Prediction of Resting Metabolic Rate in Critically Ill Patients at the Extremes of Body Mass Index

Background: Although estimation of energy needs by mathematical equation is common in practice, there is relatively little validation data for the equations. This is especially true at the upper and lower extremes of body size. The purpose of the current study was to provide validation data for several common equations in underweight and morbidly obese critically ill patients. Methods: In mechanically ventilated, critical care patients with body mass index ≤21.0 or ≥45.0 kg/m², indirect calorimetry was used to measure resting metabolic rate. Several equation methods were then compared with these measurements, including the Penn State equation, Faisy equation, Ireton‐Jones equation, Mifflin–St Jeor equation, Harris‐Benedict equation, and American College of Chest Physicians (ACCP) standard using ideal, actual, or metabolically active body weight. Results: Accuracy (percentage of estimates falling within 10% of measured) in the morbidly obese group was highest for the Penn State equation (76%) and lowest for the ACCP standard using actual body weight (0%). For the underweight group, the Penn State equation was accurate 63% of the time, but below a body mass index of 20.5, the accuracy rate dropped to 58%. No other equation was more accurate than this in the underweight patients. Conclusion: The Penn State equation is valid in morbid obesity, but the accuracy rate is much lower in underweight critically ill patients. A modification to the equation is suggested to improve accuracy in this group.     

Longitudinal Prediction of Metabolic Rate in Critically Ill Patients

Background: Indirect calorimetry is the criterion method for assessment of energy expenditure in critically ill patients but is decidedly uncommon. Thus, calculation methods proliferate. Even if indirect calorimetry is available, it usually is not repeated more than weekly on the same patient, creating potential for error. The purpose of the current study was to quantify estimation errors against indirect calorimetry measurements in critically ill patients over time. Methods: In mechanically ventilated, critical care patients, indirect calorimetry was used to measure resting metabolic rate for 7 days. Three estimation methods were compared with the cumulative measurement: the Penn State equations, the American College of Chest Physicians (ACCP) standard (25 kcal/kg body weight), and an extrapolated value based on the first measurement multiplied by 7 days. Results: The cumulative difference between measured resting metabolic rate and the rate predicted by the Penn State equations was ?468 ± 642 kcal (–3.7% ± 5.1% of the measured value). The difference for the ACCP was smaller, but variation was much wider (–387 ± 1597 kcal or ?2.2% ± 11.9% of the measured value). The extrapolated value was ?684 ± 1731 kcal (–4.1% ± 11.4% of measured expenditure). Conclusion: On average, the Penn State equations predict resting metabolic rate over time within 5% of the measured value. This performance is similar to the practice of making 1 measurement and extrapolating it over 1 week. The ACCP method has an unacceptably wide limit of agreement.     

Predictive Equation to Estimate Resting Metabolic Rate in Older Chilean Women

Resting metabolic rate (RMR) depends on body fat-free mass (FFM) and fat mass (FM), whereas abdominal fat distribution is an aspect that has yet to be adequately studied. The objective of the present study was to analyze the influence of waist circumference (WC) in predicting RMR and propose a specific estimation equation for older Chilean women. This is an analytical cross-sectional study with a sample of 45 women between the ages of 60 and 85 years. Weight, height, body mass index (BMI), and WC were evaluated. RMR was measured by indirect calorimetry (IC) and %FM using the Siri equation. Adequacy (90% to 110%), overestimation (>110%), and underestimation (<90%) of the FAO/WHO/UNU, Harris–Benedict, Mifflin-St Jeor, and Carrasco equations, as well as those of the proposed equation, were evaluated in relation to RMR as measured by IC. Normal distribution was determined according to the Shapiro–Wilk test. The relationship of body composition and WC with RMR IC was analyzed by multiple linear regression analysis. The RMR IC was 1083.6 ± 171.9 kcal/day, which was significantly and positively correlated with FFM, body weight, WC, and FM and inversely correlated with age (p < 0.001). Among the investigated equations, our proposed equation showed the best adequacy and lowest overestimation. The predictive formulae that consider WC improve RMR prediction, thus preventing overestimation in older women.     

Estimation of Resting Energy Expenditure Using Predictive Equations in Critically Ill Children: Results of a Systematic Review

Provision of adequate energy intake to critically ill children is associated with improved prognosis, but resting energy expenditure (REE) is rarely determined by indirect calorimetry (IC) due to practical constraints. Some studies have tested the validity of various predictive equations that are routinely used for this purpose, but no systematic evaluation has been made. Therefore, we performed a systematic review of the literature to assess predictive equations of REE in critically ill children. We systematically searched the literature for eligible studies, and then we extracted data and assigned a quality grade to each article according to guidelines of the Academy of Nutrition and Dietetics. Accuracy was defined as the percentage of predicted REE values to fall within ±10% or ±15% of the measured energy expenditure (MEE) values, computed based on individual participant data. Of the 993 identified studies, 22 studies testing 21 equations using 2326 IC measurements in 1102 children were included in this review. Only 6 equations were evaluated by at least 3 studies in critically ill children. No equation predicted REE within ±10% of MEE in >50% of observations. The Harris–Benedict equation overestimated REE in two‐thirds of patients, whereas the Schofield equations and Talbot tables predicted REE within ±15% of MEE in approximately 50% of observations. In summary, the Schofield equations and Talbot tables were the least inaccurate of the predictive equations. We conclude that a new validated indirect calorimeter is urgently needed in the critically ill pediatric population.)     

Energy Expenditure in Critically Ill Elderly Patients: Indirect Calorimetry vs Predictive Equations

Background: Predictive equations (PEs) are used for estimating resting energy expenditure (REE) when the measurements obtained from indirect calorimetry (IC) are not available. This study evaluated the degree of agreement and the accuracy between the REE measured by IC (REE‐IC) and REE estimated by PE (REE‐PE) in mechanically ventilated elderly patients admitted to the intensive care unit (ICU). Methods: REE‐IC of 97 critically ill elderly patients was compared with REE‐PE by 6 PEs: Harris and Benedict (HB) multiplied by the correction factor of 1.2; European Society for Clinical Nutrition and Metabolism (ESPEN) using the minimum (ESPENmi), average (ESPENme), and maximum (ESPENma) values; Mifflin–St Jeor; Ireton‐Jones (IJ); Fredrix; and Lührmann. Degree of agreement between REE‐PE and REE‐IC was analyzed by the interclass correlation coefficient and the Bland‐Altman test. The accuracy was calculated by the percentage of male and/or female patients whose REE‐PE values differ by up to ±10% in relation to REE‐IC. Results: For both sexes, there was no difference for average REE‐IC in kcal/kg when the values obtained with REE‐PE by corrected HB and ESPENme were compared. A high level of agreement was demonstrated by corrected HB for both sexes, with greater accuracy for women. The best accuracy in the male group was obtained with the IJ equation but with a low level of agreement. Conclusions: The effectiveness of PEs is limited for estimating REE of critically ill elderly patients. Nonetheless, HB multiplied by a correction factor of 1.2 can be used until a specific PE for this group of patients is developed.     

Comparison of Resting Energy Expenditure Prediction Methods With Measured Resting Energy Expenditure in Obese,Hospitalized Adults

Background: Several methods are available to estimate caloric needs in hospitalized, obese patients who require specialized nutrition support; however, it is unclear which of these strategies most accurately approximates the caloric needs of this patient population. The purpose of this study was to determine which strategy most accurately predicts resting energy expenditure in this subset of patients. Methods: Patients assessed at high nutrition risk who required specialized nutrition support and met inclusion and exclusion criteria were enrolled in this observational study. Adult patients were included if they were admitted to a medical or surgical service with a body mass index ≥ 30 kg/m². Criteria excluding patient enrollment were pregnancy and intolerance or contraindication to indirect calorimetry procedures. Investigators calculated estimations of resting energy expenditure for each patient using variations on the following equations: Harris‐Benedict, Mifflin–St. Jeor, Ireton‐Jones, 21 kcal/kg body weight, and 25 kcal/kg body weight. For nonventilated patients, the MedGem handheld indirect calorimeter was used. For ventilated patients, the metabolic cart was used. The primary endpoint was to identify which estimation strategy calculated energy expenditures to within 10% of measured energy expenditures. Results: The Harris‐Benedict equation, using adjusted body weight with a stress factor, most frequently estimated resting energy expenditure to within 10% measured resting energy expenditure at 50% of patients. Conclusion: Measured energy expenditure with indirect calorimetry should be employed when developing nutrition support regimens in obese, hospitalized patients, as estimation strategies are inconsistent and lead to inaccurate predictions of energy expenditure in this patient population.     

Toward the Development of Predictive Equations for Resting Metabolic Rate in Acutely Ill Spontaneously Breathing Patients

Background: There are many equations used for calculating energy needs of nutrition support patients but few developed specifically for the subset of spontaneously breathing acutely ill patients. The purpose of the current study was to validate existing equations and to start developing new equations for this cohort. Methods: Acutely ill patients not requiring mechanical ventilation had their resting metabolic rate measured using an indirect calorimeter. Metabolic rate was also calculated using the Mifflin–St Jeor equation, the Ireton‐Jones equation for spontaneously breathing patients, and a modification of the Penn State equation in which the minute ventilation‐dependent variable was removed. These calculated values were compared with measured expenditure and considered accurate if they fell within 10% of the measurement. Results: Fifty‐five patients were measured successfully. The modified Penn State equation was accurate in 71% of patients compared with 44% for Ireton‐Jones and 42% for Mifflin–St Jeor. Several forms of a new equation were outlined but not validated. The equation with the highest R² (0.82) was as follows: resting metabolic rate (kcal/d) = weight in kg (20) ? age in years (3) + male sex (197) + body mass index in kg/m² (25.9) + mean heart rate in beats/min (9.4) + 89. Conclusions: A modification of the Penn State equation for predicting resting metabolic rate was shown to accurately predict resting metabolic rate in acutely ill, spontaneously breathing patients if body mass index was ≥20.5 kg/m². A new set of population‐specific equations was outlined but should not be used until validated.     

Energy Estimation and Measurement in Critically Ill Patients

The estimation of caloric needs of critically ill patients is usually based on energy expenditure (EE), while current recommendations for caloric intake most often rely on a fixed amount of calories. In fact, during the early phase of critical illness, caloric needs are probably lower than EE, as a substantial proportion of EE is covered by the non‐inhibitable endogenous glucose production. Hence, the risk of overfeeding is higher during the early phase than the late phase, while the risk of underfeeding is higher during the late phase of critical illness. Therefore, an accurate measurement of EE can be helpful to prevent early overfeeding and late underfeeding. Available techniques to assess EE include predictive equations, calorimetry, and doubly labeled water, the reference method. The available predictive equations are often inaccurate, while indirect calorimetry is difficult to perform for several reasons, including a shortage of reliable devices and technical limitations. In this review, the authors intend to discuss the different techniques and the influence of the method used on the interpretation of the results of clinical studies.     

Resting Energy Expenditure in Critically Ill Patients With Spontaneous Intracranial Hemorrhage

Background: 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). Methods: 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. Results: 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. Conclusions: 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.     

Validation Study of Energy Requirements in Critically Ill,Obese Cancer Patients

Background: Current guidelines from the American Society for Parenteral and Enteral Nutrition and the Society of Critical Care Medicine (ASPEN/SCCM) regarding caloric requirements and the provision of nutrition support in critically ill, obese adults may not be suitable for similar patients with cancer. We sought to determine whether the current guidelines accurately estimate the energy requirements, as measured by indirect calorimetry (IC), of critically ill, obese cancer patients. Materials and Methods: This was a retrospective validation study of critically ill, obese cancer patients from March 1, 2007, to July 31, 2010. All patients ≥18 years of age with a body mass index (BMI) ≥30 kg/m² who underwent IC were included. We compared the measured energy expenditure (MEE) against the upper limit of the recommended guideline (25 kcal/kg of ideal body weight [IBW]) and MEE between medical and surgical patients in the intensive care unit. Results: Thirty‐three patients were included in this study. Mean MEE (28.7 ± 5.2 kcal/kg IBW) was significantly higher than 25 kcal/kg IBW (P < .001), and 78% of patients had nutrition requirements greater than the current guideline recommendations. No significant differences in MEE between medical and surgical patients in the ICU were observed. Conclusions: Critically ill, obese cancer patients require more calories than the current guidelines recommend, likely due to malignancy‐associated metabolic variations. Our results demonstrate the need for IC studies to determine the energy requirements in these patients and for reassessment of the current recommendations.     

Characteristics of Resting Metabolic Rate in Critically Ill,Mechanically Ventilated Adults With Cystic Fibrosis

Background: Critically ill patients with cystic fibrosis may be especially sensitive to the negative consequences of overfeeding and underfeeding, yet there is almost no information available about the energy needs of these patients. The purpose of this study was to characterize the metabolic rate of critically ill adult patients with cystic fibrosis requiring mechanical ventilation. Methods: This was an observational study in which the resting metabolic rate, oxygen consumption, and carbon dioxide production of adult patients with cystic fibrosis requiring critical care, sedation, and mechanical ventilation were measured with indirect calorimetry. This group was compared with a cohort of adult critical care patients without cystic fibrosis. Results: Twelve patients with cystic fibrosis were identified and measured. These were compared with a control group of 25 critically ill patients. Both groups were underweight (body mass index, 17.4 ± 4.0 kg/m² in cystic fibrosis and 18.4 ± 2.3 kg/m² in control). Adjusting for differences in age, sex, height, and weight, there was no difference in resting metabolic rate between the cystic fibrosis and control groups (1702 ± 193 vs 1642 ± 194 kcal/d, P = .388). Measured resting metabolic rate matched predicted values 58% of the time in cystic fibrosis and 60% of the time in control. Conclusions: The resting metabolic rate of sedated adult patients with cystic fibrosis being assisted with mechanical ventilation is not different from that of adult critical care patients without cystic fibrosis. In both these underweight groups, accurate prediction of resting metabolic rate is difficult to obtain.     

Another step in the search for better oral rehydration solutions.

Objective: To compare resting metabolic rate (RMR) measured by indirect calorimetry versus RMR predicted by several published formulas in a sample of healthy young women.

Methods: RMR was measured using indirect calorimetry and predicted using 6 commonly used equations (Nelson, 1992; Mifflin, 1990; Owen, 1986; Schofield_Weight, 1985; Schofield_{Weight and Height}, 1985; Harris-Benedict, 1919) in 47 reportedly healthy young females (age = 22.8 ± 2.9 years; body mass index = 21.8 ± 2.1 kg/m²). Comparisons between measured versus predicted RMR were conducted using paired t tests, and agreement using Pearson's correlation coefficient, analysis of variance, and the method of Bland-Altman.

Results: All 6 equations overestimated measured RMR by 140–738 kcal/d (all p < 0.001). The proportion of subjects for whom measured versus predicted RMR differed by ±10% ranged from 74% (Nelson) to 100% (Harris-Benedict). The adjusted coefficients of determination (R²) between measured and predicted RMR ranged from 0.13 to 0.19 (all p < 0.05). Bland-Altman analysis R² values ranged from 0.03 (p = 0.233; Harris-Benedict) to 0.72 (p = 0.000; Owen). Given its continued popularity, we modified the Harris-Benedict equation (RMR_{modified Harris-Benedict} (kcal/d) = 738 / (RMR_{Harris-Benedict} ? 738)). Doing so reduced the mean difference between measured and predicted RMR from +738 kcal/d to ?0.53 kcal/d (p = 0.984).

Conclusion: No equation performed well, and none should be used interchangeably with measured RMR. We recommend that a new equation be validated for, and prospectively tested in, young women. In the interim, RMR should be measured in this population or predicted using the modified Harris-Benedict equation that we developed.     

Measurement of Resting Energy Expenditure in Healthy Children

Background: The role that the components of energy expenditure play in the etiology of childhood obesity has highlighted the need for greater accuracy and standardized protocols for the measurement of resting energy expenditure (REE). However, protocols used to assess REE in children are varied, and consensus on a suitable method for measuring REE in children has not been reached. This study was undertaken to determine the effect of measurement time and measurement device (mask or mouthpiece) on REE in healthy children. Design: Following a 12‐hour fast and abstinence from exercise, 23 children (age, 7–12 years) completed two 35‐minute protocols: one with a face mask and the other with a mouthpiece/noseclip. Energy expenditure was measured continuously via indirect calorimetry, while device acceptability was assessed using a 6‐point comfort rating scale. Results: Repeated measures ANOVA indicated that there was no significant difference in REE when measured after 10, 15, 20, or 25 minutes of rest compared to 30 minutes for either the mask or mouthpiece/noseclip (REE range, 1371–1460 kcal/d). Examination of the percentage coefficient of variation (CV) in energy expenditure for each time period by device showed that the least variation existed after 20 minutes of measurement using the mask (CV 6%). Paired t test analysis indicated significantly less discomfort when wearing the mask compared to the mouthpiece/noseclip. Conclusion: It would appear that a 20‐minute protocol using a mask may increase compliance and prove to be a more practical protocol for measuring REE in children.     

Obese Women Have a High Carbohydrate Intake without Changes in the Resting Metabolic Rate in the Luteal Phase

Hormonal changes are caused by the menstrual cycle phases, which influence resting metabolic rate and eating behavior. The aim of the study was to determine resting metabolic rate (RMR) and its association with dietary intake according to the menstrual cycle phase in lean and obese Chilean women. This cross-sectional analytical study included 30 adult women (15 lean and 15 with obesity). Body composition was measured with a tetrapolar bioelectrical impedance meter. Nutritional status was determined by adiposity. A 24-h recall of three nonconsecutive days verifies dietary intake. The RMR was measured by indirect calorimetry. All measurements were performed in both the follicular and luteal phases of the menstrual cycle. Statistical analyses were performed with STATA software at a significance level, which was α = 0.05. The RMR (β = 121.6 kcal/d), temperature (β = 0.36 °C), calorie intake (β = 317.1 kcal/d), and intake of lipids (β = 13.8 g/d) were associated with the luteal phase in lean women. Only extracellular water (β = 1.11%) and carbohydrate consumption (β = 45.2 g/d) were associated in women with obesity. Lean women showed increased RMR, caloric intake, and lipid intake during the luteal phase. For women with obesity, carbohydrate intake increased but not RMR.     

Validation of the Society of Critical Care Medicine and American Society for Parenteral and Enteral Nutrition Recommendations for Caloric Provision to Critically Ill Obese Patients

Background:The Society of Critical Care Medicine (SCCM) and American Society for Parenteral and Enteral Nutrition (ASPEN) recommend that obese, critically ill patients receive 11–14 kcal/kg/d using actual body weight (ABW) or 22–25 kcal/kg/d using ideal body weight (IBW), because feeding these patients 50%‐70% maintenance needs while administering high protein may improve outcomes. It is unknown whether these equations achieve this target when validated against indirect calorimetry, perform equally across all degrees of obesity, or compare well with other equations. Methods: Measured resting energy expenditure (MREE) was determined in obese (body mass index [BMI] ≥30 kg/m²), critically ill patients. Resting energy expenditure was predicted (PREE) using several equations: 12.5 kcal/kg ABW (ASPEN‐Actual BW), 23.5 kcal/kg IBW (ASPEN‐Ideal BW), Harris‐Benedict (adjusted‐weight and 1.5 stress‐factor), and Ireton‐Jones for obesity. Correlation of PREE to 65% MREE, predictive accuracy, precision, bias, and large error incidence were calculated. Results: All equations were significantly correlated with 65% MREE but had poor predictive accuracy, had excessive large error incidence, were imprecise, and were biased in the entire cohort (N = 31). In the obesity cohort (n = 20, BMI 30–50 kg/m²), ASPEN‐Actual BW had acceptable predictive accuracy and large error incidence, was unbiased, and was nearly precise. In super obesity (n = 11, BMI >50 kg/m²), ASPEN‐Ideal BW had acceptable predictive accuracy and large error incidence and was precise and unbiased. Conclusions: SCCM/ASPEN‐recommended body weight equations are reasonable predictors of 65% MREE depending on the equation and degree of obesity. Assuming that feeding 65% MREE is appropriate, this study suggests that patients with a BMI 30–50 kg/m² should receive 11–14 kcal/kg/d using ABW and those with a BMI >50 kg/m² should receive 22–25 kcal/kg/d using IBW.