Indirect Calorimetry Values Research Articles

Estimation of Heat Production Rate using Thermal Data During Exercise in Indoor Environments: A Study of Heat Storage Rate in Male Athletes.

The increasing preference for indoor exercise spaces highlights the relationship between indoor thermal environments and physiological responses, particularly concerning thermal comfort during physical activity. Determining the metabolic heat production rate during exercise is essential for optimizing the thermal comfort, well-being, and performance of individuals engaged in physical activities. This value can be determined during the activity using several methods, including direct calorimetry measurement, indirect calorimetry that uses analysis of respiratory gases, or approximations using collected data such as speed, body mass, and heart rate. The study aimed to calculate the metabolic heat production rate by infrared thermal evaluation (ITE) based on the body's thermal balance approach and compare it with the values determined by indirect calorimetry (IC). Fourteen participants volunteered for the study, using a cycling ergometer in a controlled climatic chamber. After the familiarization sessions, maximal O2 intake levels (VO2max) were determined through maximal graded exercise tests. Subsequently, constant work rate exercise tests were performed at 60% of VO2max for 20min. The metabolic heat production rates were calculated by IC and ITE for each athlete individually. Respiratory gases were used to determine IC, while body skin and core temperatures, along with physical environmental data, were applied to calculate ITE using the human body thermal balance approximation of ASHRAE. According to the results, heat storage rates were misleading among the body's heat transfer modes, particularly during the first 8min of the exercise. ITE showed a moderate level of correlation with IC (r: 0.03-0.86) with a higher level of dispersion relative to the mean (CV%: 12-84%). Therefore, a new equation (ITEnew) for the heat storage rates was proposed using the experimental data from this study. The results showed that ITEnew provided more precise estimations for the entire exercise period (p > 0.05). Correlations between ITEnew and IC values were consistently strong throughout the exercise period (r: 0.62-0.85). It can be suggested that ITEnew values can predict IC during the constant work rate steady-state exercise.

Energy expenditure in COVID-19 mechanically ventilated patients: A comparison of three methods of energy estimation.

BackgroundIndirect calorimetry (IC) is the gold standard for measuring resting energy expenditure. Energy expenditure (EE) estimated by ventilator‐derived carbon dioxide consumption (EEVCO2) has also been proposed. In the absence of IC, predictive weight‐based equations have been recommended to estimate daily energy requirements. This study aims to compare simple predictive weight‐based equations with those estimated by EEVCO2 and IC in mechanically ventilated patients of COVID‐19.MethodsRetrospective study of a cohort of critically ill adult patients with COVID‐19 requiring mechanical ventilation and artificial nutrition to compare energy estimations by three methods through the calculation of bias and precision agreement, reliability, and accuracy rates.ResultsIn 58 mechanically ventilated patients, a total of 117 paired measurements were obtained. The mean estimated energy derived from weight‐based calculations was 2576 ± 469 kcal/24 h, as compared with 1507 ± 499 kcal/24 h when EE was estimated by IC, resulting in a significant bias of 1069 kcal/day (95% CI [−2158 to 18.7 kcal]; P < 0.001). Similarly, estimated mean EEVCO2 was 1388 ± 467 kcal/24 h when compared with estimation of EE from IC. A significant bias of only 118 kcal/day (95% CI [−187 to 422 kcal]; P < 0.001), compared by the Bland‐Altman plot, was noted.ConclusionThe energy estimated with EEVCO2 correlated better with IC values than energy derived from weight‐based calculations. Our data suggest that the use of simple predictive equations may potentially lead to overfeeding in mechanically ventilated patients with COVID‐19.

Validation of K-Sense against indirect calorimetry and accelerometry

PurposeConsidering the difficulty of accurately estimating energy expenditure (EE) in habitual physical activity (PA), efforts to improve estimation accuracy are well warranted. The aim of the study was, first, to validate the K-Sense EE estimation system to improve EE estimation accuracy of human low-intensity activities, and, second, to compare K-Sense EE estimation values against ActiGraph (GT3X+) accelerometer-derived EE estimates.MethodsA comparative analysis investigated the K-Sense EE estimation values against indirect calorimetry and ActiGraph (GT3X+) EE estimates. A sample of 18 participants (age: 24.0 ± 5.2 years) performed eight sedentary/low intensity lifestyle activities, each wearing K-Sense with sensors attached to right wrist and ankle.ResultsThe K-Sense estimation accuracy ranged from 89.4% to 99.9%, outperforming ActiGraph equations, which were found to overestimate EE of these low-intensity activities, achieving 70.3% estimation accuracy at best. T-tests showed no statistically significant differences between K-Sense and indirect calorimetry values. Bland-Altman plots, however, illustrated an EE estimation error ranging from –9 to 7 kcal (with 95% confidence limits of agreement) among individuals.ConclusionsEE evaluation with low-cost inertial measurement units, such as those found in K-Sense, is a valid method in comparison with indirect calorimetry and ActiGraph accelerometry.

A stepwise science-industry collaboration to optimize the calculation of energy expenditure during walking and running with a consumer-based activity device

Physical activity (PA) and sedentary behavior (SB) have important implications for health benefits. A growing number of people use consumer available wearables such as activity trackers, which claim to objectively monitor PA and SB in free-living conditions. These devices could provide essential information to understand the influence of behavior on health. This understanding assumes that available consumer products correctly monitor PA in the everyday life. A general approach in science is to validate such activity devices in a controlled environment. The classical procedure to investigate criterion validity is to examine new devices based on the gold standard. To our knowledge, the resulting validation data are not often analyzed and shared with manufacturers to further develop and improve the activity device. The current study can be seen as a validation study to check the criterion validity of a consumer-level activity device. The novelty of this study was the application of a stepwise approach to optimize the calculations of a consumer available activity device (i.e., Activ8; www.activ8all.com/product/activ8-professional-activity-monitor/) for estimating energy expenditure (EE) in walking and running.Forty adults (27 males and 13 females) participated in three substudies. Each substudy consisted of several walking and running activities in which EE was simultaneously measured with indirect calorimetry (as reference value) and the Activ8 activity device. EE values at each walking and running speed were compared to identify the accuracy of the Activ8 device. After completion of the first and second substudies, the results were shared and discussed with the manufacturer of Activ8. Next, the calculations for EE were adapted to the indirect calorimetry values to improve accuracy. In the second and third substudies, the modifications were tested, and results were used to further optimize the calculation of EE.The results of this study show an improved correlation between EE measured by indirect calorimetry and the Activ8 activity device (R2 from 0.91 to 0.95); a decrease in differences between substudy A and substudy B considering EE measured (indirect calorimetry) and calculated (Activ8 calculation) was observed. The second modification in the calculation showed a further increase in correlation (R2 from 0.95 to 0.97) between the measured and calculated EE; however, the absolute difference between the two values increased. The results from a validation study are valuable to use for further adaptation of accelerometer device calculations. A stepwise science-industry collaboration can improve the calculation accuracy and may be a practical approach for validation studies in which human movement scientists and technology manufacturers work together to successfully improve the validity and accuracy of consumer-based activity devices.

Analysis of the difference between indirect calorimetry and predicted energy estimation in patients with chronic obstructive pulmonary disease

To analyze the difference between indirect calorimetry (IC) and predicted energy estimation in patients with chronic obstructive pulmonary disease (COPD), and its possible factors affecting the difference, to provide reasonable energy supply basis for COPD patients. A prospective cohort study was conducted. Twenty-six patients with COPD undergoing mechanical ventilation (MV) admitted to intensive care unit (ICU) of Hangzhou City Fuyang District First People's Hospital in Zhejiang Province from January to December in 2016 were enrolled. The energy values of patients were calculated by IC and predicted energy estimation, respectively. According to the degree of IC values deviating from the predicted energy estimation, the patients were divided into energy approaching group (IC values deviating from the empirical energy estimation ≤15%) and energy deviation group (IC values deviating from the empirical energy estimation > 15%). Bland-Altman diagram was drawn, and the consistency of the energy target values assessing by two methods was analyzed. The factors influencing the energy value deviation of the two measuring methods were screened by the multivariate Logistic regression and linear regression analysis. Twenty-six patients were enrolled in the final analysis. The energy target value of IC was significantly higher than that of predicted energy estimation (kJ: 7 079.3±1 213.4 vs. 6 527.0±949.8), and the difference between two values was statistically significant (P < 0.01). Bland-Altman heterogeneity analysis showed that the overall consistency of the energy values between the predicted energy estimation and IC was quite good. There were 14 patients in energy approaching group, and 12 in energy deviation group. There was no significant difference in gender, age, body mass index (BMI), type of COPD, or acute physiology and chronic health evaluation II (APACHE II) score between the two groups. In energy deviation group, the IC value of patients was significantly higher than predicted energy estimation (kJ: 7 711.1±1 125.5 vs. 6 556.3±907.9, P < 0.01). However, in energy approaching group, there was no significant difference between the energy values of IC and predicted energy estimation (kJ: 6 539.6±1 037.6 vs. 6 501.9±1 016.7, P > 0.05). Multivariate Logistic regression analysis showed that APACHE II score was an independent risk factor inducing the deviation of energy evaluation between IC and predicted energy estimation [odds ratio (OR) = 1.403, 95% confidence interval (95%CI) = 1.019-1.932, P = 0.038]. Multivariate linear regression analysis showed that the APACHE II score increased by 1, and the energy deviation increased by 2.0 kJ (β = 0.476, 95%CI = 0.004-0.956, P = 0.047). For patients with COPD, there was a good correlation between predicted energy estimation and the resting energy expenditure measured by IC. APACHE II score was an independent risk factor inducing the deviation of energy evaluation between IC and predicted energy estimation. It is suggested that the target value of energy should be determined by IC for patients with high APACHE II score.

Use of both quantitative and qualitative methods to improve assessment of resting energy expenditure equation performance in hospitalized adults

To introduce the use of qualitative assessment in energy expenditure (EE) equation research to improve the understanding of performance of the equations in the clinical setting. Hospitalized individuals who had an indirect calorimetry (IC) measurement during their hospital stay from 2010 to 2012 were included in the study (n=59). An additional 1000 patients hospitalized during this time were used to limit the IC cohort to a more "clinically relevant" BMI range (n=46). The following estimation equations were assessed: Harris-Benedict, 25kcal/kg using actual body weight, Mifflin St. Jeor, Ireton-Jones, Penn State, and Owen. Bland-Altman plots with Loess curves were generated to compare estimated basal caloric needs between EE equations and IC values. This study found a large amount of variability with all EE equations. As the mean calorie level increased, the Harris Benedict, Mifflin St. Jeor, Penn State, and Owen equations all tended to increasingly under-predict caloric need. In a research setting a qualitative assessment of EE equations can provide a more comprehensive understanding of equation performance by complementing traditional quantitative methods. The addition of a Loess curve to the Bland-Altman plot further enhances qualitative assessment.

Comparison between resting metabolic rate and indirect calorimetry in postmenopausal women

ABSTRACT Objective To compare resting metabolic rate values determined by indirect calorimetry with values estimated using different predictive equations in lean and overweight postmenopausal women. Methods Twenty-four women, who had stopped menstruating for at least two years, were subjected to anthropometric measurements and indirect calorimetry after 12-hour overnight fasting to determine, mathematically and experimentally, resting metabolic rate values. Results There was no difference in the indirect calorimetry values between the groups evaluated. Difference values of resting metabolic rate were obtained with all equations used. For the lean women, there was no difference between the values obtained by indirect calorimetry and those estimated using the equations proposed by Food and Agricultural Organization, Fredix, Lazzer, and Schofield. However, in the overweight group, the resting metabolic rate values estimated using the Institute of Medicine, Berstein and Owen equations were different from those obtained by indirect calorimetry. Conclusion This study suggests that differences in body composition in postmenopausal women influence the accuracy of predictive equations, demonstrating the need for more accurate estimation methods for resting metabolic rate in postmenopausal women with different body compositions.

SUN-P212: Assessment of Resting Metabolic Rate: Comparison of Prediction Equations With Indirect Calorimetry Values; Un Update

SUN-P212: Assessment of Resting Metabolic Rate: Comparison of Prediction Equations With Indirect Calorimetry Values; Un Update

Our great forgotten, chronic respiratory sufferers

Lung’s own properties make that nutritional support, besides covering the requirements can modulate its infl ammatory response. Lung tissue has a low glucose stock. Fatty acids are the main energy producer of type II pneumocytes, which use them in order to form phospholipids, essential for surfactant whose creation and release decrease in acute lung injury (ALI). Glutamine is a good substratum for endocrine cells and type II pneumocytes. Due to high nutritional risk, it is important its assessments in disorders as COPD and acute respiratory distress syndrome (ADRS). Indirect calorimetry values the effect of ventilation and nutritional support, avoiding overfeeding. Hypophosphatemia and refeeding syndrome are frequent and need to be avoided because of their morbidity. In critically ill patients, malnutrition can lead to respiratory failure and increasing mechanical ventilation time. To avoid hypercapnia in weaning, glucose levels should be controlled. High lipids/carbohydrates ratio do not show usefulness in COPD neither mechanical ventilation removal. ALI patients beneficiate from an early start and the volume administered. Enteral nutrition with high fatty acids ratio (EPA, DHA and γ-linolenic acid) and antioxidants do not show any superiority. Omega-3 fatty acid in parenteral nutrition could modulate infl ammation and immunosuppression in a positive manner. The use of glutamine, vitamins or antioxidants in these patients could be justified.

Comparação entre equações preditivas do gasto calórico de gestantes em repouso e exercício

The regular changes that occur during pregnancy increase energy expenditure at rest and during exercise. Prediction models are practical tools that facilitate the estimation of energy expenditure. The objective of this study was to analyze the reliability of prediction models of energy expenditure for pregnant women in two situations, at rest and during light-intensity exercise, compared to indirect calorimetry. Energy expenditure was measured in 10 pregnant women during the second trimester of gestation (23.5 ± 3.7 weeks) at rest and during light-intensity treadmill walking (4 km/h) using indirect calorimetry and the prediction models proposed by Hronek (rest) and Pivarnik (exercise). The intraclass correlation coefficient (ICC) and Bland-Altman plots were used for comparison of the methods. A low correlation was observed at rest and during exercise [ICC=0.531 (-0.185; 0.814) and ICC=0.124 (-1.213; 0.653), respectively]. At rest, daily energy expenditure tended to be overestimated with increasing indirect calorimetry values. During exercise, the equation tended to underestimate the energy expenditure of pregnant women as it increased. In conclusion, the prediction models analyzed showed a low correlation with indirect calorimetry, overestimating daily energy expenditure at rest and underestimating energy expenditure during light-intensity exercise. Thus, practical and low-cost tools such as predictive equations cannot always be used safely in professional practice and care should be taken to apply them to groups with altered physiological conditions, such as pregnant women.

Prevalence of Underprescription or Overprescription of Energy Needs in Critically Ill Mechanically Ventilated Adults as Determined by Indirect Calorimetry: A Systematic Literature Review.

Underfeeding and overfeeding has been associated with adverse patient outcomes. Resting energy expenditure can be measured using indirect calorimetry. In its absence, predictive equations are used. A systematic literature review was conducted to determine the prevalence of underprescription and overprescription of energy needs in adult mechanically ventilated critically ill patients by comparing predictive equations to indirect calorimetry measurements. Ovid MEDLINE, CINAHL Plus, Scopus, and EMBASE databases were searched in May 2013 to identify studies that used both predictive equations and indirect calorimetry to determine energy expenditure. Reference lists of included publications were also searched. The number of predictive equations that underestimated or overestimated energy expenditure by ±10% when compared to indirect calorimetry measurements were noted at both an individual and group level. In total, 2349 publications were retrieved, with 18 studies included. Of the 160 variations of 13 predictive equations reviewed at a group level, 38% underestimated and 12% overestimated energy expenditure by more than 10%. The remaining 50% of equations estimated energy expenditure to within ±10 of indirect calorimetry measurements. On an individual patient level, predictive equations underestimated and overestimated energy expenditure in 13-90% and 0-88% of patients, respectively. Differences of up to 43% below and 66% above indirect calorimetry values were observed. Large discrepancies exist between predictive equation estimates and indirect calorimetry measurements in individuals and groups. Further research is needed to determine the influence of indirect calorimetry and predictive equation limitations in contributing to these observed differences.

Validating an energy expenditure prediction equation in overweight and obese Mexican patients.

The prevalence of overweight and obesity in Mexico is approximately 70%; thus, obtaining a reliable measurement of the resting energy expenditure (REE) in these patients is of extreme importance. The aim of the study was to obtain a prediction equation of REE in overweight or obese outpatients in the Mexican population. The study was conducted at The National Institute for Medical Sciences and Nutrition Salvador Zubiran (Mexico, D.F.). Consecutive outpatients (18-70 years old) at the Clinical Nutrition were evaluated between March 2010 and August 2012 after being diagnosed with overweight or obesity (body mass index [BMI] ≥ 25 kg/m2). Patients with any disease that could affect the measurement of gas exchange were excluded. Participants were evaluated by indirect calorimetry (IC), bioelectrical impedance analysis (BIA) and anthropometric measurements to design the REE prediction equation. Two groups were evaluated: one group for derivation and another group for validation. The REE was also estimated using the equations of Harris-Benedict, Mifflin St-Jeor, Ireton-Jones, Carrasco, Kleiber and Owen, assessing current weight, ideal weight and adjusted weight. A REE equation was obtained by multiple linear regression based on the evaluated variables, and those that gave the best precision to the model were selected. The real REE and the estimated REE were then compared using Student's t-test. To highlight differences, pairs of measurements were further analyzed using the Bland & Altman plot. Pearson correlation coefficients and coefficients of determination between REE values measured by IC and REE values estimated using various formulas were calculated. A total of 77 patients were included in the derivation group: 38 men (49.4%) and 39 women (50.6%). The mean age was 48.5 ± 13.9 years, and the mean BMI was 34.7 ± 5.7 kg/m2. A total of 50 participants were included in the validation group: 16 men (32%) and 34 women (68%). The mean age was 48.5 ± 15.5 years, and the mean BMI was 34.2 ± 5.2 kg/m2. The baseline characteristics of both groups were homogeneous. IC reported an average of 2001 ± 552 kcal, with a respiratory quotient (RQ) of 0.75 ± 0.04. The new REE equation that resulted from the statistical model had an R2 = 0.52 and a bias of ± 3.39 kcal. When the REE obtained from IC was compared with the REE estimated by the new formula, there was no significant difference between the results, and the correlation for all participants was 0.71 (p. <0.0001). When the equations were analyzed using the Bland-Altman method, the difference between the new formula and the REE measurement by IC was a bias of 3.39 ± 384 kcal.Furthermore, a correlation was obtained between the real and estimated REE values using different equations; the most accurate correlation with the new formula was Owen's formula (r=0.712). The new formula had an acceptable correlation with IC REE measurement in overweight and obese patients in the Mexican population. This equation may represent a useful tool for health care professionals who do not have access to IC equipment for the estimation of REE.

Accuracy of Gas Exchange Monitoring During Noninvasive Ventilation

Gas exchange monitoring by indirect calorimetry (IC) during noninvasive ventilation (NIV) is desirable but currently not available. Leaks around the mask preclude reliable measurements of carbon dioxide production (VCO2) and oxygen consumption (VO2) in this population. We aimed to examine the impact of system leaks and gas flows on the accuracy of gas exchange measurements during NIV using an in vitro metabolic simulation. We examined the agreement between VCO2 and VO2 measurements by IC (using a novel canopy device) and reference values generated during an in vitro metabolic simulation of NIV at room air. The flow rate of gas sampled by the IC device (VIC) was set relative to the output flow of the ventilator (VVENT) to obtain a range of sample factors (SF = VIC/VVENT). Linear regression was used to determine the effect of SF on the accuracy of the system. An acceptable agreement between measured and reference values was observed, with mean bias (limits of agreement) of -3.3% (-6.9% to 0.3%) and -10.6% (-14.9% to -6.4%) for VCO2 and VO2, respectively. An SF of 1.25 was associated with the highest accuracy of measurement. VO2 measurement accuracy deteriorated with system leak and at SF >1.25 and was linearly related to sample dilution by ambient air entrainment. A novel canopy device with titration of IC sample flow in relation to the ventilator flow allowed in vitro gas exchange measurements during simulated NIV with acceptable accuracy. This model needs to be tested in clinical settings.

The modified obstetric metabolic equivalent (MET): finding a MET that fits in pregnancy.

The Compendium of Physical Activities (CPA) provides the energy expenditure (EE) for hundreds of daily activities reported in metabolic equivalents (MET). It remains to be determined if the metabolic changes of pregnancy alter the use of the CPA MET (METCPA) in this population. The energy cost of rest, activities of daily living (ADL; typing, folding laundry and sweeping) and treadmill walking [2.0, 2.5, 3.0 mph (0% incline), 3.0 mph (3% incline)] were compared with the METCPA from the 2000 and 2011 CPA in 30 pregnant women (10-14 weeks gestation) using indirect calorimetry (IC). The METCPA for each activity was compared against two measured IC values: METabsolute (3.5 ml O2/kg/min) and METratio (EEactivity/EErest). Means for both comparisons were tested by one-sample t-test. Measured MET correlated with the 2011 METCPA: METabsolute v. METCPA R 2 = 0.906, P < 0.0001; METratio v. METCPA R 2 = 0.861, P < 0.0001. Differences between measured MET values and the 2011 METCPA ranged from 16% underestimation to 48% overestimation. Using the absolute definition, the METCPA significantly overestimated the ADL (P < 0.0005); yet, no significant differences were found between walking at 0% grade and METCPA. Conversely, only folding laundry was significantly different with the ratio definition, whereas walking at a level grade was significantly underestimated (P < 0.0001). Similar observations were found using the 2000 CPA. The use of the METCPA to estimate EE in pregnant women can result in significant over- or underestimation, depending on the activity and the definition of the MET that is used.

Comparison Of Two Portable Devices For Assessing Energy Expenditure During High-intensity Running

One of the main challenges of exercise nutrition is to ensure that athletes keep their energy intake and energy expenditure (EE) within healthy limits. Therefore, correct methods to quantify EE are necessary. Exercise-induced EE can be measured reliably with indirect calorimetry (IC) under laboratory conditions but the quantification of EE in the field is more challenging. Portable devices such as the Sensewear Pro3 armband (SENS; BodySense, USA) and the Actiheart monitor (ACTI; CamNtech, UK) have been designed for simple EE assessment on a daily basis. These devices have been validated in the general population but their performance has not been studied adequately during high-intensity exercise. PURPOSE: To compare energy expenditure measured with SENS and ACTI with an accepted reference method (IC) during high-intensity running. METHODS: Twenty-nine male participants (27.2 ± 1.9 y, 184 ± 7 cm, 79.8 ± 7.6 kg) performed a running test, which started at 2.8 m/s. Every 5 minutes, velocity was increased by 0.4 m/s until 4.8 m/s or voluntary exhaustion. During the test, the participants simultaneously wore SENS and ACTI. IC was performed with a ZAN 600 spirograph (Zan, Germany). RESULTS: EE measured with SENS was moderately correlated with IC values (r = 0.68). For running speeds greater or equal 3.2 m/s EE obtained with SENS was significantly smaller than reference data. The difference increased from 1.0 ± 1.0 METs at 3.2 m/s to 4.8 ± 1.7 METs at 4.8 m/s (p < 0.001). Underestimation was minimal before the test (0.2 ± 0.9 METs) but significant post-exercise (3.7 ± 2.9 METs, p < 0.001). For ACTI, there was also a moderate correlation with IC values (r = 0.77). ACTI underestimated EE between 0.9 ± 1.2 METs (2.8 m/s) and 4.3 ± 1.9 METs (4.8 m/s) and also during rest periods (before: 0.5 ± 0.4 METs, after: 4.1 ± 3.0 METs). The level of underestimation grew significantly with increasing EE (p < 0.001). CONCLUSIONS: Both devices performed similarly during the high-intensity running trial. SENS and ACTI underestimated ExEE and the level of underestimation increased significantly with increasing running velocities. However, the correlation with the IC data indicates that the application of exercise-specific should improve the validity of both devices.

Intensivmedizinische Patienten

Using indirect calorimetry (IC), required energy demand may be determined. The SenseWear (SW) armband uses skin temperature, galvanic skin response, heat flux, and a 2-axis accelerometer to estimate daily energy expenditure (EE). The aim of the present study was to evaluate accuracy of the SenseWear measurements in critically ill and ventilated patients. After approval of the local ethics committee and written informed consent, critically ill and ventilated patients were enrolled. During a 24-h study period EE was continuously measured using the SW armband (standardized position at right upper arm); an IC was performed additionally. Measurement bias (DeltaEE) was calculated as DeltaEE=EE(SW)-EE(IC). Daily energy expenditure of both techniques (IC vs. SW) was compared using regression analysis and the Bland Altman method. The t-Test for paired samples was used for statistical analysis, p<0.05 was considered statistically significant. In total, 23 critically ill and ventilated patients (17 male, 6 female; means: 59.9+/-17.3 years; body mass index 28.0+/-6.3 kg/m(-2)) were investigated. A mean bias of DeltaEE=-565.65+/-1,748.07 kJ (-135.0+/-417.2 kcal) [range: -4,709.56+/-2,224.89 kJ (-1,124 to +531 kcal); p=0.3547] was calculated. Bland-Altman analysis revealed that SW slightly overestimates IC energy expenditure for critically ill patients in the hypo- and normocaloric range [<7,123 kJ/24 h: DeltaEE=+644.42+/-1,038.70 kJ (<1,700 kcal/24 h: DeltaEE=+153.8+/-247.9 kcal); p=0.0838], but significantly underestimated IC values in the hypercaloric range [>10,056 kJ/24 h: DeltaEE=-2,679.09+/-1,698.63 kJ (>2,400 kcal/24 h: DeltaEE=-639.4+/-405.4 kcal); p=0.0098]. The SenseWear armband is non-invasive, convenient and easy to handle, but has a significant measurement bias in the hypercaloric range. Although IC is still best suited to determining metabolic need in intubated patients, measurements with the SenseWear armband provide significant advantages, e.g. in non-intubated patients, and give a fair estimation of daily energy expenditure when used alone.

Resting energy expenditure in non-ventilated, non-sedated patients recovering from serious traumatic brain injury: Comparison of prediction equations with indirect calorimetry values

Little is known about energy requirements in brain injured (TBI) patients, despite evidence suggesting adequate nutritional support can improve clinical outcomes. The study aim was to compare predicted energy requirements with measured resting energy expenditure (REE) values, in patients recovering from TBI. Indirect calorimetry (IC) was used to measure REE in 45 patients with TBI. Predicted energy requirements were determined using FAO/WHO/UNU and Harris-Benedict (HB) equations. Bland-Altman and regression analysis were used for analysis. One-hundred and sixty-seven successful measurements were recorded in patients with TBI. At an individual level, both equations predicted REE poorly. The mean of the differences of standardised areas of measured REE and FAO/WHO/UNU was near zero (-9 kcal) but the variation in both directions was substantial (range -591 to +573 kcal). Similarly, the differences of areas of measured REE and HB demonstrated a mean of 1.9 kcal and range -568 to +571 kcal. Glasgow coma score, patient status, weight and body temperature were significant predictors of measured REE (p<0.001; R(2)=0.47). Clinical equations are poor predictors of measured REE in patients with TBI. The variability in REE is substantial. Clinicians should be aware of the limitations of prediction equations when estimating energy requirements in TBI patients.

A Comparison Between Ventilation Modes: How Does Activity Level Affect Energy Expenditure Estimates?

An appropriate diet is essential to avoid complications of overfeeding or underfeeding in mechanically ventilated intensive care unit (ICU) patients. The paucity of consistent comparative data on energy expenditure for each ventilation mode complicates diet prescription. This study evaluates caloric requirements by comparing estimated and measured energy expenditure values for 2 ventilation modes. The energy expenditure of 100 ICU patients on assisted or controlled mechanical ventilation was measured by indirect calorimetry for 20 minutes. Values were calculated for a 24-hour period and compared with Harris-Benedict estimates multiplied by an injury factor and either multiplied or not by a 10% activity factor. The mean Harris-Benedict estimate was 1858.87 +/- 488.67 kcal/24 h when multiplied by an injury factor and a 10% activity factor. The mean energy expenditure values measured by indirect calorimetry were 1712.76 +/- 491.95 kcal/24 h for controlled and 1867.33 +/- 542.67 kcal/24 h for assisted ventilation. The mean total energy expenditure for assisted ventilation was 10.71% greater than the mean for controlled ventilation (P < .001). For controlled ventilation, Harris-Benedict results overestimated indirect calorimetry values by 141.10 +/- 10 kcal/24 h (8.2%, P = .012) when multiplied by injury and activity factors, and underestimated values by 44.28 +/- 28 kcal/24 h (2.6%, P = .399) when the equation was calculated without the activity factor. For assisted ventilation, Harris-Benedict results underestimated indirect calorimetry values by 198.84 +/- 84 kcal/24 h (10.7%, P = .001) when not multiplied by the activity factor and by 13.46 kcal/24 h (0.75%) when the activity factor was used, but differences were not statistically significant (P = .829). Results suggest that a 10% activity factor should be adopted only for assisted ventilation because multiplication by an activity factor may lead to overfeeding of patients on controlled ventilation.

Comparison of formulaic equations to determine energy expenditure in the critically ill patient

Objective: Inappropriate energy intake can negatively affect patient outcome during critical illness. Measuring energy expenditure via indirect calorimetry (IC) is the most accurate method of determining needs. Often predictive equations are used because IC is not available at all institutions or for all populations. Methods: This study compared 24-h IC measures with five previously published formulaic equations and nomograms using kilocalorie per kilogram of body weight to determine their accuracy in predicting energy needs in critically ill adults receiving nutrition support. Two different weight categories were analyzed: body mass indexes below 25 kg/m 2 and below 30 kg/m 2. Results: The Harris-Benedict equation using adjusted body weight multiplied by a stress factor of 1.6 and the Swinamer equation predicted measured energy expenditure (MEE) within 20% of IC values 80% of the time for the entire population studied. For those individuals at the lower weight range, the Harris-Benedict equation using actual weight reference weight via the Hamwi equation and via adjusted weight times a stress factor of 1.6 and the Swinamer equation predicted MEE within 20% of IC values 89% of the time. The Frankenfield equation overestimated MEE; whereas the Penn State and Ireton-Jones equations underestimated energy needs in the population studied. Conclusions: Predictive equations such as the Harris-Benedict equation multiplied by a stress factor of 1.6 and the Swinamer equation may be accurate enough for short-term nutrition support of critically ill patients when IC is unavailable.

Kinetics of intramuscular triglyceride fatty acids in exercising humans.

A pulse ([(14)C]palmitate)-chase ([(3)H]palmitate) approach was used to study intramuscular triglyceride (imTG) fatty acid and plasma free fatty acid (FFA) kinetics during exercise at approximately 45% peak O(2) consumption in 12 adults. Vastus lateralis muscle was biopsied before and after 90 min of bicycle exercise; (3)H(2)O production, breath (14)CO(2) excretion and lipid oxidation (indirect calorimetry) rates were measured during exercise. during exercise, 8.2+/-1.2 and 8.4+/-0.7 micromol x kg(-1) x min(-1) of imTG fatty acids and plasma FFA, respectively, were oxidized according to isotopic measurements. The sum of these two values was not different (P = 0.6) from lipid oxidation by indirect calorimetry (15.4 +/-1.6 micromol x kg(-1) x min(-1)); the isotopic and indirect calorimetry values were correlated (r = 0.79, P<0.005). During exercise, imTG turnover rate was 0.32+/-0.07%/min (6.0+/-2.0 micromol of imTG x kg wet muscle(-1) x min(-1)) and plasma FFA were incorporated into imTG at a rate of 0.7+/-0.1 micromol x kg wet muscle(-1) x min(-1). The imTG pool size did not change during exercise. This pulse-chase, dual tracer appears to be a reasonable approach to measure oxidation and synthesis kinetics of imTG.