Assessment of resting energy expenditure in mechanically ventilated patients

BACKGROUND: Usual equations for predicting resting energy expenditure (REE) are not appropriate for critically ill patients, and indirect calorimetry criteria render its routine use difficult. OBJECTIVE: Variables that might influence the REE of mechanically ventilated patients were evaluated to establish a predictive relation between these variables and REE. DESIGN: The REE of 70 metabolically stable, mechanically ventilated patients was prospectively measured by indirect calorimetry and calculated with the use of standard predictive models (Harris and Benedict's equations corrected for hypermetabolism factors). Patient data that might influence REE were assessed, and multivariate analysis was conducted to determine the relations between measured REE and these data. Measured and calculated REE were compared by using the Bland-Altman method. RESULTS: Multivariate analysis retained 4 independent variables defining REE: body weight (r(2) = 0.14, P < 0.0001), height (r(2) = 0.11, P = 0.0002), minute ventilation (r(2) = 0.04, P = 0.01), and body temperature (r(2) = 0.07, P = 0.002): REE (kcal/d) = 8 x body weight + 14 x height + 32 x minute ventilation + 94 x body temperature - 4834. REE calculated with this equation was well correlated with measured REE (r(2) = 0.61, P < 0.0001). Bland-Altman plots showed a mean bias approaching zero, and the limits of agreement between measured and predicted REE were clinically acceptable. CONCLUSION: Our results suggest that REE estimated on the basis of body weight, height, minute ventilation, and body temperature is clinically more relevant than are the usual predictive equations for metabolically stable, mechanically ventilated patients.     

Predicting resting energy expenditure in patients with musculoskeletal deformities

The aim of this study was to assess the validity of the commonly used equations (Harris-Benedict (HB), Schofield (S) and equations based on midarm circumference (MAC) and midarm muscle circumference (MAMC) in predicting resting energy expenditure (REE) in a population of patients with musculoskeletal deformities. 20 kyphoscoliotic patients (15 female (F); 5 male (M); mean age 59.6 years) and 10 controls (7 F; 3M; 59.8 years) were studied. REE measured by indirect calorimetry (IC) with a ventilated canopy system (Deltatrac metabolic monitor) was not significantly different between patients and controls (Mean (SD) REE (MJ/24 h): Patients: 5.48 (1.1); controls: 5.28(0.8)). In patients with deformities the Schofield equation gave values which were closest to measured REE (mean difference and limits of agreement IC vs S: 0.098 MJ/24 h; -0.822 and 1.018). The Harris-Benedict equation using height (Ht) and armspan (AS) in lieu of height also gave acceptable results (IC vs HB (Ht): 0.34; -0.638 and 1.318; IC vs HB (AS): 0.255; -0.683 and 1.253). Equations based on MAC and MAMC compared poorly (IC vs MAC equation: 0.398; -1.530 and 2.326; IC vs MAMC equation 0.687; -0.911 and 2.285). On regression analysis the equation REE = 0.295 (MAMC) + 0.0483 (AS) -0.0324 (age) -6.25 predicted REE best in the patient population (r(2) = 0.861).     

Increased resting energy expenditure in patients with end-stage renal disease

BACKGROUND: Protein-calorie malnutrition is a significant problem for patients with end-stage renal disease. Increased resting energy expenditure may be an important contributing factor. We postulate that resting energy expen diture in the different stages of renal disease and treatments may be different. METHODS: Resting energy expenditure was measured using a whole-room indirect calorimeter (metabolic chamber) along with nutritional parameters and body composition after 12-hour fasting in 15 patients with advanced chronic renal failure patients, 15 patients on chronic hemodialysis, and 10 patients on peritoneal dialysis. Patients on hemodialysis were assessed on a non-dialysis day. A 2-day dietary recall was used to assess energy intake. RESULTS: Resting energy expenditure, adjusted for fat-free mass, was similar in patients on hemodialysis and peritoneal dialysis but significantly higher than in patients with chronic renal failure (p < .05). Resting energy expenditure in all patients were generally higher (10% to 20%) than predicted values using standard equations derived in normal and obese populations, whereas daily energy intake was less (26% to 34%) than energy expenditure for all groups, adjusted for light daily activity. CONCLUSIONS: End-stage renal disease patients displayed increases in resting energy expenditure over the predicted values derived using normal populations. Resting energy expenditure was significantly higher in patients receiving dialysis, regardless of the modality, than patients with chronic renal failure. Daily energy intake was substantially less than required in all patient groups studied, suggesting that patients with renal failure could develop protein-calorie malnutrition because of increased resting energy expenditure, which is exacerbated by dialysis.     

Measured and predicted resting energy expenditure in clinically stable patients

Resting energy expenditure (REE), measured by bedside indirect calorimetry, was compared to estimated REE by the Harris-Benedict and Kleiber predictors in 200 clinically stable hospitalized patients (100 males, 100 females) and 72 healthy control subjects (20 males, 52 females).Mean predicted values were not significantly different from measured REE for the male patients and control subjects, but measured REE was significantly overestimated by the Kleiber formula in female patients and controls (p<0.01). In comparison to control subjects, a substantially larger range of individual differences between measured REE and resting energy expenditure as estimated by the Harris-Benedict and Kleiber formulae existed among the male and female patient samples. Measured REE was over or underestimated by greater than 10% via the Harris-Benedict predictors in 40% of the patients but only 20% of the healthy controls. The Kleiber formulae were inappropriate for 46% of the individual patients and 33% of the normal subjects.Since no method exists for identifying the clinically stable patient for whom REE cannot be estimated via these commonly employed predictors, the bedside measurement of resting energy expenditure is the most appropriate method for deriving caloric expenditure and designing subsequent caloric provision regimens for adequate and safe nutritional repletion or maintenance.     

Failure of preoperative resting energy expenditure in predicting weight loss after gastroplasty

OBJECTIVE: To evaluate the predictive efficacy of preoperative resting energy expenditure (REE) on weight loss after vertical banded gastroplasty (VBG). When subjected to a gastric restriction procedure of similar extent, the patients with higher energy expenditure should experience a greater negative energy balance than those with lower-energy expenditure, and thus, lose more weight, thereby making REE a reliable predictor of weight loss after VBG. RESEARCH METHODS AND PROCEDURES: This was a prospective investigation after VBG, taking into account the relationship between preoperative REE values and the results at 1-year follow-up in terms of weight loss and success of the procedure. The correlations were evaluated by multiple and logistic regression analysis. RESULTS: The weight loss and the outcome at 1 year after VBG seemed to be completely independent of preoperative energy expenditure. DISCUSSION: These findings suggest that, despite gastric restriction, patients may voluntarily adjust their energy intake, and that the weight outcome after VBG is influenced more by behavioral and cognitive variables than by biological or surgical factors.     

Short-term overfeeding increases resting energy expenditure in patients with HIV lipodystrophy

BACKGROUND: HIV lipodystrophy and other lipodystrophy syndromes are characterized by extensive loss of subcutaneous adipose tissue. Lipodystrophy syndromes are also associated with increased resting energy expenditure (REE). This hypermetabolism may be an adaptive response to an inability to store triacylglycerol fuel in a normal manner. OBJECTIVE: This study was done to determine whether REE increases significantly after short-term overfeeding in patients with HIV lipodystrophy. DESIGN: REE was measured in HIV-infected patients with lipodystrophy (n = 9) and in HIV-infected (n = 10) and healthy (n = 9) controls after 3 d on a eucaloric diet and again after 3 d on a diet of similar composition but increased in calories by 50%. RESULTS: After 3 d of eucaloric feeding, REE was significantly higher in patients with HIV lipodystrophy [33.2 +/- 0.27 kcal/kg lean body mass (LBM)] than for both HIV-infected and healthy controls (29.9 +/- 0.26 and 29.6 +/- 0.27 kcal/kg LBM, respectively; P < 0.01). Furthermore, after 3 d of overfeeding, REE increased significantly in patients with HIV lipodystrophy but not in the control groups (33.2 +/- 0.27 vs 34.7 +/- 0.27 kcal/kg LBM; P < 0.01). Finally, postprandial thermogenesis did not differ among the groups after a "normal" test meal but tended to be higher in patients with HIV lipodystrophy than in healthy controls after a large test meal. CONCLUSIONS: Adaptive thermogenesis in the resting component of total daily energy expenditure and in the postprandial period may be a feature of the HIV lipodystrophy syndrome and may be due to an inability to store triacylglycerol fuel in a normal manner.     

Estimation of resting energy expenditure by anthropometry

The role of anthropometry in estimating resting energy expenditure (REE) has been assessed in 142 clinically stable patients. Ninety eight patients had cancer (54 weight stable, 44 weight losing) and 44 patients had nonmalignant illness (27 weight stable, 17 weight losing). Mid-arm muscle circumference (MAMC) measurements correlated significantly with REE measured by indirect calorimetry in each of the groups studied. Weight loss significantly affected this correlation whereas cancer did not. The correlation in weight stable patients was poorer than that in weight losing patients, possibly reflecting inaccuracy of anthropometric measurements due to subcutaneous adipose tissue. Significant correlations were also observed between mid-arm circumference (MAC) and REE, and between MAMC and whole body oxygen consumption.REE can be estimated from MAMC measurements in weight stable and weight losing patients with benign or malignant disease. This simple method may be of value in estimating REE where indirect calorimetry facilities are unavailable.     

Intra- and interindividual variability of resting energy expenditure in healthy male subjects -- biological and methodological variability of resting energy expenditure

The objective of the present study was to investigate the contribution of intra-individual variance of resting energy expenditure (REE) to interindividual variance in REE. REE was measured longitudinally in a sample of twenty-three healthy men using indirect calorimetry. Over a period of 2 months, two consecutive measurements were done in the whole group. In subgroups of seventeen and eleven subjects, three and four consecutive measurements were performed over a period of 6 months. Data analysis followed a standard protocol considering the last 15 min of each measurement period and alternatively an optimised protocol with strict inclusion criteria. Intra-individual variance in REE and body composition measurements (CV(intra)) as well as interindividual variance (CV(inter)) were calculated and compared with each other as well as with REE prediction from a population-specific formula. Mean CV(intra) for measured REE and fat-free mass (FFM) ranged from 5.0 to 5.6 % and from 1.3 to 1.6 %, respectively. CV(intra) did not change with the number of repeated measurements or the type of protocol (standard v. optimised protocol). CV(inter) for REE and REE adjusted for FFM (REE(adj)) ranged from 12.1 to 16.1 % and from 10.4 to 13.6 %, respectively. We calculated total error to be 8 %. Variance in body composition (CV(intra) FFM) explains 19 % of the variability in REE(adj), whereas the remaining 81 % is explained by the variability of the metabolic rate (CV(intra) REE). We conclude that CV(intra) of REE measurements was neither influenced by type of protocol for data analysis nor by the number of repeated measurements. About 20 % of the variance in REE(adj) is explained by variance in body composition.     

An evaluation of resting energy expenditure in hospitalized, severely underweight patients.

A prospective trial was conducted with 14 hospitalized patients who were severely underweight with a mean weight of 40.9+/-5.1 kg and 70.7+/-7.8% of ideal body weight, to compare estimates of resting energy expenditure (REE) with measured values. The 9 women and 3 men, whose mean age was 66.5+/-13.9 y, underwent nutritional assessment and measurement of their REE by indirect calorimetry using the Sensormedics Deltatrac MBM100 indirect calorimeter. Their REE was also estimated by the Harris-Benedict formula (mean 1032+/-66 kcal/d) as well as a previously established empirical formula where REE = 25 x body weight in kg (mean 1023+/-129 kcal/d). Results by both estimates were significantly lower than the measured resting energy expenditure (MREE) in this group of patients (P<0.0001). The percentage difference between MREE and estimated REE by the Harris-Benedict formula was 18.4+/-9.4% and 20.9+/-7.5% by the empirical formula. The MREE exceeded the estimated REE in each individual. The correlation between MREE and body weight (r2 = 0.558, r = 0.005) was better than that between MREE and estimated REE by Harris-Benedict formula (r2 = 0.275, P = 0.08) suggesting that weight was the principal determinant rather than the other components (height, age, sex) of the Harris-Benedict formula. Our data shows that commonly employed formulae routinely underestimate the energy needs of severely underweight patients below 50 kg in body weight. The Harris-Benedict equation had limited predictive value for the individual, explaining approximately 25% of the variance in energy expenditure. Given the particular importance of matching energy intake to needs in this group of patients with limited reserves, many of whom are critically ill, we suggest an empirical equation using 30-32 kcal/kg be used to estimate the energy requirements of severely underweight patients when direct measurements are unavailable or clinically less imperative.     

Lean-body-mass composition and resting energy expenditure before and after long-term overfeeding.

This report deals with the association between the constituents of lean body mass (LBM) and resting metabolic rate (RMR) before and after a 100-d overfeeding period. Computed-tomography (CT) scan of 22 young adult males at nine different body levels were used to estimate adipose tissue mass (ATMCT), LBMCT, skeletal-muscle mass (SMMCT), and non-muscular LBMCT (NM-LBMCT). Before overfeeding, all body constituents, except ATMCT, were significantly correlated with RMR. Only body mass changes were significantly correlated with RMR changes. Comparison of these results with those of several studies in the literature reveals that the relationship between RMR and fat-free mass is highly influenced by the size of the SD for the latter variable. In stepwise-multiple-regression analysis, only SMMCT could be used to predict RMR. It was concluded that SMMCT and ATMCT, but not NM-LBMCT, increased during overfeeding and that the best correlates of RMR remain LBMCT, SMMCT, and body mass.     

Correction of anemia in patients with congestive heart failure increases resting energy expenditure

BACKGROUND & AIM: Congestive heart failure (CHF) and anemia were reported to affect resting energy expenditure (REE). The aim of this study was to evaluate the effect of the correction of anemia on REE in subjects with CHF. PATIENTS AND METHODS: Nine anemic patients with compensated CHF and CRF were studied before and after correction of anemia. REE was studied by an open circuit indirect calorimeter, body composition by dual-energy-X-ray absorption and total body and extracellular water by multi-frequency bioelectrical impedence. Four anemic and 5 non-anemic CHF patients who did not receive any new treatment served as controls. RESULTS: After the correction of their anemia patients tended to increase weight (P<0.06), but no significant changes were observed in body composition. Daily caloric intake increased significantly (P<0.02). Ejection fraction increased (P<0.05) and pulse rate decreased significantly (P<0.001). REE and REEPP were in the normal range before correction but increased significantly afterwards (1402+/-256 vs. 1496+/-206 kcal/d, and 101+/-9 vs. 109+/-8, P<0.023 and P<0.006, respectively). CONCLUSION: Correction of anemia in patients with CHF increases their REE. This can be related either to improved tissue oxygenation and/or to increased caloric intake.     

Accuracy of predictive methods to estimate resting energy expenditure of thermally-injured patients

BACKGROUND: The purpose of this study was to evaluate the bias and precision of 46 methods published from 1953 to 2000 for estimating resting energy expenditure (REE) of thermally injured patients. METHODS: Twenty-four adult patients with > or =20% body surface area burn admitted to a burn center who required specialized nutrition support and who had their REE measured via indirect calorimetry (IC) were evaluated. Patients with morbid obesity, human immunovirus, malignancy, pregnancy, hepatic or renal failure, neuromuscular paralysis, or those requiring a FiO2 >50% or positive end expiratory pressure (PEEP) > or =10 cm H2O were excluded. One steady-state measured REE measurement (MEE) was obtained per patient. The methods of Sheiner and Beal were used to assess bias and precision of these methods. The formulas were considered unbiased if the 95% confidence interval (CI) for the error (kilocalories per day) intersected 0 and were considered precise if the 95% CI for the absolute error (%) was within 15% of MEE. RESULTS: MEE was 2780+/-567 kcal/d or 158%+/-34% of the Harris Benedict equations. None of the methods was precise (< or =15% CI error). Over one-half (57%) of the 46 methods had a 95% confidence interval error >30% of the MEE. Forty-eight percent of the methods were unbiased, 33% were biased toward overpredicting MEE, and 19% consistently underpredicted MEE. The pre-1980s methods more frequently overpredicted MEE compared with the 1990 to 2000 (p < .01) and 1980 to 1989 (p < .05) published methods, respectively. The most precise unbiased methods for estimating MEE were those of Milner (1994) at a mean error of 16% (CI of 10% to 22%), Zawacki (1970) with a mean error of 16% (CI of 9% to 23%), and Xie (1993) at a mean error of 18% (CI of 12% to 24%). The "conventional 1.5 times the Harris Benedict equations" was also unbiased and had a mean error of 19% (CI of 9% to 29%). CONCLUSIONS: Thermally injured patients are variably hypermetabolic and energy expenditure cannot be precisely predicted. If IC is not available, the most precise, unbiased methods were those of Milner (1994), Zawacki (1970), and Xie (1993).     

机械通气的外科危重症患者静息能量消耗评价

目的比较机械通气的外科危重症患者测定的静息能耗(MREE)与校正Harris-Benedict公式计算的静息能耗(CREE)之间的差异,评估静息能耗与疾病严重程度的相关性.方法选取2008年8月至2010年2月符合入选标准的外科危重症患者21例.收集患者相关数据,计算急性生理与既往健康状况评分(APACHEⅡ评分)和器官功能不全评分(Marshall评分).采用美国MedGraphics CCM/D系统间接能耗测量仪测定MREE,采用校正Harris-Benedict公式计算CREE.结果营养支持1周内,21例患者的平均CREE明显高于平均MREE[(8305.09±1392.76)kJ比(6544.84±2079.65) kJ,P=0.000].营养支持当日和第1、2(P均=0.000)、4天(P =0.003)的CREE明显高于MREE.CREE与MREE之间无相关性(r=0.064,P=0.408),MREE与APACHEⅡ评分也无相关性(r=-0.045,P=0.563).MREE与Marshall评分有相关性(P =0.001),但相关系数较低(r=0.263).结论基于病情校正的Harris-Benedict公式明显高估了外科危重症患者的能耗水平,间接能耗仪测定的静息能耗更为准确.     

Normal value of resting energy expenditure in healthy neonates

OBJECTIVE: We investigated the value of resting energy expenditure (REE) in healthy neonates and evaluated the impact factors on REE. METHODS: One hundred eighty healthy neonates (95 boys and 85 girls) with birth weights above 2500 g were measured by indirect calorimetry, and the effect of birth weight evaluated. Measured and predicted REEs were compared, and the effects of sex and delivery method on REE were examined in 154 newborn infants with birth weights of approximately 2500 to 4000 g. RESULTS: Birth weight had a significant effect on REE. There was a negative relation between REE and birth weight (r = -0.289). The REEs of newborn infants weighing more than 4000 g were statistically lower than those of infants weighing 2500 to 4000 g (44.5 +/- 5.9 versus 48.3 +/- 6.1 kcal x kg(-1) x d(-1), P = 0.01). The measured and predicted REEs of 154 newborn infants were 48.3 +/- 6.1 and 54.1 +/- 1.1 kcal x kg(-1) x d(-1), respectively. There was a significant difference between the two values. Sex and delivery methods had no effect on REE in healthy neonates. CONCLUSIONS: The value from the predicted equation is not suitable for neonatal energy supplementation in clinical practice. The normal REE value for healthy neonates with birth weights of 2500 to 4000 g is 48.3 +/- 6.1 kcal x kg(-1) x d(-1).     

Measurement of resting energy expenditure in a clinical setting

In this study indirect calorimetry for the measurement of a patient's resting energy expenditure (REE) was assessed in clinical practice. REE measured early in the morning after an overnight fast was highly reproducible. REE measured in the afternoon, when patients had consumed their meals, was 15% higher than REE measured in the morning. REE measured at mid-morning was not different from that measured early in the morning, except for patients who had breakfast between the two measurements. Therefore, to avoid the effect of diet-induced thermogenesis in the measurement a patient must be measured in the morning in the post-absorptive state. Variations because of limited physical activities may be neglected, including a short travel from home to the hospital, which implies that REE may be measured on an out-patient basis. The effect of total parenteral nutrition (TPN) on energy expenditure (EE) was a 12% increase. The respiratory quotient (RQ) rose to almost 1.0. Nine days of enteral nutritional support showed only a 3% increase in REE, while RQ increased from 0.78 to 0.87, indicating restored glycogen stores.