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.

Predictive Equations Based on Body Composition for Resting Energy Expenditure Estimation in Adults with Obesity

2020 ◽  
Vol 16 (4) ◽  
pp. 381-386
Author(s):  
Dana El Masri ◽  
Leila Itani ◽  
Dima Kreidieh ◽  
Hana Tannir ◽  
Marwan El Ghoch
Keyword(s):  
Body Composition ◽  
Energy Expenditure ◽  
Resting Energy Expenditure ◽  
Fat Free Mass ◽  
Accurate Estimation ◽  
Predictive Equations ◽  
Bland Altman Method ◽  
The Mean ◽  
Mean Differences ◽  
Resting Energy

Background and Aim: An accurate estimation of Resting Energy Expenditure (REE) in patients with obesity is crucial. Therefore, our aim was to assess the validity of REE predictive equations based on body composition variables in treatment-seeking Arab adults with obesity. Methods: Body composition and REE were measured by Tanita BC-418 bioimpedance and Vmax Encore 229 IC, respectively, and predictive equations based on fat mass and fat-free mass were used in REE estimations among 87 adults of both genders, in the Outpatient Clinic in the Department of Nutrition and Dietetics at Beirut Arab University (Lebanon). The mean differences between the measured and estimated REE values were calculated to assess the accuracy, and the Bland-Altman method was used to assess the level of agreement. Results: Ten predictive equations were included. In males, all the predictive equations gave significantly different estimates of REE when compared to that measured by IC. On the other hand, in females, the mean difference between the REE value estimated by Huang and Horie-Waitzberg equations and that measured using IC was not significant, and the agreement was confirmed using Bland-Altman plots. Conclusion: Huang and Horie-Waitzberg equations are suggested for accurate REE estimation in females; however, new validated REE estimation equations for males in this population are still needed.

scholarly journals Resting energy expenditure measured longitudinally following hip fracture compared to predictive equations: is an injury adjustment required?

2005 ◽  
Vol 94 (6) ◽  
pp. 976-982 ◽  
Author(s):  
Michelle D. Miller ◽  
Lynne A. Daniels ◽  
Elaine Bannerman ◽  
Maria Crotty
Keyword(s):  
Hip Fracture ◽  
Energy Expenditure ◽  
Total Energy ◽  
Physiological Stress ◽  
Resting Energy Expenditure ◽  
Mean Difference ◽  
Limits Of Agreement ◽  
Predictive Equations ◽  
The Mean ◽  
Resting Energy

The present study measuring resting energy expenditure (REE; kJ/d) longitudinally using indirect calorimetry in six elderly women aged ≥70 years following surgery for hip fracture, describes changes over time (days 10, 42 and 84 post-injury) and compares measured values to those calculated from routinely applied predictive equations. REE was compared to REE predicted using the Harris Benedict and Schofield equations, with and without accounting for the theoretical increase in energy expenditure of 35 % secondary to physiological stress of injury and surgery. Mean (95 % CI) measured REE (kJ/d) was 4704 (4354, 5054), 4090 (3719, 4461) and 4145 (3908, 4382) for days 10, 42 and 84, respectively. A time effect was observed for measured REE,P=0·003. Without adjusting for stress the mean difference and 95 % limits of agreement for measured and predicted REE (kJ/kg per d) for the Harris Benedict equation were 1 (−9, 12), 10 (2, 18) and 9 (1, 17) for days 10, 42 and 84, respectively. The mean difference and 95 % limits of agreement for measured and predicted REE (kJ/kg per d) for the Schofield equation without adjusting for stress were 8 (−3, 19), 16 (6, 26) and 16 (10, 22) for days 10, 42 and 84, respectively. After adjusting for stress, REE predicted from the Harris Benedict or Schofield equations overestimated measured REE by between 38 and 69 %. Energy expenditure following fracture is poorly understood. Our data suggest REE was relatively elevated early in recovery but declined during the first 6 weeks. Using the Harris Benedict or Schofield equations adjusted for stress may lead to overestimation of REE in the clinical setting. Further work is required to evaluate total energy expenditure before recommendations can be made to alter current practice for calculating theoretical total energy requirements of hip fracture patients.

scholarly journals An easy way to estimate energy expenditure from hemodynamic data in septic patients

2008 ◽  
Vol 23 (suppl 1) ◽  
pp. 112-117 ◽  
Author(s):  
Anibal Basile-Filho ◽  
Maria Auxiliadora Martins ◽  
Flavio Marson ◽  
Paulo Roberto Barbosa Evora
Keyword(s):  
Intensive Care Unit ◽  
Mechanical Ventilation ◽  
Intensive Care ◽  
Oxygen Consumption ◽  
Energy Expenditure ◽  
Resting Energy Expenditure ◽  
University Hospital ◽  
Predictive Equations ◽  
Metabolic Cart ◽  
The Mean

PURPOSE: The purpose of this study is to compare the resting energy expenditure (REE) obtained by indirect calorimetry (IC) to the REE calculated by predictive equations (Brandi and Liggett) using the oxygen consumption (VO2) obtained by Fick's method in septic patients. METHODS: Prospective study in septic patients admitted in an intensive care unit of a university hospital. Fifteen adult patients (11 men and four women) were studied. VO2 measurements were made using a portable metabolic cart connected to a respirator and a pulmonary artery catheter. RESULTS: The APACHE II at admission was 22.6±7.2 with a mortality risk of 46.1±27.7%. The mean REE obtained by IC and by the Brandi and Liggett equations were 1699±271, 1815±355 and 1361±277 kcal.day-1 respectively. There were no statistically significant differences between the two methods, with the two means showing a difference of 8.7%. REE values were close for IC x BRANDI (r=0.80), but the IC x LIGGETT correlation was only 0.58. CONCLUSIONS: The results suggest that REE can be easily calculated by obtaining VO2 with the Swan-Ganz catheter and using the Brandi equation even for septic patients under mechanical ventilation.

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 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.

665 Reliability of Resting Energy Expenditure in Major Burns: Comparison Between Measured and Predictive Equations

2020 ◽  
Vol 41 (Supplement_1) ◽  
pp. S177-S178
Author(s):  
Jun Hur ◽  
Dohern Kym
Keyword(s):  
Energy Expenditure ◽  
Resting Energy Expenditure ◽  
Concordance Correlation Coefficient ◽  
Altman Analysis ◽  
Concordance Correlation ◽  
Predictive Equations ◽  
Severe Burns ◽  
The Mean ◽  
Poor Outcomes ◽  
Resting Energy

Abstract Introduction Poor outcomes can result from inadequate energy intake. We aimed to investigate the reliability of resting energy expenditure (REE) measured by indirect calorimetry (IC) with REE calculated using predictive equations for nutritional support in patients with major burns. Methods REE was measured using IC and compared with predictive equations in 215 adult severe burns patients from Jan 2011 to Jun 2015. Agreement between IC and predictive equations was assessed using Bland-Altman methods. Results All predictive equations were compared with REE measured using IC. The mean measured REE was 1712 kcal/d. Bland-Altman analysis showed that 1.2 times HBE, Thumb 25, and Ireton-Jones equations had higher accuracy and reliability. The concordance correlation coefficient was higher (0.49) in the Ireton-Jones equation, and root mean square error (RMSE) was lowest (471.5) in the Thumb 25 equation. The proportion of patients with predicted REE within ±10% of measured REE was highest with Thumb 25 (52.5%). Other equations for burns patients had higher mean bias and overestimated REE when compared with IC results. Conclusions This study suggests that Thumb 25 can be used as an alternative method for estimating energy requirements of patients with major burns when IC is not available or applicable. Applicability of Research to Practice Harrison-Benedict known as standard equation failed to show superiority to others in burns. Burn-specific equations are tend to overestimate ebergy requirements.

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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