Measured Resting Energy Expenditure Values Research Articles

Resting energy expenditure in Korean type 2 diabetes patients: comparison between measured and predicted values.

Estimation of energy demand using resting energy expenditure (REE) is a reasonable approach for optimizing glycemic control and weight management in patients with type 2 diabetes mellitus (T2DM). This study aimed to compare REE predictions and objective measurements in patients with T2DM in Korea. This study enrolled 36 participants with T2DM (age range, 20-60 years). Anthropometric variables including height, weight, waist-hip ratio, blood pressure, body fat, body fat percentage, and total body weight were measured using bioimpedance. REE was evaluated using indirect calorimetry. The measured REE values were compared to values estimated using five predictive equations: the Harris-Benedict, Mifflin, Owen, Food and Agriculture Organization of the United Nations/World Health Organization (FAO/WHO), and Schofield equations. This study evaluated the associations between measured REE values and anthropometric/clinical data, including height, weight, and age, using multivariate linear regression. The mean measured REE value was 1891.79 ± 288.03 kcal/day (male), 1,502.00 ± 202.96 kcal/day (female). REE estimates generated from the Mifflin equation showed the largest differences from measured REE values, whereas estimates derived from the FAO/WHO equation were the closest to the measured REE values. This study also identified associations between measured REE values and various anthropometric/clinical variables. The accuracy of REE prediction equations is critically important in promoting the efficacy of dietary counseling and the effective treatment of diabetes. Our results indicate the need for additional studies informing more suitable methods for determining the energy requirements of Korean patients with T2DM.

POS-726 NOVEL EQUATIONS FOR ESTIMATING RESTING ENERGY EXPENDITURE IN PERITONEAL DIALYSIS PATIENTS

With the development of chronic kidney disease (CKD), the risk of protein-energy wasting is increasing. It is urgent to have a convenient method to assess energy expenditure accurately in these patients. However, besides the direct measurement, which is expensive and time-consuming, there is no resting energy expenditure (REE) equations focus on peritoneal dialysis patients. We aimed to develop and validate REE equations in peritoneal dialysis patients. This cross-sectional study enrolled 140 patients with peritoneal dialysis according to inclusion and exclusion criteria. After random sampling matched 1:1 by age, gender and dialysis duration, stepwise linear regression analysis was used to derive a new REE equation (eREEpd) according to actual REE (aREE) measured using indirect calorimetry in the development dataset. The eREEpd value was then validated with aREE in the validation dataset in terms of bias, precision, and accuracy. Bias was assessed as the difference between the estimated and measured REE values, precision was assessed as the absolute value of difference, and accuracy was assessed as the percentage of estimates that differed >20% from the measured REE (1- P20). The eREEpd equation: eREEpd (kcal) = (1 if male; 0 if female) × 144.08 − 5.8 × age (y) + 10.33 × weight (kg)+ 850.71 (R2 = 0.649). The bias, precision, and accuracy of the eREEpd equation were -11.3 (IQR: −195.7, 115.0) kcal, 22.2 (IQR:64.9, 249.3) kcal, and 20.3%, respectively with indirect calorimetry as the reference method. Besides, we found hemoglobin was an independent predictive factor of REE developed an REE equation using age, sex, weight, and hemoglobin data, as eREEpd_Hb (kcal) = (1 if male; 0 if female) × 172.16 − 4.87 × age (y) + 9.63 × weight (kg)-3.05×hemoglobin (g/L)+ 1180.04 (R2 = 0.673).The bias, precision, and accuracy (percentage of estimates that differed >20% from the measured REE) of the eREEpd_Hb equation were 31.5 (IQR: −174.3, 102.0) kcal, 120.8 (IQR:59.6, 239.2) kcal, and 18.6%, respectively with indirect calorimetry as the reference method. The REE equation using age, sex, weight with or without hemoglobin data could serve as a reliable tool for estimating REE in patients with peritoneal dialysis.

Resting energy expenditure in cancer patients: Agreement between predictive equations and indirect calorimetry

Disorders of energy metabolism is a common phenomenon in cancer patients. Changes in resting energy expenditure (REE) combined with inadequate nutrition support appear to be causes of nutritional depletion in cancer patients. In clinical practice, REE is typically calculated using predictive equations. The aim of this study was to determine the agreement between REE estimated by predictive equations and REE measured by IC in Portuguese cancer patients. Differences in measured REE between patients with different types of digestive cancers were also assessed. REE was measured by indirect calorimetry (IC) in 61 patients with cancer diagnosis (gastric cancer, cholangiocarcinoma, pancreatic cancer, liver cancer and colorectal cancer). Measured REE values were compared with those estimated by equations of Harris-Benedict, Schofield, Ireton-Jones, Mifflin-St.Jeor and Barcellos I and II. Mean Respiratory Quotient (RQ) was 0.77±0.09, which indicates high lipids utilization as substrate. No statistically significant differences between REE or RQ from patients with different cancer types were observed. All equations underestimate REE: Harris-Benedict, mean difference-648 kcal (limits of agreement+627 to-1923 kcal); MifflinSt.Jeor, mean difference-694 kcal (limits of agreement+544 to-193 kcal); Schofield, mean difference-531 kcal (limits of agreement+662 to-1723 kcal); and Ireton-Jones, mean difference-556 kcal (limits of agreement+774 to-1887 kcal). Barcellos I and II showed lower mean difference when compared to measured REE,+59 and+52kcal, respectively, although presenting wide limits of agreement,+1542 to-1424 kcal and+1429 to-1326, respectively. Although Barcellos Equations underestimate less and enable more accurate average REE prediction in cancer patients, still present wide limits of agreement and therefore clinically important differences in REE estimation may be found at individual level. Our results support the appropriateness of measuring REE by IC to better adequate the nutrition support to cancer patients. Further research is needed to improve the current knowledge base of energy expenditure in cancer patients, and to improve the accuracy of existing predictive equations.

Comparison of Measured Energy Expenditure Using Indirect Calorimetry vs Predictive Equations for Liver Transplant Recipients.

BackgroundTo assess the appropriate energy expenditure requirement for liver transplant (LT) recipients in South Korea, 4 commonly used predictive equations were compared with indirect calorimetry (IC).MethodsA prospective observational study was conducted in the surgical intensive care unit (ICU) of an academic tertiary hospital between December 2017 and September 2018. The study population comprised LT recipients expected to remain in the ICU >48 hours postoperatively. Resting energy expenditure (REE) was measured 48 hours after ICU admission using open‐circuit IC. Theoretical REE was estimated using 4 predictive equations (simple weight‐based equation [25 kcal/kg/day], Harris‐Benedict, Ireton‐Jones [ventilated], and Penn State 1988). Derived and measured REE values were compared using an intraclass correlation coefficient (ICC) and Bland‐Altman plots.ResultsOf 50 patients screened, 46 were enrolled, were measured, and completed the study. The Penn State equation showed 65.0% agreement with IC (ICC, 0.65); the simple weight‐based (25 kcal/kg/day), Harris‐Benedict, and Ireton‐Jones equations showed 62.0%, 56.0% and 39.0% agreement, respectively. Bland‐Altman analysis showed that all 4 predictive equations had fixed bias, although the simple weight‐based equation (25 kcal/kg/day) showed the least.ConclusionAlthough predicted REE calculated using the Penn State method agreed with the measured REE, all 4 equations showed fixed bias and appeared to be inaccurate for predicting REE in LT recipients. Precise measurement using IC may be necessary when treating LT recipients to avoid underestimating or overestimating their metabolic needs.

New equation to estimate resting energy expenditure in non-critically ill patients

Correct measurement of resting energy expenditure (REE) is essential to offer a proper nutritional management during hospital stay. Dietitians are not able to perform an effective dietary treatment if predicted REE values are obtained from invalid equations. The aim of this study was to develop a more valid method to estimate REE in non-critically ill Portuguese patients. In this cross-sectional study, REE was measured by indirect calorimetry (IC) in 180 non-critically patients during hospital stay (50 participants were allocated to the validation group by simple randomization and the remaining 130 were allocated to the derivation group). The best accurate equations were derived by multiple linear regression analysis (stepwise) based on anthropometric variables. The equations were tested on the validation group and compared with published predictive equations. Data was collected from 130 patients, 68 women (52.3%) and 62 men (47.7%), mean age was 58.9±16.8 years and REE-IC was 1918±721kcal/day. The new best-fit equation REE (kcal/day)=14.4 (Height)+52.7 (MUAC)+453.4 (1 if male, 0 if female) - 371.2 (if Obese) - 2138.3 showed strength of evidence decisive (BF₁₀=8008), when compared by Bayesian model, and r2=0.315. Only estimated REE values obtained using new equations did not present significant difference when compared with measured REE values (kcal/kg). In this study, new equations derived from a non-critically ill population showed higher validity in estimating REE than currently used equations. A better estimation of REE may lead to a better nutritional intervention and a decreased risk of undernutrition in hospitalized patients.

Spinal Muscular Atrophy, types I and II: What are the differences inbody composition and resting energy expenditure?

SummaryBackground & aimsDifferent neuromuscular functional domains in types I and II Spinal Muscular Atrophy (SMAI and SMAII) could lead to differences in body composition (BC) and resting energy expenditure (REE). Their identification could provide the key to defining appropriate strategies in clinical dietary management, but data comparing SMAI and SMAII in terms of BC and REE are not yet available. We measured total and regional fat (FM), lean (LBM), mineral (BMC) masses, body water (total, intra- and extra-cellular, TBW, ICW, ECW) and REE in a sample of SMAI and II children, matched for age and sex, and also adjusting for body size to compare these features of the two SMA phenotypes.Methods15 SMAI and 15 SMAII children, (M/F = 9/6 vs 9/6, age 3.6 ± 1.9 vs 3.5 ± 1.8 years, p = 0.99), confirmed genetically, were measured as follows: Anthropometric measurements [Body Weight (BW), Supine Length (SL), Arm Length (AL), Femur Length (FL), Tibia Length (TL)], Dual x-ray Energy Absorptiometry (DEXA) [total and segmental FM, LBM, FFM, and BMC], Bioelectrical impedance (BIA) [TBW, ICW, ECW] and Indirect Calorimetry (REE, respiratory quotients) were collected by the same trained dietician. BW, SL and Body Mass Index (BMI) Z-scores were calculated according to CDC Growth Charts (2000).ResultsSMA children had high percentages of FM and a lower percentage of TBW and ECW compared to the respective reference values for sex and age, whereas the BMC percentages did not differ, even splitting the two phenotypes. SMA I children had a lower BW and BMI-Z score compared to children with SMA II, but similar total and segmental FM. On the contrary, total FFM and LBM were significantly lower in SMAI (7290.0 ± 1729.1 g vs 8410.1 ± 1508.4 g; 6971.8 ± 1637.1 g vs 8041.7 ± 1427.7 g, p = 0.039, p = 0.037, respectively), particularly at the trunk level. Arm BMC also resulted significantly lower in SMAI. The measured REE values were similar (684 ± 143 kcal/day vs 703 ± 122 Kcal/day p = 0.707) whereas REE per FFM unit was higher in SMA I children than in SMA II (95 ± 12 kcal/FFMkg vs 84 ± 11 kcal/FFMkg p = 0.017).ConclusionsThis study has shown that BW and BMI Z-score measurements alone can be misleading in assessing nutritional status, particularly in SMAI. The differences between SMAI and II in total and regional BC are related only to FFM, LBM and BMC, and seem to be more linked to the magnitude of neurofunctional impairment rather than to the nutritional status derangement. SMA I and SMA II children can have different energy requirements in relation to their specific BC and hypermetabolism of FFM. Based on these results, our recommendation is to use direct BC and REE measurements in the nutritional care process until SMA-specific predictive equations become available.

Increased Resting Energy Expenditure after Endovascular Coiling for Subarachnoid Hemorrhage

Appropriate nutritional care from the acute stage is essential for improved functional outcomes and reduced mortality in patients with subarachnoid hemorrhage (SAH). Although endovascular coiling is increasingly being used as an alternative to neurosurgical clipping and craniotomy for ruptured aneurysms, the resting energy expenditure (REE) of patients treated with this new technique has not been systemically evaluated. We measured REE values by indirect calorimetry in 12 SAH patients treated with endovascular coiling. We averaged the REE measurements obtained on days 1 and 7 after endovascular coiling, and then we statistically compared the mean REE values with those in 30 patients with acute cerebral infarction (ACI) by the Wilcoxon rank-sum test (P <.05). Next, we calculated the ratio of measured REE values to the values estimated using the Harris-Benedict equation to adjust for demographic differences in sex, weight, height, and age between the groups. The ratios were significantly higher in SAH patients (median value, 1.12; interquartile range, 1.05-1.23) than in ACI patients (median value, 1.02; interquartile range, .97-1.09). Because endovascular coiling is less invasive than neurosurgical clipping, the observed increase in REE was attributed to metabolic changes after SAH. To provide optimal nutritional care to SAH patients from the acute stage, clinicians should be aware of this change in REE.

Resting Energy Expenditure in Critically Ill Patients With Spontaneous Intracranial Hemorrhage.

Data on energy requirements of patients with spontaneous intracranial hemorrhage (SICH) are scarce. The objective of this study was to determine the resting energy expenditure (REE) in critically ill patients with SICH and to compare it with the predicted basal metabolic rate (BMR). In 30 nonseptic patients with SICH, the REE was measured during the 10 first posthemorrhage days with the use of indirect calorimetry (IC). Predicted BMR was also evaluated by the Harris-Benedict (HB) equation. Bland-Altman analysis was used to evaluate the agreement between measured and predicted values. The possible effect of confounding factors (demographics, disease, and severity of illness score) on the evolution of continuous variables was also tested. mean predicted BMR, calculated by the HB equation, was 1580.3 ± 262 kcal/d, while measured REE was 1878.9 ± 478 kcal/d (117.5% BMR). Compared with BMR, measured REE values showed a statistically significant increase at all studied points (P < .005). Measured and predicted values showed a good correlation (r = 0.73, P < .001), but the test of agreement between the 2 methods with the Bland-Altman analysis showed a mean bias (294.6 ± 265.6 kcal/d) and limits of agreement (-226 to 815.29 kcal/d) that were beyond the clinically acceptable range. REE values presented a trend toward increase over time (P = .077), reaching significance (P < .005) after the seventh day. Significant correlation was found between REE and temperature (P = .002, r = 0.63), as well as between REE and cortisol level (P = .017, r = 0.62) on the 10th day. No correlation was identified between REE and depth of sedation, as well as Acute Physiology and Chronic Health Evaluation II, Glasgow Coma Scale, and Hunt and Hess scores. During the early posthemorrhagic stage, energy requirements of critically ill patients with SICH are increased, presenting a trend toward increase over time. Compared with IC, the HB equation underestimates energy requirements and is inefficient in detecting individual variability of REE in this group of patients.

Accuracy of a simplified equation for energy expenditure based on bedside volumetric carbon dioxide elimination measurement – A two-center study

Accurate assessment of resting energy expenditure (REE) and metabolic state is essential to optimize nutrient intake in critically ill patients. We aimed to examine the accuracy of a simplified equation for predicting REE using carbon dioxide elimination (VCO2) values. We conducted a two-center study of metabolic data from mechanically ventilated children less than 18 years of age. Mean respiratory quotient (RQ) from the derivation set (n = 72 subjects) was used to modify the Weir equation to obtain a simplified equation based on VCO2 measurements alone. This equation was then applied to subjects at the second institution (validation dataset, n = 94) to predict resting energy expenditure. Bland-Altman analysis was used to assess the agreement between measured REE values, and REE estimated by the new equation as well as the Schofield equation. We also examined the accuracy of the new equation in classifying patients according to their metabolic state. Mean respiratory quotient (± SD) of 0.89 ± 0.09 in the derivation set was used to obtain a simplified equation, REE (kcal/day) = 5.534*VCO2 (L/min)*1440. In relation to the measured REE in the validation set, the mean bias (limits of agreement) for the REE predicted by this equation was -0.65% (-14.4-13.1%); and the overall diagnostic accuracy for classifying subjects as hypometabolic or hypermetabolic was 84%. Mean bias (limits) of agreement between measured and Schofield equation estimated REE was -0.1% (-40.5-40.7%). A simplified metabolic equation using VCO2 values was superior to the standard equation in estimating REE, and provided a reasonably accurate metabolic classification in mechanically ventilated children. In the absence of indirect calorimetry, bedside VCO2 monitoring could provide valuable continuous metabolic information to guide optimal nutrient intake.

Resting Energy Expenditure in a Controlled Group of Young Arab Females: Correlations with Body Composition and Agreement with Prediction Equations

Objectives: To assess correlates of body compositions measures and resting energy expenditure (REE) in young Arab females, and to compare measured REE values with values calculated from REE predictive equations. Methods: Seventy nine healthy women, aged 18 - 30 years, were recruited for the study. All volunteers fasted for 8 hours, abstained from vigorous physical activity, smoking and caffeinated beverages for twelve hours before measuring body composition and REE. Resting energy expenditure was measured by indirect calorimetry and body composition was measured by a bioelectrical impedance analysis. Results: Measured-REE was significantly correlated with body fat mass, fat free mass, skeletal muscle mass, and soft lean mass (R2 ranges 0.498 - 0.592; p p ?101.9 kcal, p p > 0.05). Mean measured REE varied significantly with BMI (p Conclusion: All body composition measures were significantly correlated with REE measured. Mifflin-St. Jeor equation showed the closest estimate to the measured REE in predicting REE of participants who had a normal weight or were overweight. Harris-Benedict equation significantly overestimated REE and Owen significantly underestimated REE.

Predictive equation of resting energy expenditure in obese adult Taiwanese

To examine the accuracy of present resting energy expenditure (REE) predictive equations among obese adult Taiwanese, and obtain new predictive equations for this population. A total of 80 participants (men = 42, women = 38) aged 18-64 years (mean 41.5 ± 13.1 years) with body mass index (BMI) ≥30 kg/m(2) were recruited from obesity clinic of a tertiary hospital. Anthropometric data including weight, height, waist circumference (WC), hip circumference (HipC) were collected. Body fat and fat free mass (FFM) were measured using bioelectric impedance analysis. Indirect calorimeter (Vmax 29n) was used for measurement of REE. The commonly used predictive equations (Harris-Benedict equation, Bernstein equation, Mifflin equation, Owen equation, Schofield equation, Cunningham equation, Wang equation, and Liu equation) were tested for group means and individual prediction accuracy for our target population. New predictive equations were formulated using multiple linear regression analyses. The mean BMI was 34.3 ± 4.6 kg/m(2). All of the commonly used predictive equations showed statistically significant differences with the measured REE values. As for individual prediction accuracy, these predictive equations showed poor performance and only Mifflin equation provided an individual accuracy over 40%. The new predictive equation recommended for obese adult Taiwanese is below: REE (kcal/d) = 11.6 × weight (kg) - 204.84 × gender - 4.8 × height (cm) + 1594.6 (gender: men = 1; women = 2). The previous commonly used predictive equations were less accurate in obese Taiwanese. For this specific population, the newly developed equation should be applied for better prediction of REE.

Assessement of resting energy expenditure of obese patients: Comparison of indirect calorimetry with formulae

Determining the caloric expenditure of overweight (Body mass index - BMI from 25-29.9 kg/m(2)) or obese (BMI> or =30 kg/m(2)) patients is a difficult task. The importance of the measurement using indirect calorimetry (IC) is to avoid the administration of an inadequate caloric load, which can increase clinical morbidity. The aim of this study was to compare the resting energy expenditure (REE) obtained by IC with that estimated by the Harris-Benedict (HB) and the Ireton-Jones (IJ) equations and a fixed amount of 21 kcal/kg of body weight, using actual, average and adjusted weights. A total of 71 IC measurements were prospectively performed in 44 patients using the "DELTATRAC II" calorimeter. In 53 of the measurements the patients were on mechanical ventilation and 18 were performed using the canopy, in the fasting state and 24h after achieving calculated caloric requirements. The best REE prediction value in the fasting state was obtained with the HB equation using the actual body weight (HB/ABW), 1873+/-484 kcal/day (p=0.49) when compared to the REE of 1798+/-495 kcal/day measured by IC. After achieving caloric needs, the best REE prediction values were using the HB/ABW equation, 1873+/-484 kcal/day (p=0.56) and the IJ equation with the adjusted body weight, 2103+/-580 kcal/day (p=0.19) when compared to 1948+/-507 kcal/day measured by IC. Although, when the above results were plotted on the Bland and Altman limits of agreement test their average bias were low, but the ranges of their absolute values from the observed average agreement were large, > or =531 kcal. From these data we can conclude that even though the average values obtained with the equations utilized to predict the REE for this population, showed good correlation with those by IC, as they had the best comparative absolute results, however they demonstrated an unacceptable variability when matched to the measured REE values.

High REE in Sumo Wrestlers Attributed to Large Organ-Tissue Mass

It is unknown whether high resting energy expenditure (REE) in athletes is attributable to changes in organ-tissue mass and/or metabolic rate. The purpose of this study was to examine the contribution of organ-tissue mass of fat-free mass (FFM) components to REE for Sumo wrestlers who have large FFM and REE. We investigated the relationship between the REE measured by indirect calorimetry and the REE calculated from organ-tissue mass using a previously published approach. Ten Sumo wrestlers and 11 male untrained college students (controls) were recruited to participate in this study. FFM was estimated by two-component densitometry. Contiguous magnetic resonance imaging (MRI) images with a 1-cm slice thickness were obtained from the top of head to the ankle joints, and the cross-sectional area and volume were determined for skeletal muscle (SM), liver, kidney, and brain. The volume of adipose tissue, heart, and residual was calculated from each equation. The volume units were converted into mass by an assumed constant density. The measured REE was determined by indirect calorimetry. The calculated REE was estimated as the sum of individual organ-tissue masses (seven body compartments) multiplied by their metabolic rate constants. The measured REE for Sumo wrestlers (2286 kcal x d(-1)) was higher (P<0.01) than for controls (1545 kcal x d(-1)). Sumo wrestlers had a greater amount of FFM and FFM components (e.g., SM, liver, and kidney), except for brain. The ratio of measured REE to FFM and the measured REE adjusted by FFM were similar between the two groups. The measured REE values for Sumo wrestlers were not significantly different from the calculated REE values. The high REE for Sumo wrestlers can be attributed not to an elevation of the organ-tissue metabolic rate, but to a larger absolute amount of low and high metabolically active tissue including SM, liver, and kidney.

Equation to estimate resting energy expenditure in adolescents with sickle cell anemia

Basal energy requirements are higher in adolescents with sickle cell anemia (SCA) than in healthy control subjects. However, no equation is available to accurately predict their energy needs. Our objective was to develop a clinically useful equation to estimate resting energy expenditure (REE) in adolescents with SCA. REE and other components of total energy expenditure were measured in adolescents with SCA (n = 37) and in control subjects (n = 23) for 24 h in a whole-room indirect calorimeter. Multiple linear regression analysis was used to describe the relations of REE with independent variables such as sex, weight, height, fat-free mass, fat mass, age, and hemoglobin concentration in adolescents with SCA. The Bland-Altman comparison technique was used to compare values predicted by existing equations with measured REE values. Mean (+/-SD) measured REEs were 7746 +/- 974 and 6332 +/- 869 kJ/d in the male and female subjects with SCA, respectively, and these values were 16% higher than those in the healthy control subjects. Standard equations underestimated REE by 12% (P </= 0.001) in adolescents with SCA and overestimated REE by 4% in control subjects (P = 0.01-0.29). Several REE regression equations for SCA were developed. The most parsimonious equation for males was REE (kJ/d) = 5461 + 77.7.weight (kg) - 233.2.hemoglobin (g/dL), or REE (kcal/d) = 1305 + 18.6.weight (kg) - 55.7.hemoglobin (g/dL). For females, the most parsimonious equation was REE (kJ/d) = 4603 + 55.6.weight (kg) - 126.2.hemoglobin (g/dL), or REE (kcal/d) = 1100 + 13.3.weight (kg) - 30.2.hemoglobin (g/dL). The new equations have better agreement with the basal metabolic needs of adolescents with SCA than do standard predictive equations.

Elevated Resting Energy Expenditure in Adolescents With Sickle Cell Anemia

Objective To assess the reliability of standard prediction equations in estimating resting energy expenditure (REE) values in adolescents with sickle cell anemia. Subjects/design Body composition and metabolic measurements were performed in 8 adolescents, aged 11 to 18 years, with homozygous sickle cell anemia. REE was measured by indirect calorimetry under standard conditions, and measurements were compared with 4 prediction formulas (Harris-Benedict, Schofield, Mayo Clinic, and Food and Agriculture Organization/World Health Organization/United Nations University). Fat-free mass was measured to assess REE per unit of actively metabolizing tissue. Fat-free mass was expressed as a mean of values obtained by densitometry, deuterium dilution, 40K-counting, and total body electrical conductivity. Statistical analyses Repeated measures analysis of variance was performed to determine whether measured REE values and predicted values differed. The Fischer test was used to identify which predicted values differed significantly from the measured REE. Results All 4 prediction formulas significantly underestimated REE. Group mean values for the prediction formulas ranged from 83% to 89% of the measured value. REE averaged 47.7±10.0 kcal/kg fat-free mass per day, which is 30% to 50% higher than reported values in healthy adolescent populations. Conclusions These data suggest that REE is elevated in adolescents with sickle cell anemia. Standard equations used to predict REE are unreliable in these patients. Applications REE in patients with sickle cell anemia is best determined by indirect or direct measurement of energy expenditure. Clinically useful formulas to estimate REE should be developed for patients with conditions, including sickle cell anemia, where the metabolic rate may be altered. J Am Diet Assoc. 1999;99:195–199.