Clinical accuracy of the MedGemâ¢ indirect calorimeter for measuring ...

European Journal of Clinical Nutrition (2005) 59, 603–610

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ORIGINAL COMMUNICATION Clinical accuracy of the MedGemt indirect calorimeter for measuring resting energy expenditure in cancer patients MM Reeves1,2*, S Capra3, J Bauer2, PSW Davies4 and D Battistutta1 1 Centre for Health Research, Queensland University of Technology, Brisbane, Queensland, Australia; 2The Wesley Research Institute, Brisbane, Queensland, Australia; 3Australian Centre for Evidence Based Nutrition and Dietetics, University of Newcastle, Newcastle, New South Wales, Australia; and 4Children’s Nutrition Research Centre, Royal Children’s Hospital, University of Queensland, Brisbane, Queensland, Australia

Objective: To compare, in patients with cancer and in healthy subjects, measured resting energy expenditure (REE) from traditional indirect calorimetry to a new portable device (MedGemt) and predicted REE. Design: Cross-sectional clinical validation study. Setting: Private radiation oncology centre, Brisbane, Australia. Subjects: Cancer patients (n ¼ 18) and healthy subjects (n ¼ 17) aged 37–86 y, with body mass indices ranging from 18 to 42 kg/m2. Interventions: Oxygen consumption (VO2) and REE were measured by VMax229 (VM) and MedGem (MG) indirect calorimeters in random order after a 12-h fast and 30-min rest. REE was also calculated from the MG without adjustment for nitrogen excretion (MGN) and estimated from Harris–Benedict prediction equations. Data were analysed using the Bland and Altman approach, based on a clinically acceptable difference between methods of 5%. Results: The mean bias (MGN–VM) was 10% and limits of agreement were –42 to 21% for cancer patients; mean bias 5% with limits of 45 to 35% for healthy subjects. Less than half of the cancer patients (n ¼ 7, 46.7%) and only a third (n ¼ 5, 33.3%) of healthy subjects had measured REE by MGN within clinically acceptable limits of VM. Predicted REE showed a mean bias (HB–VM) of 5% for cancer patients and 4% for healthy subjects, with limits of agreement of 30 to 20% and 27 to 34%, respectively. Conclusions: Limits of agreement for the MG and Harris Benedict equations compared to traditional indirect calorimetry were similar but wide, indicating poor clinical accuracy for determining the REE of individual cancer patients and healthy subjects.

European Journal of Clinical Nutrition (2005) 59, 603–610. doi:10.1038/sj.ejcn.1602114 Published online 2 March 2005 Keywords: REE; oxygen consumption; metabolic rate; cancer; nutrition support

Introduction *Correspondence: M Reeves, Viertel Centre for Research in Cancer Control, Queensland Cancer Fund, PO Box 201, Spring Hill QLD 4004, Australia. E-mail: [email protected] Guarantor: M Reeves. Contributors: MMR was responsible for writing the manuscript, initiated and designed the study, carried out data collection, data analysis, interpretation and discussion of results. SC initiated the study and assisted in the design of the study. JB contributed to the study design and assisted with data collection. PSWD and DB contributed to the study design, data analysis, interpretation and discussion of results and manuscript preparation. Received 6 July 2004; revised 18 November 2004; accepted 5 December 2004; published online 2 March 2005

The most accurate method for determining energy requirements is via measurement of energy expenditure, most commonly using indirect calorimetry (Jequier and Schutz, 1983; Soares et al, 1989; Flancbaum et al, 1999). Resting energy expenditure (REE) over shorter periods of time is often measured instead of total daily energy expenditure (TEE) over 24 h. Energy requirements can then be calculated from REE when an estimate of physical activity is known. In hospitalised patients, physical activity levels are usually reduced (Toth, 1999) and as such REE contributes a large proportion of TEE. Measurements of energy expenditure using traditional indirect calorimetry methods such as respiration chambers or metabolic carts are expensive, time consuming, require

Accuracy of MedGemt device in cancer patients MM Reeves et al

604 trained technicians to perform them and are therefore impractical in the clinical setting. Prediction equations are often used as alternatives to measurements of energy expenditure, however, the accuracy of these equations has often been criticised (Daly et al, 1985; Reeves & Capra, 2003). A new portable indirect calorimeter (MedGemt, HealtheTech, USA) has been promoted for its ease of use and relatively short measurement time. Only one published study has compared measurements of REE using the BodyGemt (identical to the MedGem) with traditional indirect calorimetry in healthy subjects (Nieman et al, 2003). The study concluded that the portable indirect calorimeter accurately and reliably measured oxygen consumption (VO2) and calculated REE in healthy subjects (Nieman et al, 2003). However, to be of use in a clinical setting the MedGem (MG) needs to be validated in other population groups, including people with disease or injury. One such disease state where there may be disturbances in energy metabolism is cancer. The aim of this study was to compare the individual agreement between measurements of REE using the MG device and traditional indirect calorimetry in patients with cancer and in healthy subjects. An additional aim was to compare the individual agreement between measured REE using the traditional indirect calorimeter and REE estimated by commonly used prediction equations.

Subjects and methods Subjects Patients with cancer and healthy subjects participated in the study. Cancer patients were recruited from consecutive new patients attending a private radiation oncology centre over a 6-month period. Cancer patients had histologically proven solid tumours and were aged 18 y or over. The limited literature regarding energy expenditure in patients with solid tumours of the breast, prostate or brain suggests little effect of the tumour on REE (Leenders, 1994; Demark-Wahnefried et al, 2001; Platz, 2002) and as such these patients were excluded from the study. Subjects were also excluded if they had undergone surgery within the month prior to the study, had severe endocrine abnormalities (eg hypothyroidism, hyperthyroidism), or were treated with high-dose steroid medications. These criteria were based on excluding conditions that have an independent effect on energy expenditure. The group of healthy subjects was based on a purposive volunteer sample of individuals from our affiliated institutions. All healthy subjects were in self-reported good health, did not have a history of cancer or severe endocrine abnormalities, had not undergone surgery within 1 month of the study, and were not treated with high-dose steroid medication.

Measurement protocol The data collection protocol was identical for cancer patients and healthy subjects. Measurements of REE were conducted under outpatient conditions between 7 and 9 a.m. after an European Journal of Clinical Nutrition

overnight fast (at least 12 h). Prior to commencement of the measurements, subjects rested quietly for 30 min, during which time the traditional indirect calorimeter was calibrated. Subjects were asked to remain awake and motionless for the duration of the measurement. For each subject, measurements with the two indirect calorimeters were conducted in random order. Height was measured without shoes to the nearest 0.5 cm using a stadiometer (KaWe, Germany). Weight was measured to the nearest 0.1 kg with shoes and heavy clothing removed (Model 300GS, Tanita Inc., Japan).

Traditional indirect calorimetry measurement REE was measured by breath-by-breath respiratory gas exchange with the VMax 229 (VM, SensorMedics, Yorba Linda, CA, USA) indirect calorimetry device, using a mouthpiece and noseclip. A mass flow sensor measured volume and airflow, which was calibrated prior to each measurement using a certified three-litre calibration syringe. Calibration was achieved when measured stroke volume was within 73% of syringe volume. Expired gas was analysed for oxygen concentration using a paramagnetic oxygen analyser and carbon dioxide concentration using a nondispersive infrared analyser. Gas analysers were calibrated prior to each measurement using three known standard gas concentrations (1670.02% O2, 470.02% CO2; 2670.02% O2, 0% CO2; room air 20.94% O2, 0.05% CO2). Calibration was complete when gas analysers measured oxygen and carbon dioxide concentration within 75% of the expected. REE measurements with the VM were ceased once a steadystate period was achieved or after 30 min, whichever occurred first. Steady state was defined as a 3-min period during which oxygen consumption (VO2), respiratory quotient (RQ) and minute ventilation (VE) changed by r10%. We have previously found that reducing the time period for steady state as opposed to the commonly used 5-min period, increases the number of subjects who achieve steady state while producing clinically acceptable measurements of REE (Reeves et al, 2004). Variation in the REE measurement slightly increased with the 3-min steady-state period; however, in this study sensitivity analyses indicated that similar results were achieved whether data were analysed using a 5-min or 3-min steady-state period. The 3-min period was therefore used to maximise the sample size. If steady state was not achieved within the 30-min period, the data were discarded. VO2 and carbon dioxide consumption (VCO2) were converted to REE using the abbreviated Weir equation (1949): REE ¼ VO2 ð3:941Þ þ VCO2 ð1:106Þ where REE is measured in kcal/day and VO2 and VCO2 in l/day.

MG indirect calorimetry measurement Measurements with the MG indirect calorimeter used singleuse disposable mouthpieces and a noseclip for collection of


605 expired air. Prior to each measurement, the MG selfcalibrates a 5-s interval during which time the flow sensors measure relative humidity, temperature and barometric pressure. The gas analyser is not calibrated prior to each measurement. The MG contains only an oxygen analyser for measurement of VO2. The oxygen analyser uses a dualchannel fluorescent quenching sensor, which is based on the deactivation of ruthenium cells in the presence of oxygen (Nieman et al, 2003). VCO2 is not measured, instead the MG assumes a constant RQ of 0.85. Termination of the MG measurement and calculation of VO2 and REE is self-determined by the device, using a proprietary algorithm for the determination of steady state. The first 2 min of data are excluded from the analysis. If steady state is not achieved an average of data during the last 8 min (maximum 10-min test) of the test is used. The MG displays VO2 (ml/min) and REE (kcal/day) on an LCD screen. REE is calculated from VO2 using a modified version of the Weir equation, where VCO2 is equivalent to VO2 0.85 and REE is adjusted for excreted urinary nitrogen assuming a dietary intake of 16% of energy from protein (HealtheTech, 2002): REEðkcal=dayÞ ¼ 6:931VO2 where VO2 is measured in ml/min. As the traditional indirect calorimetry does not adjust for loss of energy due to urinary nitrogen excretion, this difference between the two indirect calorimeters will introduce a small degree of error in the order of approximately 1–2% (Weir, 1949). As such, REE was also estimated from measured VO2 from the MG, without the adjustment for nitrogen excretion: REEðkcal=dayÞ ¼ 7:029VO2 where VO2 is measured in mL/min. Prediction of REE For each subject, REE (in kcal/day) was predicted using the Harris–Benedict equations (1919): REEmales ¼ 66:5 þ 5:0H þ 13:7W 6:8A REEfemales ¼ 655:1 þ 1:8H þ 9:6W 4:7A where H is the height in cm, W is the body weight in kg and A is the age in years. For subjects with a body mass index (BMI) greater than 29 kg/m2, an adjusted body weight was used in the prediction of REE to account for the increased proportion of body weight as low metabolically active adipose tissue and skeletal muscle in obese people (Heymsfield, 2002). The adjusted weight was based on the recommendations from Glynn et al (1999) and Barak et al (2002):

informed consent was obtained from each subject prior to commencement of the study.

Statistical analysis Statistical analysis was carried out using SPSS for Windows (Version 11.0.1, 2001, SPSS Inc., Chicago, USA) statistical software package. Continuous variables were normally distributed and are presented as mean (s.d.). A clinically acceptable difference of 5% between the two indirect calorimeters was defined a priori. This limit was set to allow for intraindividual variation, which is in the order of 3–5% (Garby & Lammert, 1984; Soares & Shetty, 1986; Henry et al, 1989). Other authors have used a 5% cutoff point for comparing different measurement methods (Segal, 1987). Power calculations indicated a minimum sample size of 20 subjects per group was required to detect this clinically meaningful difference, assuming a standard deviation of the difference of 8% (Nieman et al, 2003), with 80% power at the 95% significance level (two-tailed). Interpretations of significance of results were primarily based on assessing differences for clinical meaningfulness. For completeness, statistical significance is presented, but is not the sole basis on which interpretations of results are discussed. Mean biases in REE and VO2 measurements between MG and VM were first assessed for any effect of order of administration of measurement by multiple regression analysis. There was no order or interaction (order health status) effect for mean bias of REE or VO2 between MG and VM (data not shown). Consequently, analyses proceeded on pooled data ignoring order of administration. Differences in the mean biases (MG–VM) for measured REE and VO2 between cancer patients and healthy subjects were assessed by independent sample t-tests. Although there was no statistically significant difference for REE or VO2, the magnitude of the mean biases were of clinically significant concern, and as such data were analysed and presented separately for cancer patients and healthy subjects. Mean biases between the two indirect calorimeters for measured REE and VO2 and between measured and predicted REE were analysed for statistical significance by paired t-tests. Mean bias, limits of agreement (72 s.d.) and plot of bias against average of two measurements using the Bland– Altman approach (1986) were used to describe agreement at the individual level and assess whether the bias was consistent across the entire range of measurements. Pearson’s correlation coefficients were used to assess whether there were trends in the magnitude of the bias with increasing REE and VO2 measurements.

Adjusted weight ðkgÞ ¼ 50% ðIBW þ actual weightÞ where IBW is ideal body weight in kilogram calculated from the Hamwi equation (1964). Ethics The ethics committees of the relevant medical and tertiary institutions approved the conduct of this study. Voluntary

Results A total of 83 eligible cancer patients attended the radiation oncology centre over the 6-month period, of whom 19 (12 male, seven female) consented to participate in the study. Cancer patients who consented to participate did not differ European Journal of Clinical Nutrition


606 significantly to the total eligible pool with respect to age, gender or tumour site (data not shown). Following consent, one cancer patient became ill and was unable to undertake further data collection. As such results are based on data from 18 cancer patients. The group of cancer patients consisted of eight patients with tumour of the lung, seven with gastrointestinal tumours and three other tumours. All cancer patients except one were undergoing radiotherapy treatment and half were undergoing concurrent chemotherapy treatment at the time of the study. In all, 17 healthy subjects (10 male, seven female) also participated in the study. Physical characteristics of the participants are presented in Table 1. Cancer patients and healthy subjects were not significantly different, although there appeared to be a larger variation in the body weight of cancer patients. Two cancer patients and two healthy subjects did not obtain a valid measurement from the MG due to air leaks. Steady state with the VM was not achieved in one cancer patient. As such, complete data for comparison between MG and VM were available for 15 cancer patients and 15 healthy subjects. Comparison between VM and predicted REE is based on data from 16 cancer patients and 17 healthy subjects. Mean measured REE from the VM, MG and MG without adjustment for nitrogen excretion (MGN) and mean biases for cancer patients and healthy subjects are shown in Table 2. For cancer patients, mean measured REE from the VM differed significantly, both statistically and clinically (at the prescribed level of 5%), to mean measured REE from the MG and MGN. For the group of cancer patients, the mean bias was 11 and 10% for MG and MGN, respectively. Mean measured REE from the VM in healthy subjects was not statistically different from MG or MGN. For the group of healthy subjects, the mean bias between measured REE from VM and MG was clinically significant (8%); however, when REE was calculated from the MG without adjustment for nitrogen excretion, the mean bias was within clinically acceptable limits (5%). For individuals, the limits of agreement (72 standard deviations of the mean bias) for cancer patients and healthy subjects were wide, indicating that for some individuals there were clinically significant differences in REE measured

Table 1 Physical characteristics of participants

Age (y) Height (cm) Weight (kg) BMI (kg/m2)

Cancer patients (n ¼ 18)

Healthy subjects (n ¼ 17)

65713 (37–84) 167.0714.0 (144.5–205) 80.2719.8 (48.2–130.4) 28.475.0 (21.0–41.9)

60711 (39–76) 169.079.0 (155–181) 75.0713.0 (48.3–99.4) 26.374.1 (17.7–33.9)

Data are means7s.d. (range).

European Journal of Clinical Nutrition

Table 2 Comparison of measured resting energy expenditure (REE) and oxygen consumption (VO2) between the VMax229 (VM) and MedGem (MG) indirect calorimeters and predicted REE in cancer patients and healthy subjects

REE (kcal/day) VM MG Bias (MG–VM) MGN Bias (MGN–VM) HB Bias (HB–VM) VO2 (mL/min) VM MG Bias (MG–VM)

Cancer patients (n ¼ 15)

Healthy subjects (n ¼ 15)

1526 1351 175 1370 157 1513 72

1371 1258 113 1301 70 1480 51

(248) (282)* (227) (285)* (228) (327)** (191)

231 (40) 195 (41)* 36 (35)

(264) (239) (254) (287) (276) (227)*** (221)

206 (40) 185 (41) 21 (40)

Data are means (s.d.). MGN, MG without adjustment for nitrogen excretion; HB, Harris–Benedict prediction equations. *Significantly different from VM, Po0.05 **n ¼ 16, VM ¼ 15857337 kcal/day ***n ¼ 17, VM ¼ 14297299 kcal/day P-value from paired t-tests.

by the MG, with and without the adjustment for nitrogen excretion, when compared to the VM. The MGN measured REE as much as 42% below, up to 21% above, REE measured by the VM in cancer patients, and as much as 45% below, up to 35% above, REE measured by the VM in healthy subjects. REE measured by the MGN was within clinically acceptable limits (75%) of the VM in less than half of the group of cancer patients (n ¼ 7, 46.7%) and in only a third of healthy subjects (n ¼ 5, 33.3%). Bland–Altman plots (1986) were used to show the mean bias, limits of agreement and spread of the bias over the range of values, between REE measured by the VM and MGN for cancer patients and healthy subjects (Figures 1 and 2). There was no linear correlation between the bias and the average of the REE measurements for either cancer patients or healthy subjects. Figure 1 showed a tendency for a fanning effect with a narrower spread of biases at higher REE measurements. While Figure 2 did not show this same effect, biases for the majority of healthy subjects tended to fall below the mean bias. That is, the MG was more likely to measure REE less than the VM. The mean bias for one healthy subject was an outlier (Figure 2). Excluding this subject from the analysis reduced the limits of agreement slightly, however they were still outside clinically acceptable limits (37% up to 20%). Mean measured VO2 from the VM and MG and mean bias for cancer patients and healthy subjects are shown in Table 2. Mean VO2 measured by the MG was clinically different to VO2 measured by the VM in cancer patients (mean bias of 16%) and healthy subjects (mean bias of 10%); however, VO2 was only statistically significantly different in cancer patients. Mean RQ measured by VM was


607

Figure 1 Bland–Altman plot depicting differences in REE for cancer patients between the VMax229 (VM) and MedGem (MG) without adjustment for nitrogen excretion (MGN) vs mean REE values (n ¼ 15). Solid line represents the mean bias between the two methods and dotted lines represent 72 standard deviations from the mean (limits of agreement).

Figure 2 Bland–Altman plot depicting differences in REE for healthy subjects between the VMax229 (VM) and MedGem (MG) without adjustment for nitrogen excretion (MGN) vs mean REE values (n ¼ 15). Solid line represents the mean bias between the two methods and dotted lines represent 72 standard deviations from the mean (limits of agreement).

0.7070.09 and 0.7270.08 in cancer patients and healthy subjects, respectively. Mean predicted REE from Harris–Benedict equations and mean bias (between predicted REE and REE measured by VM)

for cancer patients and healthy subjects are shown in Table 2. There was no significant difference between mean measured REE and mean predicted REE in either cancer patients (mean bias of 5%) or healthy subjects (mean bias of 4%). The European Journal of Clinical Nutrition


608 limits of agreement, however, were also wide with the Harris–Benedict equations predicting REE as much as 30% below, up to 20% above, REE measured by the VM for cancer patients and as much as 27% below, up to 34% above, REE measured by the VM for healthy subjects. There was no significant correlation or pattern for either cancer patients or healthy subjects between the bias and the average of measured and predicted REE.

Discussion An understanding of patients’ energy requirements is necessary for the provision of adequate and appropriate nutrition support to ensure that patients attain and maintain a desirable body weight, improve nutritional status and avoid negative outcomes associated with over- or underfeeding. Practical validated measurement methods are necessary for patients where an accurate assessment of energy expenditure is warranted (Reeves & Capra, 2003). The portable MG indirect calorimeter is an ideal practical instrument for assessing energy expenditure, however to be of use clinically, it must be validated in patients with disease or injury. The aim of this study was to compare, in patients with cancer and in healthy subjects, REE measured with a traditional indirect calorimeter to measured REE using the new MG device and to REE estimated by prediction equations to determine the most appropriate alternative to traditional impractical methods. The proprietary calculation of REE from the MG included an adjustment for nitrogen excretion. The standard measurement that we used to compare with the MG did not include a 24-h urine collection to adjust energy expenditure for the incomplete oxidation of protein. Recent studies of short-term measurements of energy expenditure in patients do not commonly adjust for nitrogen excretion (Barak et al, 2002; Siervo et al, 2003), which produces a negligible error of 1–2% (Weir, 1949; Bursztein et al, 1989). So that measurements were directly comparable, REE was calculated from VO2 measured by the MG without adjustment for nitrogen excretion. Although the mean bias between REE measured by the VM and MGN was not significantly different between cancer patients and healthy subjects, REE measured by MGN was only within clinically acceptable limits for the group of healthy subjects. The mean bias of 5% observed in this study for healthy subjects, however, was considerably larger than that reported in the study by Nieman et al (2003) (o1%). For the group of cancer patients, the mean bias (-10%) was greater than the predetermined clinically acceptable level of 5%. For individuals, the limits of agreement for measurements of REE from the MGN compared to the VM were well outside clinically acceptable limits for both cancer patients and healthy subjects. Considering the positive results of the study by Nieman et al (2003), the results we observed are quite poor. We do not believe, however, that the results are an outcome of poor European Journal of Clinical Nutrition

measurement methods but may, in fact, be due to differences in the measurement instruments. Differences between measurements of REE from the VM and MGN may be due to differences in the measurement of VO2 and/or an incorrect assumption of a constant RQ of 0.85. Our results indicate that measurement of VO2 was significantly lower with the MG compared to the VM, with a greater bias in cancer patients compared to healthy subjects. Measured RQ by the VM was considerably lower for both cancer patients (0.71) and healthy subjects (0.72) compared to the assumed RQ of 0.85 with the MG. To assess the accuracy of the MG, a traditional indirect calorimeter was used as the standard. The previous study investigating the accuracy of the MG used a Douglas Bag to compare measurements (Nieman et al, 2003). Ventilated hoods are often referred to as the preferred portable collection system (Weissman et al, 1984; Feurer and Mullen, 1986). This study used the SensorMedics VMax229 indirect calorimeter, which collected expired air using a mouthpiece plus noseclip. In this way, both indirect calorimeters utilised similar collection systems. The lower measured VO2 observed with the MG may be due to differences in the collection system, gas analysis or selection of steady state. While both devices used mouthpieces for the collection of expired air, there were differences in the size of the mouthpiece. Mouthpieces have been shown to increase minute ventilation (volume of air inspired per minute) through increases in tidal volume (Weissman et al, 1984). It is unclear, however, what corresponding effect this has on VO2 and REE, with some suggestion that energy expenditure is not greatly influenced (McLean and Tobin, 1987; Segal, 1987). Decreasing the diameter of the mouthpiece to 9 mm, eliminated the effect on minute ventilation (Weissman et al, 1984). Although both mouthpieces had a diameter greater than 9 mm, the mouthpiece used with the VM had a greater diameter than that used with the MG. It was therefore assumed that REE measured by the VM may be slightly higher than that measured by the MG due to the potential influence on increasing minute ventilation. The observed overall underestimation by the MG in this study was therefore in the expected direction. The mechanisms for analysing oxygen concentration between the MG and VM differed. The VM uses a paramagnetic oxygen analyser, which is commonly used in other indirect calorimeters and maintains excellent stability (Branson, 1990). The MG on the other hand analyses oxygen concentration using a new technique, a dual channel fluorescent quenching sensor. The gas analysers in the VM were calibrated prior to each measurement using standard gases of known concentration. The oxygen analyser within the MG is not recalibrated. Differences between the oxygen analysers and the lack of regular calibration of the oxygen analyser in the MG would introduce bias between the two calorimeters. Without further information regarding the stability of the MG analyser however, we are unable to determine the magnitude or direction of the bias.


609 The selection of steady state from the VM was based on steady-state criteria that have been identified from the literature and rigorously tested (Feurer and Mullen, 1986; McClave et al, 2003b; Reeves et al, 2004). If steady state was not achieved with the VM, the measurement was discarded. The MG uses a proprietary algorithm for identifying steady state; however, the degree of variation that is permitted with this algorithm is unclear. If steady state is not achieved a mathematical average is used to calculate REE. As such, there is likely to be considerable difference between the steadystate criteria used by the two devices introducing bias between the measurements. Nieman et al (2003), in their comparison of the MG in healthy subjects, altered the programming of the MG so that the steady-state criteria used by the MG was identical to that used with the Douglas Bag. In this study, however, our intention was to measure REE with the MG as it would be used in practice. All subjects had reported fasting for at least 12 h overnight and no measurements appeared unusual. Four cancer patients and three healthy subjects, however, had a measured RQ below the physiological range of 0.67–1.30 (McClave et al, 2003a). Measurements on these seven subjects appeared to be random and balanced between the group of cancer patients and healthy subjects. As such these data have been included in the analyses, but would account for the lower mean RQ observed. REE was also predicted from the Harris–Benedict equations to assess the level of agreement with measured REE from the VM. For the group of cancer patients and healthy subjects, mean bias between predicted and measured REE was within clinically acceptable limits (710%). However, the limits of agreement for individual predictive accuracy were wide. These results have confirmed previous findings in patients with cancer (Bauer et al, 2004) and healthy subjects (Taaffe et al, 1995; Siervo et al, 2003). This study had some minor limitations. Firstly, recruitment of cancer patients was poorer than anticipated, resulting in a smaller sample size. With a sample size of 15, the study was powered (80%) to detect differences of 11% or greater in cancer patients and 14% or greater in healthy subjects, compared to the clinically important predetermined difference of 5% or greater. Hence, interpretation of results was primarily based on clinical significance. Secondly, this study did not investigate the reproducibility of the VM or the MG indirect calorimeters. Reproducibility of the MG had previously been assessed to be high (Nieman et al, 2003) and therefore was not repeated in this study. Reproducibility of traditional indirect calorimetry methods is often reported to be high, with measurement error in the order of less than 5% (Hansell et al, 1986; Fearon et al, 1988; Falconer et al, 1994). Although reproducibility of the VM was not measured in this study, if it is assumed to be of a similar magnitude to that reported by other studies, it is highly unlikely that any of the conclusions from these studies, where limits of agreement were approximately 730 to 40%, would be altered. That is, even with minimal measurement error in

the VM, neither the MG nor the prediction method would be considered clinically acceptable for individual patients. The results of this study have indicated that the portable MG indirect calorimeter is acceptable for measuring the REE of healthy subjects at the group level, but does not measure REE within clinically acceptable limits for individual healthy subjects or for cancer patients at the individual or group level. While the Harris–Benedict equations only predicted REE within an acceptable degree of accuracy at the group level and not for individual cancer patients and healthy subjects, the limits of agreement were similar to that observed with the MG. As these equations are universally available, have no cost and are easy to use, this study has indicated that the Harris–Benedict equations are currently a more appropriate, practical alternative to traditional measurement of energy expenditure, than the MG indirect calorimeter. Nevertheless, both the MG and Harris–Benedict equations have poor clinical accuracy for individual cancer patients and healthy subjects. A better understanding of the measurement methods, particularly the steady-state criteria used with the MG, may assist in identifying sources of error between the measurements.

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Clinical accuracy of the MedGemâ¢ indirect calorimeter for measuring ...

Clinical accuracy of the MedGemâ¢ indirect calorimeter for measuring ...

Clinical accuracy of the MedGemâ¢ indirect calorimeter for measuring ...

Clinical accuracy of the MedGemâ¢ indirect calorimeter for measuring ...