  
Energy Requirements ◽  
Significant Heterogeneity ◽  
Predictive Equations ◽  
Language Studies ◽  
The Mean ◽  
Mean Differences

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.

scholarly journals Measurement of Resting Energy Expenditure with Indirect Calorimetry and Predictive Equations in Healthy Young Adults: A Cross-sectional Study

2020 ◽  
Author(s):  
harsha soni ◽  
Sudhanshu Kacker ◽  
Neha Saboo ◽  
Karampreet Buttar ◽  
. jitender
Keyword(s):  
Young Adults ◽  
Energy Expenditure ◽  
Indirect Calorimetry ◽  
Resting Energy Expenditure ◽  
Cross Sectional Study ◽  
Energy Requirements ◽  
Predictive Equation ◽  
Sectional Study ◽  
Cross Sectional ◽  
Predictive Equations

Introduction: Resting Energy Expenditure (REE) is the main determinant of energy requirements. An inaccurate estimation of REE can lead to the over or under-prediction of energy requirements. Indirect calorimetry is considered as the gold standard for the assessment of REE. The most of the predictive equations which are formed, are from the studies conducted on Caucasian people while on Asian population these studies are very limited. Aim: To compare the REE measured by indirect calorimetry and predictive equation in healthy young adults. Materials and Methods: A cross-sectional study was done on 100 healthy young adult participants from November 2018 to May 2019, of age group 18 to 25 years to measure REE using indirect calorimetry and predictive equations (Harris-Benedict’s, Schofield, FAO/WHO/UNU and Mifflin-St. Jeor equations). Statistical analysis was carried out using SPSS version 16.0. Unpaired student t-test for comparison of data and Bland Altman test to check for validity of predictive equations were applied. Results: The mean value of REE using Indirect calorimetry was 1994.20±577.33 and that of using four Harris-Benedict’s, Schofield, FAO/WHO/UNU and Mifflin-St.Jeor equations were 1638.15±335.64 kcal/day, 1636.21±359.85 kcal/day, 1636.93±367.59 kcal/day and 1582.41±251.29 kcal/day, respectively. Thus, the highest mean difference between values of REE obtained using predictive equation and indirect calorimetry was 411.79±326.04 kcal/day with respect to Mifflin-St.Jeorand’s and the lowest mean difference was 356.05±241.69 kcal/day with respect to Herris Benedict’s equation. Conclusion: Predictive equations underestimated the REE of young adults when compared with that measured by indirect calorimetry.

scholarly journals Throwing darts in ICU: how close are we in estimating energy requirements?

2020 ◽  
Vol 5 (1) ◽  
pp. e000493
Author(s):  
Laryssa Grguric ◽  
Lisa Musillo ◽  
Jody C DiGiacomo ◽  
Swapna Munnangi
Keyword(s):  
Energy Requirement ◽  
Clinical Care ◽  
Resting Energy Expenditure ◽  
Energy Requirements ◽  
State University ◽  
Level Of Evidence ◽  
Predictive Equations ◽  
Energy Needs ◽  
Final Sample ◽  
Penn State

BackgroundIndirect 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).MethodsThe 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.ResultsPatients, 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).DiscussionThis study identified significant differences between mREE and commonly used predictive equations in the ICU.Level of evidenceIII.

Reliability of resting energy expenditure in major burns: Comparison between measured and predictive equations

2019 ◽  
Vol 38 (6) ◽  
pp. 2763-2769 ◽  
Author(s):  
Jinwoo Jeon ◽  
Dohern Kym ◽  
Yong Suk Cho ◽  
Youngmin Kim ◽  
Jaechul Yoon ◽  
...  
Keyword(s):  
Energy Expenditure ◽  
Resting Energy Expenditure ◽  
Predictive Equations ◽  
Resting Energy

scholarly journals Resting energy expenditure in elite athletes: development of new predictive equations based on anthropometric variables and bioelectrical impedance analysis derived phase angle

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Maurizio Marra ◽  
Olivia Di Vincenzo ◽  
Iolanda Cioffi ◽  
Rosa Sammarco ◽  
Delia Morlino ◽  
...  
Keyword(s):  
Energy Expenditure ◽  
Phase Angle ◽  
Elite Athletes ◽  
Resting Energy Expenditure ◽  
Sport Performance ◽  
Accurate Estimation ◽  
Anthropometric Parameters ◽  
Predictive Equations ◽  
Good Prediction Accuracy ◽  
Resting Energy

Abstract Background An accurate estimation of athletes’ energy needs is crucial in diet planning to improve sport performance and to maintain an appropriate body composition. This study aimed to develop and validate in elite athletes new equations for estimating resting energy expenditure (REE) based on anthropometric parameters as well as bioimpedance analysis (BIA)-derived raw variables and to validate the accuracy of selected predictive equations. Methods Adult elite athletes aged 18–40 yrs were studied. Anthropometry, indirect calorimetry and BIA were performed in all subjects. The new predictive equations were generated using different regression models. The accuracy of the new equations was assessed at the group level (bias) and at the individual level (precision accuracy), and then compared with the one of five equations used in the general population or three athletes-specific formulas. Results One-hundred and twenty-six male athletes (age 26.9 ± 9.1 yrs; weight 71.3 ± 10.9 kg; BMI 22.8 ± 2.7 kg/m2) from different sport specialties were randomly assigned to the calibration (n = 75) or validation group (n = 51). REE was directly correlated with individual characteristics, except for age, and raw BIA variables. Most of the equations from the literature were reasonably accurate at the population level (bias within ±5%). The new equations showed a mean bias −0.3% (Eq. A based on anthropometric parameters) and −0.6% (Eq. B based on BIA-derived raw variables). Precision accuracy (individual predicted-measured differences within ±5%) was ~75% in six out of eight of the selected equations and even higher for Eq. A (82.4%) and Eq. B (92.2%). Conclusion In elite athletes, BIA-derived phase angle is a significant predictor of REE. The new equations have a very good prediction accuracy at both group and individual levels. The use of phase angle as predictor of REE requires further research with respect to different sport specialties, training programs and training level.

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