 Research
 Open Access
 Published:
Ventilatorderived carbon dioxide production to assess energy expenditure in critically ill patients: proof of concept
Critical Care volume 19, Article number: 370 (2016)
 The Letter to this article has been published in Critical Care 2016 20:399
 The Commentary to this article has been published in Critical Care 2016 20:72
Abstract
Introduction
Measurement of energy expenditure (EE) is recommended to guide nutrition in critically ill patients. Availability of a gold standard indirect calorimetry is limited, and continuous measurement is unfeasible. Equations used to predict EE are inaccurate. The purpose of this study was to provide proof of concept that EE can be accurately assessed on the basis of ventilatorderived carbon dioxide production (VCO_{2}) and to determine whether this method is more accurate than frequently used predictive equations.
Methods
In 84 mechanically ventilated critically ill patients, we performed 24h indirect calorimetry to obtain a gold standard EE. Simultaneously, we collected 24h ventilatorderived VCO_{2}, extracted the respiratory quotient of the administered nutrition, and calculated EE with a rewritten Weir formula. Bias, precision, and accuracy and inaccuracy rates were determined and compared with four predictive equations: the Harris–Benedict, Faisy, and Penn State University equations and the European Society for Clinical Nutrition and Metabolism (ESPEN) guideline equation of 25 kcal/kg/day.
Results
Mean 24h indirect calorimetry EE was 1823 ± 408 kcal. EE from ventilatorderived VCO_{2} was accurate (bias +141 ± 153 kcal/24 h; 7.7 % of gold standard) and more precise than the predictive equations (limits of agreement −166 to +447 kcal/24 h). The 10 % and 15 % accuracy rates were 61 % and 76 %, respectively, which were significantly higher than those of the Harris–Benedict, Faisy, and ESPEN guideline equations. Large errors of more than 30 % inaccuracy did not occur with EE derived from ventilatorderived VCO_{2}. This 30 % inaccuracy rate was significantly lower than that of the predictive equations.
Conclusions
In critically ill mechanically ventilated patients, assessment of EE based on ventilatorderived VCO_{2} is accurate and more precise than frequently used predictive equations. It allows for continuous monitoring and is the best alternative to indirect calorimetry.
Introduction
The optimal energy target in the first days of critical illness remains controversial [1–3]. Nonetheless, measurement of energy expenditure (EE) is important to prevent early overfeeding and later underfeeding, as both are associated with increased mortality [4–6]. EE can be accurately assessed with indirect calorimetry, which measures oxygen consumption (VO_{2}) and carbon dioxide production (VCO_{2}) from respiratory gases [7, 8]. EE is then calculated using the abbreviated formula published by Weir [9]:
Indirect calorimetry is often not available and is resource and timeconsuming. Daily assessment of EE is not feasible but could be important because EE is known to vary widely over time as a result of changing metabolic rate [10–12]. In the absence of indirect calorimetry, numerous predictive equations are used to estimate EE, including the Harris–Benedict equation and the European Society for Clinical Nutrition and Metabolism (ESPEN) guideline equation of 25 kcal/kg/day [13, 14]. These equations are notoriously inaccurate for individual critically ill patients, owing to large disease, treatment, and interindividualrelated differences in metabolic rate [15–17]. The Penn State University and Faisy equations were especially developed for mechanically ventilated critically ill patients and include temperature and minute ventilation in the calculation of EE [15, 18]. The Penn State University equation is recommended by the Academy of Nutrition and Dietetics when indirect calorimetry is not feasible [19]. Validation studies for both equations are limited.
An alternative method to assess EE in mechanically ventilated critically ill patients could be the use of VCO_{2} measurements only. This is practical, as most mechanical ventilators provide the option to measure VCO_{2} continuously. When VCO_{2} is known, the Weir formula can be used to calculate VO_{2}, assuming the respiratory quotient (RQ), which is the ratio between VCO_{2} and VO_{2}. Its physiologic range of 0.67–1.2 depends on the type of the actually metabolized substrate provided that ventilation and acid–base balance are stable [20]. Although the latter vary during critical illness, in prolonged measurement periods metabolic CO_{2} production equals its excretion. Given these limitations, we hypothesized that EE could be assessed on the basis of ventilatorderived VCO_{2} using RQ of the administered nutrition and the rewritten Weir formula.
The aim of this study was to provide proof of concept that EE can be accurately assessed on the basis of ventilatorderived VCO_{2} and nutritional RQ and to determine whether this method is more accurate than frequently used predictive equations.
Material and methods
Study design and setting
This prospective observational study was conducted in the mixed medicalsurgical adult intensive care unit (ICU) of the VU University Medical Center in Amsterdam, The Netherlands. The study was approved by the Medical Ethics Committee of the VU University Medical Center. The need for written informed consent was waived because indirect calorimetry is part of routine care in our ICU and imposes no burden on patients.
Subjects
Inclusion criteria were age 18 years or older, mechanical ventilation, ICU stay of 3 days or more, and enteral or parenteral nutrition reaching at least twothirds of calculated nutritional target. According to the standard practice of the unit, the initial nutritional target was an energy delivery as calculated with the revised Harris–Benedict equation [21], adding 20 % for stress and 10 % for activity [22, 23] and protein delivery of 1.2–1.5 g/kg preadmission body weight per day [24]. This target was adjusted based on indirect calorimetry measurements. An algorithm was used to determine the optimal nutritional product and amount needed to meet both protein and energy requirements [25]. Patients were excluded if they failed to meet accuracy criteria or safety criteria for indirect calorimetry, being a fraction of inspired oxygen (FiO_{2}) greater than 0.6, air leakage through cuff or chest tubes, or a positive endexpiratory pressure (PEEP) greater than 14 cmH_{2}O (arbitrary limit).
Our patient data management system (PDMS) (MetaVision; iMDsoft, Düsseldorf, Germany) was used to routinely record demographic and clinical data; Acute Physiology and Chronic Health Evaluation (APACHE) II and III scores and APACHE IV predicted mortality [26–28]; diagnosis group; type, amount, and composition of feeding; and ventilation characteristics. Sedation was assessed by using the Ramsay Sedation Scale [29].
Study protocol
Patient weight and height were recorded upon ICU admission. Preadmission weight and height were obtained, and, if not available, they were measured or estimated by a clinician. Indirect calorimetry was performed for 24 h. Simultaneously, 24h minutebyminute ventilatorderived VCO_{2}, which is routinely exported to the PDMS, was recorded. After the first hour of measurement, type and amount of nutrition were adjusted to meet EE as measured with indirect calorimetry. All macronutrient intake during the study period, including propofol and glucose infusions, were routinely stored in the PDMS.
Methods used to assess energy expenditure
Energy expenditure from indirect calorimetry
Twentyfour–hour indirect calorimetry was performed with a Deltatrac II MBM200 Metabolic Monitor (Datex, Helsinki, Finland) connected to the ventilator. Before this study, an alcoholburning test was performed to calibrate the metabolic monitor. Before each 24h measurement, the metabolic monitor was prepared and calibrated according to the manufacturer’s instructions. Artifact suppression was turned on. For each patient, VCO_{2}, VO_{2}, RQ, and energy expenditure from indirect calorimetry (EE:Calorimetry) were recorded minute by minute and exported to a computer. For comparison, the mean 24h value was calculated for each patient.
Energy expenditure from ventilatorderived volume of carbon dioxide and nutritional respiratory quotient
We use SERVOi mechanical ventilators (Maquet, Rastatt, Germany) in our ICU. These have mainstream CO_{2} sensors connected to the airway adapter that measure endtidal CO_{2}. Sensors were calibrated before every study period and subsequently at 8h intervals or more often if necessary. The SERVOi ventilator calculates VCO_{2} from the product of CO_{2} concentration in expiratory air and the expiratory volume (VCO_{2} = volume × fraction of expired CO_{2}).
VCO_{2} is displayed breath by breath and exported to the PDMS once each minute. For each patient, 24h minutebyminute VCO_{2} values were collected. To calculate energy expenditure from ventilatorderived volume of carbon dioxide and nutritional respiratory quotient (EE:VCO_{2}), the average 24h VCO_{2} (ml/min) was used.
Nutritional RQ was calculated considering 24h macronutrient delivery, including calories provided by propofol (1.1 kcal/ml) and glucose (4 kcal/g). We assumed RQs of 1 for carbohydrates, 0.7 for fat, and 0.8 for protein. Nutritional RQ was calculated from the weighted average RQ for intake during the study period. For example, if the composition of the enteral formula was 16 % protein, 49 % carbohydrates, and 35 % fat, the nutritional RQ was calculated as 0.16 × 0.8 + 0.49 × 1 + 0.35 × 0.7 = 0.86.
After calculating nutritional RQ for each patient, EE:VCO_{2} was subsequently calculated using the following rewritten Weir formula:
Energy expenditure derived from predictive equations
EE was calculated using four predictive equations: the Harris–Benedict equation [21], the ESPEN guideline equation [14], the Penn State University 2003b equation [15], and the Faisy equation [18].
Energy expenditure was calculated with the Harris–Benedict equation (EE:HB) as follows:
Energy expenditure was calculated with the European Society for Clinical Nutrition and Metabolism guideline of 25 kcal/kg/day (EE:Esp25).
Energy expenditure was calculated with the Penn State University 2003b equation (EE:PSU) as follows:
The MifflinSt Jeor calculation is as follows [30]:
Tmax is highest body temperature during the 24h study period, and Ve is mean minute ventilation during the 24h study period.
Energy expenditure was calculated with the Faisy equation (EE:Faisy) as follows:
Ve is mean minute ventilation during the 24h study period, and T is mean temperature during the 24h study period.
Endpoints
The primary endpoint was accuracy of EE:VCO_{2} using EE:Calorimetry as a gold standard. Secondary endpoints were the accuracy of EE:HB, EE:Esp25, EE:Faisy, and EE:PSU.
Data analysis
Descriptive data are reported as mean [standard deviation (SD)], median (25th–75th percentile), or number (percentage) as appropriate. Student’s t test was used for comparison of paired data. Correlations were calculated using Pearson’s test, and strength of correlation was expressed as r. The accuracy of the different measurement methods was assessed in accordance with the ISO 5725 standard [31], which describes how accuracy can be defined in terms of bias and precision. Bias is the systematic error as compared with the gold standard (in this case EE:Calorimetry), whereas precision is the random (nonsystematic) error of individual measurements. The inaccuracy of a measurement method can thus be due to a large bias (the systematic component), low precision (the random component), or both. Bias was calculated as the mean difference of EE:VCO_{2} (or equationbased EEs) and gold standard EE (EE:Calorimetry). EE was considered unbiased if the bias was less than 10 % of the gold standard EE [32]. Precision was quantified as the SD of the bias and the limits of agreement (2 SD). SDs of the different methods were compared using Levene’s test for equality of variances. Bland–Altman plots were used to graphically represent bias and limits of agreement [33]. Accuracy was further quantified by accuracy rates, which we defined as the proportion of patients for which the EE:VCO_{2} (or equationbased EE) predicted EE within 10 % and 15 % of gold standard EE:Calorimetry. We calculated greater than 25 % and greater than 30 % inaccuracy rates to quantify the occurrence of large errors, as the proportion of patients for which the EE:VCO_{2} (or equationbased EE) differed by more than 25 % or more than 30 % from gold standard EE:Calorimetry.
In a post hoc analysis, we calculated for which stress and activity factor the bias of the Harris–Benedict equation was lowest and used this equation in further data analysis (EE:HB15).
IBM SPSS 20 software (IBM, Armonk, NY, USA) was used for statistical analysis. A p value less than 0.05 was considered statistically significant.
Results
During the study period (20 March to 5 December 2013), 1172 patients were admitted to our ICU. Among them, 163 (13.9 %) were mechanically ventilated for more than 3 days with FiO_{2} 60 % or less and PEEP 14 cmH_{2}O or less. Among these 163 patients, 123 (75 %) received about twothirds of their nutritional energy target (defined by Harris–Benedict +30 %) and 92 of those 123 had no thoracic drains. Of the 92 eligible patients, 84 patients (91 %) were included (see Fig. 1). The main reason for missed inclusion was absence of a researcher. The included patients’ demographic, clinical, and nutritional characteristics are shown in Table 1. Twentysix patients (31 %) were female. The most prevalent ICU admission diagnoses were post–cardiac arrest, postsurgery, and trauma. Twelve patients (14 %) had sepsis. The mean APACHE II score was 23.9 ± 8.4. Most patients were on pressure support ventilation (82 %). The mean total macronutrient intake during the 24h study period was 1835 ± 627 kcal, including caloric intake from glucose and propofol infusions.
Energy expenditure, VO_{2}, VCO_{2}, and RQ
Mean 24h results for EE, VO_{2}, VCO_{2}, and RQ are presented in Table 2. Mean 24h EE:Calorimetry was 1823 ± 408 kcal. Mean 24h EE:VCO_{2} was 1963 ± 431 kcal, which was significantly higher than EE:Calorimetry (p < 0.001) (see Table 2).
Correlation
EE:VCO_{2} correlated strongly with EE:Calorimetry (r = 0.935). The equationbased EEs correlated less strongly and the correlation coefficient was lowest for EE:Esp25 (r = 0.639) (see Fig. 2).
Bias (mean difference of EE:VCO_{2} and predictive equations with EE:Calorimetry)
Bland–Altman plots are shown in Fig. 2. The bias of EE:VCO_{2} was +141 ± 153 kcal/24 h (7.7 % of EE:Calorimetry). This was significantly lower than the bias of EE:HB (−246 ± 263 kcal/24 h, p < 0.001), comparable to the bias of EE:Faisy (+176 ± 218 kcal/24 h, p = 0.226) and EE:Esp25 (+156 ± 344 kcal/24 h, p = 0.709), but higher than the bias of EE:PSU (−22 ± 254 kcal/24 h, p < 0.001). In post hoc analysis, we calculated that the bias of the Harris–Benedict equation was lowest with a stress and activity factor of +15 % (−10 ± 257 kcal/24 h). See Table 3 for detailed results. The bias of ventilatorderived VCO_{2} was 14.7 ml/min (6.5 % of VCO_{2}:Calorimetry). The bias of nutritional RQ was 0.0037 (0.4 % of RQ:Calorimetry).
Precision
Limits of agreement were smallest for EE:VCO_{2} (−166 to +447 kcal/24 h) The SD of the bias of EE:VCO_{2} was significantly smaller than that of all equationbased EE values (see Table 3 and Figs. 2 and 3).
Accuracy and inaccuracy rates
Less than 10 % and less than 15 % accuracy rates of EE:VCO_{2} were 61 % and 79 %, respectively. These were significantly higher than those of EE:HB, EE:Esp25, and EE:Faisy but not significantly different from EE:PSU and EE:HB15. Less than 25 % and less than 30 % inaccuracy rates of EE:VCO_{2} were 2 % and 0 %, respectively. The less than 30 % inaccuracy rate of EE:VCO_{2} was significantly lower than that of all equationbased EE values (Table 3 and Fig. 4).
Discussion
The present prospective observational study in critically ill mechanically ventilated patients provides proof of concept that EE can be accurately calculated from EE:VCO_{2}. Furthermore, it shows that this method is more precise than frequently used predictive equations. The bias or systematic error of EE:VCO_{2} was 141 kcal/24 h, indicating that EE:VCO_{2} as derived from the ventilator systematically overestimates EE compared with gold standard EE:Calorimetry. However, this bias corresponds to a relative error of only 7.7 % of the gold standard, whereas up to 10 % is considered acceptable according to a consensus statement [32]. The precision or random error component of EE:VCO_{2}, expressed as the SD of the bias and compared between methods by using Levene’s test, is visualized by the width of the limits of agreement in the Bland–Altman plots and in Fig. 2. The precision of EE:VCO_{2} was significantly better than that of the equations.
The accuracy rates of EE:VCO_{2} were higher than those of all predictive equations, but not significantly so for EE:PSU. However, the inaccuracy of EE:PSU was higher, with greater than 25 % and greater than 30 % inaccuracy rates of 10 % and 6 %, respectively, indicating that in more than half of the patients with inaccuracy of greater than 25 %, the error was even larger—namely, more than 30 % difference from EE as measured by indirect calorimetry.
High inaccuracy rates were found for EE:HB and EE:Esp25, making these equations unacceptable for use in critically ill patients. In all, EE:VCO_{2} appears to be the most precise equation and EE:PSU and EE:HB15 the most unbiased equations. Despite a better estimation of the mean EE of the study population, the inferior precision of EE:PSU and EE:HB15 led to higher inaccuracy rates, which may result in severe over or underfeeding in a considerable number of patients. Thus, for the individual patient, EE:VCO_{2} performs best.
We further explored the source of the bias of EE:VCO_{2}, which can be due to inaccuracy of the VCO_{2} measurement or the RQ estimation. We found an unexpected bias of ventilatorderived VCO_{2} of 14.7 ml/min (6.5 % of VCO_{2}:Calorimetry). Assuming an RQ of 0.86, which is the RQ of most nutritional products, this systematic error accounts for 120 kcal/24 h (i.e., 85 % of the bias of EE:VCO_{2}). We noted the largest differences between ventilatorderived and calorimetryderived VCO_{2} in patients with extreme variations in respiratory rate and tidal volume. Rapid and irregular breathing may lead to errors in ventilatorderived VCO_{2} due to dyssynchrony between the flow and the CO_{2} measurement. Furthermore, the ventilator exports a singlebreath VCO_{2} value once each minute to the PDMS, which can lead to high variability in patients with irregular breathing. One way of improving the accuracy of the EE:VCO_{2} method is the development of more accurate VCO_{2} analyzers in mechanical ventilators, such as by more frequent sampling and data export.
A second source of error and an important limitation of our study is that the actual RQ of the patients was not known. In the present study, we used nutritional RQ. However, during critical illness, RQ is influenced not by actual nutritional intake alone. An unknown and uninhibitable part of energy is derived from endogenous sources, and there are different illnessrelated degrees of protein synthesis or catabolism, lipogenesis or lipolysis, and gluconeogenesis or glycolysis. Because of the uncertainty of actual RQ when endogenous sources are used for energy, we could not correct RQ if nutritional intake was less than EE. However, our patients received more than twothirds of actual EE, and this is the time point when measurement of EE becomes relevant. RQ is also influenced by periods of hypo and hyperventilation (e.g., induced by stress or sedation or in respiratory compensation for metabolic acidosis or alkalosis). This will temporarily modulate VCO_{2} [34]; however, over 24 h, mean VCO_{2} reflects CO_{2} produced by metabolism. Although nutritional RQ did indeed not correlate with measured RQ:Calorimetry, only 15 % of the bias of EE:VCO_{2} was attributable to the difference between nutritional RQ and RQ:Calorimetry.
In our study, additional calories provided during the study period by glucose and propofol were taken into account. With a single exception in a patient who received large amounts of glucose 40 %, these additional calories did not substantially change nutritional RQ and subsequently EE:VCO_{2}.
Mehta et al. tested the accuracy of a VCO_{2} based equation to calculate EE in critically ill children [35]. Metabolic data from mechanically ventilated children was used to derive this equation. The equation was then applied to a second dataset of critically ill children to test accuracy. They found superiority of the simplified equation over standard equations. These findings further strengthen the concept of using VCO_{2} measurement instead of estimating equations to calculate EE in critically ill adults and children. It should be noted, however, that the VCO_{2} in the Mehta study was not independently measured; it was derived from the metabolic monitor. Thus, a direct comparison between EE:Calorimetry and a ventilatorderived or separate modulederived EE:VCO_{2} was not performed. Mehta et al. mentioned this as a limitation of their study. Also, measurement periods were relatively short. We were able to perform simultaneous 24h VCO_{2} and indirect calorimetric measurements in a large and representative population of ICU patients ventilated for more than 3 days, providing information on realtime total EE.
Indirect calorimetry remains the gold standard. However, the most validated system, the Deltatrac, is no longer being manufactured. While we await new, accurate, affordable metabolic monitors, EE:VCO_{2} could be of great benefit for ICUs that do not have indirect calorimetry available. The method can also be used to monitor fluctuations in EE over time and to identify patients at risk for being over or underfed. EE:VCO_{2} allows for daily adjustment of nutrition in ventilated patients. This may be important because metabolic rate and associated energy requirements vary widely during the day and during the course of disease [11, 12, 36, 37]. Another major advantage of EE:VCO_{2} is that the calculation of EE is independent of body length and weight, thereby reducing error.
We are aware of the fact that not all ICUs have mechanical ventilators that measure VCO_{2} continuously. Most modern ventilators do have this option available and cost less than a metabolic monitor. Of note, the present validation was performed with one type of mechanical ventilator. We do not know the accuracy of VCO_{2} measurements with other ventilators.
We excluded patients with FiO_{2} exceeding 0.6 for reliability reasons and patients with PEEP above 14 cmH_{2}O because of risks associated with disconnection when connecting the indirect calorimeter to the ventilator. Therefore, our method was not validated in this population. Nonetheless, we suppose that EE:VCO_{2} is reliable in all mechanically ventilated patients, regardless of ventilator settings, provided that air leakage is not present.
The most important message of this study is that EE (kcal/day) can be calculated at the bedside as 8.19 × VCO_{2} (ml/min). This equation is derived from the rewritten Weir formula using an RQ of 0.86, which is the RQ of most nutritional products, and after converting liters per minute to milliliters per minute.
Conclusions
In critically ill mechanically ventilated adult patients, the assessment of EE from ventilatorderived VCO_{2} is accurate and more precise than frequently used predictive equations. It allows for continuous monitoring and provides the best alternative to gold standard indirect calorimetry. Future studies are necessary to improve accuracy of the VCO_{2} measurement, to detect sources of error, and to investigate whether daily adjustment of nutrition based on ventilatorderived EE improves the outcome of ICU patients.
Key messages

EE from ventilatorderived VCO_{2} is accurate and more precise than predictive equations.

This method allows for continuous monitoring and is the best alternative to indirect calorimetry.

EE (kcal/day) can be calculated at the bedside as 8.19 × VCO_{2} (ml/min).
Abbreviations
 APACHE:

Acute Physiology and Chronic Health Evaluation
 BMI:

Body mass index
 CPAP:

Continuous positive airway pressure
 CVVH:

continuous venovenous hemofiltration, EE, Energy expenditure
 EE:Esp25:

Energy expenditure calculated with the European Society for Clinical Nutrition and Metabolism guideline equation of 25 kcal/kg/day
 EE:Faisy:

Energy expenditure calculated with the Faisy equation
 EE:HB:

Energy expenditure calculated with the Harris–Benedict equation
 EE:PSU:

Energy expenditure calculated with the Penn State University 2003b equation
 EE:VCO_{2} :

Energy expenditure from ventilatorderived volume of carbon dioxide and nutritional respiratory quotient
 ESPEN:

European Society for Clinical Nutrition and Metabolism
 FiO_{2} :

Fraction of inspired oxygen
 ICU:

Intensive care unit
 IQR:

Interquartile range
 MAP:

mean arterial pressure
 PaO_{2} :

Partial pressure of arterial oxygen
 PC:

Pressure control
 PDMS:

Patient data management system
 PEEP:

Positive endexpiratory pressure
 PS:

Pressure support
 RQ:

Respiratory quotient
 SD:

Standard deviation
 T:

Mean temperature during the 24h study period
 Tmax:

Maximum body temperature during the 24h study period
 VCO_{2} :

carbon dioxide production
 Ve:

Mean minute ventilation during the 24h study period
 VO_{2} :

oxygen consumption
References
 1.
Casaer MP, Mesotten D, Hermans G, Wouters PJ, Schetz M, Meyfroidt G, et al. Early versus late parenteral nutrition in critically ill adults. N Engl J Med. 2011;365:506–17.
 2.
Rice TW, Mogan S, Hays MA, Bernard GR, Jensen GL, Wheeler AP. Randomized trial of initial trophic versus fullenergy enteral nutrition in mechanically ventilated patients with acute respiratory failure. Crit Care Med. 2011;39:967–74.
 3.
Singer P, Anbar R, Cohen J, Shapiro H, ShalitaChesner M, Lev S, et al. The tight calorie control study (TICACOS): a prospective, randomized, controlled pilot study of nutritional support in critically ill patients. Intensive Care Med. 2011;37:601–9.
 4.
Villet S, Chiolero RL, Bollmann MD, Revelly JP, Cayeux RNM, Delarue J, et al. Negative impact of hypocaloric feeding and energy balance on clinical outcome in ICU patients. Clin Nutr. 2005;24:502–9.
 5.
Weijs PJM, Looijaard GPM, Beishuizen A, Girbes ARJ, Oudemansvan Straaten HM. Early high protein intake is associated with low mortality and energy overfeeding with high mortality in nonseptic mechanically ventilated critically ill patients. Crit Care. 2014;18:701.
 6.
Singer P, Pichard C, Heidegger CP, Wernerman J. Considering energy deficit in the intensive care unit. Curr Opin Clin Nutr Metab Care. 2010;13:170–6.
 7.
Cooney RN, Frankenfield DC. Determining energy needs in critically ill patients: equations or indirect calorimeters. Curr Opin Crit Care. 2012;18:174–7.
 8.
Branson RD, Johannigman JA. The measurement of energy expenditure. Nutr Clin Pract. 2004;19:622–36.
 9.
Weir JB. New methods for calculating metabolic rate with special reference to protein metabolism. Nutrition. 1990;6:213–21.
 10.
McClave SA, Martindale RG, Kiraly L. The use of indirect calorimetry in the intensive care unit. Curr Opin Clin Nutr Metab Care. 2013;16:202–8.
 11.
Preiser JC, Ichai C, Orban JC, Groeneveld AB. Metabolic response to the stress of critical illness. Br J Anaesth. 2014;113:945–54.
 12.
Berger MM, Pichard C. Best timing for energy provision during critical illness. Crit Care. 2012;16:215.
 13.
Harris JA, Benedict FG. A biometric study of human basal metabolism. Proc Natl Acad Sci USA. 1918;4:370–3.
 14.
Kreymann KG, Berger MM, Deutz NE, Hiesmayr M, Jolliet P, Kazandjiev G, et al. ESPEN guidelines on enteral nutrition: intensive care. Clin Nutr. 2006;25:210–23.
 15.
Frankenfield DC, Coleman A, Alam S, Cooney RN. Analysis of estimation methods for resting metabolic rate in critically ill adults. J Parenter Enteral Nutr. 2009;33:27–36.
 16.
Weijs PJ. Validity of predictive equations for resting energy expenditure in US and Dutch overweight and obese class I and II adults aged 18–65 y. Am J Clin Nutr. 2008;88:959–70.
 17.
Walker RN, Heuberger RA. Predictive equations for energy needs for the critically ill. Respir Care. 2009;54:509–21.
 18.
Faisy C, Guerot E, Diehl J, Labrousse J, Fagon J. Assessment of resting energy expenditure in mechanically ventilated patients. Am J Clin Nutr. 2003;78:241–9.
 19.
Academy of Nutrition and Dietetics. Critical illness evidencebased nutrition practice guideline. Chicago: Author; 2012. http://www.guideline.gov/content.aspx?id=39404. Accessed 15 Oct 2015.
 20.
Haugen HA, Chan LN, Li F. Indirect calorimetry: a practical guide for clinicians. Nutr Clin Pract. 2007;22:377–88.
 21.
Roza AM, Shizgal HM. The Harris Benedict equation reevaluated: resting energy requirements and the body cell mass. Am J Clin Nutr. 1984;40:168–82.
 22.
Alexander E, Susla GM, Burstein AH, Brown DT, Ognibene FP. Retrospective evaluation of commonly used equations to predict energy expenditure in mechanically ventilated, critically ill patients. Pharmacotherapy. 2004;24:1659–67.
 23.
van Lanschot JJ, Feenstra BW, Vermeij CG, Bruining HA. Calculation versus measurement of total energy expenditure. Crit Care Med. 1986;14:981–5.
 24.
Sauerwein HP, Strack van Schijndel RJ. Perspective: How to evaluate studies on perioperative nutrition? Considerations about the definition of optimal nutrition for patients and its key role in the comparison of the results of studies on nutritional intervention. Clin Nutr. 2007;26:154–8.
 25.
van Strack van Schijndel RJM, Weijs PJM, Sauerwein HP, de Groot SDW, Beishuizen A, Girbes ARJ. An algorithm for balanced protein/energy provision in critically ill mechanically ventilated patients. E Spen Eur E J Clin Nutr Metab. 2007;2:69–74.
 26.
Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Crit Care Med. 1985;13:818–29.
 27.
Knaus WA, Wagner DP, Draper EA, Zimmerman JE, Bergner M, Bastos PG, et al. The APACHE III prognostic system: risk prediction of hospital mortality for critically ill hospitalized adults. Chest. 1991;100:1619–936.
 28.
Zimmerman JE, Kramer AA, McNair DS, Malila FM. Acute Physiology and Chronic Health Evaluation (APACHE) IV: hospital mortality assessment for today’s critically ill patients. Crit Care Med. 2006;34:1297–310.
 29.
Ramsay MAE, Savege TM, Simpson BRJ, Goodwin R. Controlled sedation with alphaxalonealphadolone. Br Med J. 1974;2:656–9.
 30.
Mifflin MD, St Jeor ST, Hill LA, Scott BJ, Daugherty SA, Koh YO. A new predictive equation for resting energy expenditure in healthy individuals. Am J Clin Nutr. 1990;51:241–7.
 31.
International Organization for Standardization (ISO). Accuracy (trueness and precision) of measurement methods and results. Part 1: general principles and definitions. ISO 57251:1994. https://www.iso.org/obp/ui/#iso:std:iso:5725:1:ed1:en. Accessed 15 Oct 2015.
 32.
Frankenfield D, Hise M, Malone A, Russell M, Gradwell E, Compher C. Prediction of resting metabolic rate in critically ill adult patients: results of a systematic review of the evidence. J Am Diet Assoc. 2007;107:1552–61.
 33.
Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;1:307–10.
 34.
McClave SA, Lowen CC, Kleber MJ, McConnell JW, Jung LY, Goldsmith LJ. Clinical use of the respiratory quotient obtained from indirect calorimetry. J Parenter Enteral Nutr. 2003;27:21–6.
 35.
Mehta NM, Smallwood CD, Joosten KFM, Hulst JM, Tasker RC, Duggan CP. Accuracy of a simplified equation for energy expenditure based on bedside volumetric carbon dioxide elimination measurement – a twocenter study. Clin Nutr. 2015;34:151–5.
 36.
Fraipont V, Preiser JC. Energy estimation and measurement in critically ill patients. J Parenter Enteral Nutr. 2013;37:705–13.
 37.
Elia M. Insights into energy requirements in disease. Public Health Nutr. 2005;8:1037–52.
Acknowledgements
We thank Ronald Driessen of the Department of Adult Intensive Care Medicine at VU University Medical Center for his contribution.
Author information
Additional information
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
SNS, ARJG, PJMW, PWGE, and HMOvS designed the study. SNS and HA obtained the data. SNS, HJSdG, HMOvS, and PJMW analyzed the data. HMOvS had primary responsibility for final content. All authors contributed to the drafting of the manuscript. All authors read and approved the final manuscript.
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
About this article
Cite this article
Stapel, S.N., de Grooth, H.S., Alimohamad, H. et al. Ventilatorderived carbon dioxide production to assess energy expenditure in critically ill patients: proof of concept. Crit Care 19, 370 (2016). https://doi.org/10.1186/s1305401510872
Received:
Accepted:
Published:
Keywords
 Energy Expenditure
 Respiratory Quotient
 Indirect Calorimetry
 Predictive Equation
 Patient Data Management System