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Background & aims: This review aims to clarify the use of indirect calorimetry (IC) in nutritional therapy for critically ill and other patient populations. It features a comprehensive overview of the technical concepts, the practical application and current developments of IC. Methods: Pubmed-referenced publications were analyzed to generate an overview about the basic knowledge of IC, to describe advantages and disadvantages of the current technology, to clarify technical issues and provide pragmatic solutions for clinical practice and metabolic research. The International Multicentric Study Group for Indirect Calorimetry (ICALIC) has generated this position paper. Results: IC can be performed in in- and out-patients, including those in the intensive care unit, to measure energy expenditure (EE). Optimal nutritional therapy, defined as energy prescription based on measured EE by IC has been associated with better clinical outcome. Equations based on simple anthropometric measurements to predict EE are inaccurate when applied to individual patients. An ongoing international academic initiative to develop a new indirect calorimeter aims at providing innovative and affordable technical solutions for many of the current limitations of IC. Conclusion: Indirect calorimetry is a tool of paramount importance, necessary to optimize the nutrition therapy of patients with various pathologies and conditions. Recent technical developments allow broader use of IC for in- and out-patients.
Schematic presentation of indirect calorimetry used in patients on mechanical ventilation and on those breathing spontaneously. a) Breath by breath: Respiratory gas composition and flow are measured continuously by connecting the gas analyzers to the ventilator circuit. The signals received by the gas analyzers and flow meters are synchronized to calculate the oxygen consumption (VO 2 L/min) and CO 2 production (VCO 2 L/min) as the difference between the volumes of inhaled and exhaled O 2 and CO 2 per breath by integral calculations. The Haldane transformation [Table 1] is used to calculate the inhaled gas volume from exhaled gas volume measurement. The Weir's equation [Table 1] is used to calculate EE (kcal/d) per breath, and averaged for the duration of the measurement. The system is highly responsive to the dynamic changes of the EE, but prone to errors due to the response time of the gas analyzers and software. b) Mixing chamber: The O 2 concentration of inhaled air (FiO 2 ) is first measured. Exhaled gas is collected into the mixing chamber, where it is physically averaged and analyzed for O 2 (FeO 2 ) and CO 2 (FeCO 2 ) concentrations. The collected gas is eliminated through an independent chamber where the gas flow (Q) is kept constant at 40e45 L/min, to dilute the exhaled gas from the mixing chamber with the ambient air. CO 2 in the diluted gas (FedCO 2 ) is measured to calculate the CO 2 production (VCO 2 , L/min) by multiplying the concentration by the flow (VCO 2 ¼ FedCO 2 Â Q). An equation using the Haldane transformation allows the calculation of the respiratory quotient (RQ) from the measured O 2 and CO 2 values (RQ ¼ (1ÀFiO 2 )/[(FiO 2 ÀFeO 2 )/FeCO 2 ÀFiO 2 ]), and thus enables the calculation of the oxygen consumption (VO 2 , L/min; VO 2 ¼ VCO 2 /RQ). This unique method used in the Deltatrac Metabolic Monitor ® (Datex, Finland) enables VO 2 and VCO 2 measurements without measuring the flow of the exhaled gas, which usually introduces technical difficulties. c) Canopy: The canopy is used to measure EE in spontaneously breathing subjects. The subject is placed under a clear canopy with a plastic drape to avoid air leakage. Calorimeters feature constant flow generator to create an outward flow through the canopy. The exhaled breath by the subject is diluted by the constant flow Q (L/min), and collected by the calorimeter for gas analysis (FedO 2 , FedCO 2 ), and enables calculations of VO 2 and VCO 2 [Table 1]. FiO 2 and FiCO 2 are either assumed as ambient air values or measured, depending on the calorimeter. These values are used to calculate the EE using the Weir's equation. (1: flow analysis, 2: FiO 2 , 3: FiCO 2 , 4: FeO 2 , 5:FeCO 2 , 6:FedCO 2 , 7: FedO 2 ; small arrows: respiratory gas flow, solid line: gas sampling, dotted line: signal for flow analysis, small arrows: respiratory gas flow, bold arrow: constant flow).
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Review
Indirect calorimetry in nutritional therapy. A position paper by the
ICALIC study group
Taku Oshima
a
, Mette M. Berger
b
, Elisabeth De Waele
c
, Anne Berit Guttormsen
d
,
e
,
f
,
Claudia-Paula Heidegger
g
, Michael Hiesmayr
h
, Pierre Singer
i
, Jan Wernerman
j
,
Claude Pichard
k
,
*
a
Department of Emergency and Critical Care Medicine, Chiba University Graduate School of Medicine, 1-8-1 Inohana Chuou-ku, Chiba City, Chiba 260-
8677, Japan
b
Adult Intensive Care, Lausanne University Hospital, 1011 Lausanne, Switzerland
c
Department of Intensive Care, Vrije Universiteit Brussel, Brussels, Belgium
d
Department of Anaesthesiology and Intensive Care, Haukeland University Hospital, Jonas Liesvei 65, 5021 Bergen, Norway
e
Department of Clinical Medicine University of Bergen, Bergen, Norway
f
Haukeland Universitetssykehus Laboratoriebygget, 7. etg. Heis øst, Norway
g
Service of Intensive Care, Geneva University Hospital, Rue Gabrielle-Perret-Gentil 4, 1211 Geneva 14, Switzerland
h
Division of Cardiac Thoracic Vascular Anesthesia and Intensive Care Medicine, University Hospital of Vienna, Waehrihger Guertel 18-20, 1090 Vienna,
Austria
i
Critical Care Medicine, Institute for Nutrition Research, Rabin Medical Center, Beilison Hospital, Petah Tikva 49100, Israel
j
Department of Anesthesiology and Intensive Care Medicine, Karolinska University Hospital Huddinge, Sweden
k
Nutrition Unit, Geneva University Hospital, Rue Gabrielle-Perret-Gentil 4, 1211 Geneva 14, Switzerland
article info
Article history:
Received 21 February 2016
Accepted 15 June 2016
Keywords:
Indirect calorimetry
Energy expenditure (EE)
Respiratory quotient (RQ)
Oxygen consumption (VO
2
)
Carbon dioxide production (VCO
2
)
Resting energy expenditure (REE)
summary
Background &aims: This review aims to clarify the use of indirect calorimetry (IC) in nutritional therapy
for critically ill and other patient populations. It features a comprehensive overview of the technical
concepts, the practical application and current developments of IC.
Methods: Pubmed-referenced publications were analyzed to generate an overview about the basic
knowledge of IC, to describe advantages and disadvantages of the current technology, to clarify technical
issues and provide pragmatic solutions for clinical practice and metabolic research. The International
Multicentric Study Group for Indirect Calorimetry (ICALIC) has generated this position paper.
Results: IC can be performed in in- and out-patients, including those in the intensive care unit, to
measure energy expenditure (EE). Optimal nutritional therapy, dened as energy prescription based on
measured EE by IC has been associated with better clinical outcome. Equations based on simple
anthropometric measurements to predict EE are inaccurate when applied to individual patients. An
ongoing international academic initiative to develop a new indirect calorimeter aims at providing
innovative and affordable technical solutions for many of the current limitations of IC.
Conclusion: Indirect calorimetry is a tool of paramount importance, necessary to optimize the nutrition
therapy of patients with various pathologies and conditions. Recent technical developments allow
broader use of IC for in- and out-patients.
©2016 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.
1. Introduction
Indirect calorimetry (IC) measures the oxygen consumption and
the carbon dioxide production, which correspond to the cellular
respiration and allows to calculate the energy expenditure (EE) of
the whole body [1]. The study of the basic principles started more
than 100 years ago mainly by physicists and chemists, from the
discovery of gas and its components to the establishment of the
*Corresponding author. Geneva University Hospital, 1211 Geneva 14,
Switzerland. Tel.: þ41 22 372 93 45; fax: þ41 22 372 93 63.
E-mail addresses: t_oshima@chiba-u.jp (T. Oshima), Mette.Berger@chuv.ch
(M.M. Berger), Elisabeth.DeWaele@uzbrussel.be (E. De Waele), anne.guttormsen@
helse-bergen.no (A.B. Guttormsen), claudia-paula.heidegger@hcuge.ch
(C.-P. Heidegger), michael.hiesmayr@meduniwien.ac.at (M. Hiesmayr), psinger@
clalit.org.il (P. Singer), jan.wernerman@karolinska.se (J. Wernerman), claude.
pichard@unige.ch (C. Pichard).
Contents lists available at ScienceDirect
Clinical Nutrition
journal homepage: http://www.elsevier.com/locate/clnu
http://dx.doi.org/10.1016/j.clnu.2016.06.010
0261-5614/©2016 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.
Clinical Nutrition xxx (2016) 1e12
Please cite this article in press as: Oshima T, et al., Indirect calorimetry in nutritional therapy. A position paper by the ICALIC study group, Clinical
Nutrition (2016), http://dx.doi.org/10.1016/j.clnu.2016.06.010
concept of gas exchange related to combustion [1]. Technical pro-
gresses have enabled measurements of oxygen (O
2
) and carbon
dioxide (CO
2
) concentrations, as well as the volume of respiratory
gasses and heat production of living organisms. In 1949, Weir
derived an equation to calculate EE based on the heat produced
when a given substrate was burned and the volume of O
2
needed to
burn this substrate and on the protein oxidation derived from urea
excretion via the kidney. The EE can be derived from O
2
con-
sumption, CO
2
production and urea excretion [Table 1][2]. Many
proposed formulae deviate minimally from the Weir formula, and
are not valid if other substrates such as ketones or pyruvate are
oxidized in substantial amounts [5]. The principle that nitrogen is
neither utilized nor produced during respiration has enabled
calculation of EE without measuring inhaled air volume [4]. This
principle, known as the Haldane transformation, has contributed
greatly to simplifying the measurement systems as only expiratory
gas volume needed to be measured.
Measuring EE was labor-intensive and reserved for laboratory
research. It is only in the 1980's, that indirect calorimeters were
commercialized for medical use. Their complexity and high cost
have limited their use for clinical routine during the last 4 decades.
IC was mostly used for metabolic research. Nevertheless, in clinical
practice it has been taking hold, and became indispensable in pe-
diatric intensive care units (ICU) [6]. In critically ill adults, half of
patients in a mixed medical-surgical ICU had indications for IC [7,8],
but were rarely measured.
The assessment of EE requires IC and cannot be predicted by
equations [9,10]: a number of predictive equations based on simple
anthropometric measures and some including gender, age and
minute ventilation have been proposed for clinical use as a surro-
gate of measuring EE [11]. Unfortunately, many reports have shown
that predictive equations are not accurate enough, because patients
with acute or chronic conditions have different metabolic charac-
teristics, reected by highly variable EE. Body composition is also an
important modier of EE because fat free mass accounts for most of
EE [12].
Recently, it became clear that both overfeeding and under-
feeding can be harmful [13e18] and that optimizing nutrition
support to the patients specic needs is an urgent task. IC is the
only practical clinical method to measure EE of in- and out-patients
[3,19,20] in order to tailor nutrition therapy/support to their spe-
cic needs; assuming that the energy target should match EE.
However, the calorimeters currently available on the market are not
sufciently accurate [9,21e24], difcult to use, and too expensive to
be readily accesible in general hospitals.
An ongoing initiative supported by two major academic orga-
nizations (i.e. The European Society for Clinical Nutrition and
Metabolism (ESPEN) and The European Society for Intensive Care
Medicine (ESICM)) was launched to develop a new indirect calo-
rimeter. The goals were dened by a bottom-up approach with the
aim of developing an accurate, easy-to-use and affordable indirect
calorimeter for the use of the scientic and medical community.
This review aims at summarizing the scientic background
supporting IC in order to optimize nutrition therapy for criticallyill
patients and other patient populations. It features a comprehensive
overview of the technical concepts, the practical application and
current developments of IC.
2. Technical concepts
2.1. Calorimetry: the basics
IC measures inspired and expired gas exchanges to calculate EE.
This is possible because heat production is tightly correlated with
O
2
consumption (VO
2
) and CO
2
production (VCO
2
) according to the
type of energy substrate [20,25].
The conditions of the subjects during IC must be dened as they
deeply inuence the results. For healthy individuals, basal energy
expenditure (BEE) is measured in a resting state that is free of
physical and psychological stress, a thermally neutral environment,
i.e. at temperature ranges where energy used for the body tem-
perature maintenance is minimal, and a fasting state, i.e. no oral
intake for more than 10 h prior to the measurement, to avoid the EE
related to physical activity and diet-induced thermogenesis (DIT)
[Table 2]. DIT is dened as the production of heat related to sub-
strate oxidation during energy uptake. Resting energy expenditure
(REE) is dened as the sum of BEE and DIT, and total energy
expenditure (TEE) as the sum of REE and activity induced energy
expenditure (AEE) [1,25].Bydenition, BEE measurements must be
conducted in conditions that are unfeasible for diseased individuals
[Table 3]. In clinical practice, REE or TEE reects the patient energy
needs. For patients in the ICU, measured EE should be considered as
TEE. If physical activity becomes a standard routine ICU care in the
future, then this statement must be revised.
2.2. How is EE measured?
IC requires the measurement of inspired and expired O
2
and
expired CO
2
concentrations, as well as the volume of expired gas
per minute to calculate the VO
2
(L/min) and VCO
2
(L/min) [28]. Then
VO
2
and VCO
2
are used to calculate the EE(kcal/day) using the
Weir's equation [Table 1][2,20,28,29].
In a mechanically ventilated patient, the gas sampling is ob-
tained from the circuit connecting the endotracheal tube to the
ventilator, and measured by using either the breath-by-breath
analysis [Fig. 1 a] or the analysis using a mixing chamber [Fig. 1
b] [Table 4]. In spontaneously breathing subjects, a ventilated
canopy hood or a tted face mask is used to collect the inspired and
expired gas [Fig. 1c] [28]. Air leaks of respiratory gases alter the
accuracy of the measurements and should be avoided.
For measurements using the canopy without O
2
enrichment,
VO
2
and VCO
2
can be calculated as a difference between the O
2
concentration in ambient air and the measured O
2
and CO
2
con-
centration in the expired gas, collected by the canopy. For mea-
surements in mechanically ventilated conditions or using the
canopy with O
2
enrichment, the measurements are more complex.
Breath-by-breath systems measure the exhaled gas volume and the
O
2
and CO
2
concentration transitions, and integrate the product of
instantaneous expired gas concentrations with instantaneous
expiratory ow over time. A mixing chamber system measures the
Table 1
Equations used for the calculations related to indirect calorimetry [2e4].
Calculations of O
2
consumption
and CO
2
production
VO
2
¼(Vi FiO
2
)e(Ve FeO
2
)
VCO
2
¼(Ve FeCO
2
)e(Vi FiCO
2
)
Haldane transformation
Assumption based on the concept that N
2
is constant in inspired and expired gas
Vi ¼[FeN
2
/FiN
2
]Ve
FeN
2
¼(1 FeO
2
FeCO
2
)
FiN
2
¼(1 FiO
2
FiCO
2
)
If FiCO
2
of 0.03e0.05% is ignored,
VO
2
¼[(1 FeO
2
FeCO
2
)(FiO
2
FeO
2
)Ve]/(1FiO
2
)
Weir's equation
EE ¼[(VO
2
3.941) þ(VCO
2
1.11) þ(u N
2
2.17)] 1.44
VO
2
:O
2
consumption (L/min), VCO
2
:CO
2
production (L/min), Vi: inspired volume
(L), Ve: expired volume (L), FiO
2
: fraction of inspired oxygen, FeO
2
: fraction of
expired oxygen, FeN
2
: fraction of expired nitrogen, FiN
2
: fraction of inspired ni-
trogen, EE: energy expenditure (kcal/d), uN
2
: urinary nitrogen (g/d).
T. Oshima et al. / Clinical Nutrition xxx (2016) 1e122
Please cite this article in press as: Oshima T, et al., Indirect calorimetry in nutritional therapy. A position paper by the ICALIC study group, Clinical
Nutrition (2016), http://dx.doi.org/10.1016/j.clnu.2016.06.010
inhaled and exhaled gasses separately, to detect the global change
in the inhaled and exhaled gas [30]. The expired volume is usually
measured by a separate ow meter, or by a dilution technique using
a constant ow chamber to calculate the volume [30]. Both systems
use the Haldane transformation, i.e. the method to calculate
inspired gas volume by the ratio of the inspired and expired ni-
trogen concentrations, to calculate the inspired gas volume thus
simplifying the ow or volume measurements [Table 1][4,20,28].
Some commercially available simplied devices only measures
either VO
2
or the VCO
2
to calculate EE by assuming that the RQ is a
xed value (i.e. 0.8e0.85) [28,31,32]. While this type of assumption
may be acceptable in healthy subjects on balanced nutrition [28],itis
not recommended for patients because their substrate oxidation may
change signicantly according to the type of disease and nutrition.
Assuming a xed RQ in patients give inaccurate EE, in turnleading to
suboptimal energy prescription. Usingthe VCO
2
and RQ of prescribed
nutrition formulas (food quotient) to calculate EE has been proposed
as a way to improve the accuracy of the calculation for ICU patients
[32]. The analysis was conducted on stabilized patients who tolerated
more than two-thirds of the prescribed nutrition allowing the mean
EE bias of 7.7% (¼þ141 kcal/d) while improving the precision
compared to predictive equations.However, the accuracy level of this
method forindividual patientscan only be validated by conducting IC.
Thus, this method can be considered as an alternative for predictive
equations, but should not be considered as a valid alternative for IC in
the general ICU population.
2.3. The reference device of the 20th century
Numerous indirect calorimeters have been in and out of the
market in the past decades. However, the Deltatrac Metabolic
Monitor
®
(Datex, Finland) produced 35 years ago is often viewed as
the reference device [9,22e24,33]. This device features both canopy
and ventilator measurements [30]. When on ventilator mode, it
uses the mixing chamber technique with a unique constant ow
chamber to dilute the exhaled gas to enable calculations of VO
2
,
VCO
2
and EE without directly measuring the expired gas volume
[30]. The device has been repeatedly validated, including a com-
parison against mass spectrometry [30,34,35]. However, existing
units are progressively disappearing and the manufacturer no
longer offers any support.
2.4. Technology of modern indirect calorimeters
Calorimeters are designed to measure spontaneously breathing
patients or mechanically ventilated patients [28]. The different
techniques predetermine the limitations of their performances
[Table 4].
Devices with breath-by-breath technology can be made smaller
as they do not require a bulky mixing chamber. They generate rapid
readings by measuring short intervals of gas samples, a valuable
feature in case of exercise physiology or rapid shift in substrates
oxidation.
Devices with a mixing chamber generate more stable mea-
surements because the gases are physically averagedbefore being
analyzed, allowing the gas analyzers to generate very accurate
analysis. The mixing chamber typically occupies 3e5 L of space,
precluding the making of a small device. The capacity to make
reliable measurements in a short duration (e.g. 3e5 min) is also
limited, as it takes just as much time for the gas concentrations in
the mixing chamber to stabilize.
2.5. Accuracy and reproducibility
Three components of the hardware play a major role: the O
2
and
CO
2
analyzers, and the owmeter. Their accuracy, precision and
reproducibility are critical for IC and are inuenced by many factors
[Table 5]. For breath-by-breath systems, the reaction time of the gas
analyzers is important. The reliability of the software to synchro-
nize the signals from the gas analyzers and the expiratory ow-
meter to allow continuous calculations is a challenging demand.
Small errors in the alignment of the acquired data can lead to great
differences in the results. Mixing chamber devices are not as
technically demanding. However, the use of the Haldane trans-
formation formula introduces a mathematical limitation, especially
in case of O
2
enrichment higher than 60% as the inaccuracy of the
analyzers will be enhanced by the calculation [28,30,34].
Outside the calorimeter itself, the collection of inspired and
expired gases by an appropriate and airtight system is mandatory
[Fig. 2]. Avoiding leaks of inspired and expired gas is crucial, and
Table 2
Components of the energy expenditure in healthy subjects and diseased individuals
[1].
Components of
energy expenditure
Denition
Basal energy
expenditure (BEE)
Energy expended in fasting state,
resting in lying position at neutral
ambient temperature, free of physical
and psychological stress.
Note: Only applicable in healthy subjects.
Diet-induced
thermogenesis (DIT)
Oxidation of energy substrates during oral,
enteral or intravenous energy intake
Activity energy
expenditure (AEE)
Energy expenditure to support
physical activity
Resting energy
expenditure (REE)
BEE þDIT
Total energy
expenditure (TEE)
REE þAEE
Table 3
Required conditions for accurate measurement of energy expenditure in healthy subjects or diseased individuals [1,26,27].
Parameter Condition Subject
BEE At least 10 h after the previous meal
Free of drugs
Resting in supine position and free of physical stress
Awake and free of psychological stress
Normal body temperature
Ambient temperature in zone of neutrality (27e29
C)
Only healthy subjects
REE At least 5 h after the previous meal, or under continuous feeding
Minimum 2 h after alcohol and nicotine ingestion, 4 h after caffeine ingestion
After 30 min of resting period
Resting in supine position and free of physical stress
Awake and free of psychological stress
Comfortable environmental condition
Healthy subjects or patients
TEE No specic conditions Healthy subjects or patients
T. Oshima et al. / Clinical Nutrition xxx (2016) 1e12 3
Please cite this article in press as: Oshima T, et al., Indirect calorimetry in nutritional therapy. A position paper by the ICALIC study group, Clinical
Nutrition (2016), http://dx.doi.org/10.1016/j.clnu.2016.06.010
Fig. 1. Schematic presentation of indirect calorimetry used in patients on mechanical ventilation and on those breathing spontaneously. a) Breath by breath: Respiratory gas
composition and ow are measured continuously by connecting the gas analyzers to the ventilator circuit. The signals received by the gas analyzers and ow meters are syn-
chronized to calculate the oxygen consumption (VO
2
L/min) and CO
2
production (VCO
2
L/min) as the difference between the volumes of inhaled and exhaled O
2
and CO
2
per breath
by integral calculations. The Haldane transformation [Table 1] is used to calculate the inhaled gas volume from exhaled gas volume measurement. The Weir's equation [Table 1]is
used to calculate EE (kcal/d) per breath, and averaged for the duration of the measurement. The system is highly responsive to the dynamic changes of the EE, but prone to errors
due to the response time of the gas analyzers and software. b) Mixing chamber: The O
2
concentration of inhaled air (FiO
2
)isrst measured. Exhaled gas is collected into the mixing
chamber, where it is physically averaged and analyzed for O
2
(FeO
2
) and CO
2
(FeCO
2
) concentrations. The collected gas is eliminated through an independent chamber where the gas
ow (Q) is kept constant at 40e45 L/min, to dilute the exhaled gas from the mixing chamber with the ambient air. CO
2
in the diluted gas (FedCO
2
) is measured to calculate the CO
2
production (VCO
2
, L/min) by multiplying the concentration by the ow (VCO
2
¼FedCO
2
Q). An equation using the Haldane transformation allows the calculation of the respiratory
quotient (RQ) from the measured O
2
and CO
2
values (RQ ¼(1FiO
2
)/[(FiO
2
FeO
2
)/FeCO
2
FiO
2
]), and thus enables the calculation of the oxygen consumption (VO
2
, L/min;
VO
2
¼VCO
2
/RQ). This unique method used in the Deltatrac Metabolic Monitor
®
(Datex, Finland) enables VO
2
and VCO
2
measurements without measuring the ow of the exhaled
gas, which usually introduces technical difculties. c) Canopy: The canopy is used to measure EE in spontaneously breathing subjects. The subject is placed under a clear canopy
with a plastic drape to avoid air leakage. Calorimeters feature constant ow generator to create an outward ow through the canopy. The exhaled breath by the subject is diluted by
the constant ow Q (L/min), and collected by the calorimeter for gas analysis (FedO
2
, FedCO
2
), and enables calculations of VO
2
and VCO
2
[Table 1]. FiO
2
and FiCO
2
are either assumed
as ambient air values or measured, depending on the calorimeter. These values are used to calculate the EE using the Weir's equation. (1: ow analysis, 2: FiO
2
, 3: FiCO
2
, 4: FeO
2
,
5:FeCO
2
, 6:FedCO
2
, 7: FedO
2
; small arrows: respiratory gas ow, solid line: gas sampling, dotted line: signal for ow analysis, small arrows: respiratory gas ow, bold arrow:
constant ow).
Table 4
Technologies used in commercially available calorimeters.
Patient condition Patient application Technology Practical characteristics (advantages/disadvantages)
Spontaneous Breathing Canopy Constant ow dilution Patient discomfort minimum
Difcult to measure with O
2
supplementation
Facemask Breath by breath or mixing chamber Supports O
2
and mask ventilation
Patient discomfort, risk of leak
Mechanical Ventilation In-circuit Breath by breath Small device, fast response
Prone to error in calculation, dead space &
resistance of the measurement components
Gas collection Mixing chamber Stable measurements, validated in literature
Large devices, difcult to disinfect
T. Oshima et al. / Clinical Nutrition xxx (2016) 1e124
Please cite this article in press as: Oshima T, et al., Indirect calorimetry in nutritional therapy. A position paper by the ICALIC study group, Clinical
Nutrition (2016), http://dx.doi.org/10.1016/j.clnu.2016.06.010
becomes even more critical in case of O
2
enrichment. High quality
of calibration gas for calibration and periodic maintenance of the
calorimeters guarantee the technical performances [25].
2.6. Alternative methods to measure energy expenditure
EE can be measured by four different methods: 1) direct calo-
rimetry, 2) indirect calorimetry, 3) thermodilution (Fick method),
and 4)
2
H/
1
H and
18
O/
16
O doubly labeled water.
2.6.1. Direct calorimetry
Direct calorimetry is the direct measurement of heat production
in the body. This concept is based on the phenomenon that all
energy substrates, upon oxidation, produce heat. The subject needs
to be conned in an insulated chamber to measure the heat pro-
duction. The subjects also have to be able to maintain a complete
resting state during the measurement in order to avoid extra heat
production by physical activity. Thus the conditions are unrealistic
for clinical use, and the availability is limited to a few specialized
research centers.
2.6.2. Fick method
The Fick method requires a pulmonary artery catheter to mea-
sure the cardiac output, using the thermodilution method. Arterial
and mixed venous oxygen contents must also be measured. After
measuring the O
2
content in arterial and mixed venous blood from
the pulmonary artery, VO
2
can be calculated using the Fick equation
[Table 6]. The EE is calculated by assuming a xed RQ. Several
problems limit its use in clinical practice. First, only few patients
have pulmonary artery catheters and the insertion of the catheter
only for EE measurements would be too invasive. Second, the VO
2
calculated by this method is only a snapshot of the moment of the
measurement, while the error of the thermodilution method is
about 15% due to cardiac output variation over the respiratory cycle.
Furthermore, mixed venous oxygen concentration may be over-
estimated because of the shunting of arterial blood from bronchial
vessels, thus leading to underestimation of VO
2
and subsequently
the EE.
2.6.3. Doubly labeled water
Water containing non-radioactive isotope labeled hydrogen and
oxygen atoms (
2
H/
1
H and
18
O/
16
O) is given orally, after a baseline
evaluation of the body liquids; urine, saliva, and blood. The evalu-
ation of the body liquids is repeated after 7e12 days to calculate the
variations of concentrations of the isotopes over time. CO
2
pro-
duction can be calculated by observing the elimination rates of the
isotopes from the body liquids. EE can be calculated by assuming a
given RQ. The calculations are based on several assumptions such
as steady-state CO
2
and H
2
O turnover, and constant body water
pool size during the measurement period. These assumptions may
not be applicable for critically ill patients, as uid volume shifts
together with large changes in CO
2
production are frequently
observed [3]. The costs of the doubly labeled water and of mass
spectrometry measurements are very high. This method allows to
calculate EE, but the delay to obtain the results limits its use to
research [3].
In summary, these three methods are tooinvasive, cumbersome
or costly. IC remains the most practical method that is applicable in
patients with various characteristics.
3. Practical considerations
3.1. Indications and limitations
IC is a non-invasive technique [25] applicable to many patients
in order to individualize their nutrition therapy or for research
purpose. However, IC may require special considerations for the
interpretation of the results in a number of specic situations
[Table 7].
3.1.1. Patients
By denition, the most important condition is the absence of air
leak in the respiratory circuit. For example, patients with air-
leaking chest drainage cannot be studied by IC [27]. Mechanically
ventilated patients with high pressure settings on the ventilator are
prone to air leakage at the level of the endotracheal tube [36].ICin
patients with unstable conditions is less useful, as the measure-
ment will not represent their true metabolic characteristics. For
example, agitated patients or those with seizures or other invol-
untary movements are difcult to assess, as measurements will
include the EE related to the body movements, by nature incon-
stant, and therefore will not represent the true daily EE [27].A
patient should be in a resting condition or at least be able to keep
calm during most of the IC duration [26], for the results to be a true
representative value of the resting EE. Patients with unstable body
temperature, variable pH due to CO
2
accumulation or other causes
are also likely to present unreliable results, and measurements
should be repeated periodically or after stabilization.
3.1.2. Treatments
Mechanical ventilation with FiO
2
>60% is likely to generate
inaccurate measurements because of the Haldane transformation
[27,30]. Patients on organ support treatments that supply O
2
to the
blood or remove CO
2
from the blood (e.g. ECMO) and treatments
that alter acid-base homeostasis (e.g. renal replacement therapy
and liver support therapies such as Molecular Adsorbent Recircu-
lating System (MARS)) also need special consideration.
Currently available IC devices do not provide valid solutions for
these special conditions. However, the improvements of organ
support therapies have enabled their frequent application in longer
durations for patients under extremely severe conditions or wait-
ing organ transplantation. Thus, technical solutions to conduct IC
accurately in these patients are mandatory. De Waele et al. sug-
gested a method for conducting IC in ECMO patients [37]. Devel-
opment of commercial calorimeters designed for use with various
treatment conditions will contribute to the improved nutrition
therapy in these patients.
Table 5
Source of errors for indirect calorimetry depending on the measurement technology.
Technology Specic factors Common factors
Canopy Leak of the gas collection
Reliability of the constant ow
Accuracy of O
2
,CO
2
, and ow analyzers
Haldane transformation introduces high variability when FiO
2
>60%
Adequate maintenance and calibrationBreath-by-breath Response speed of gas analyzers
Accuracy of data synchronization by the software
Mixing chamber Leak of the gas collection
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3.2. Practical recommendations of clinical use
IC is successful when an appropriate device is used in optimal
conditions, and the results are analyzed by experienced pro-
fessionals in order to individualize the nutrition care. Although,
these conditions are not easily met, practical recommendations are
proposed below according to patient characteristics. Table 8 sum-
marizes the important checkpoints for a successful routine use of
IC. It can easily be adapted to create protocols after adjustment for
the local medical practices.
3.3. Calorimetry: important considerations
IC is the only easy-to-use, non-invasive method to measure the
EE of healthy active or inactive subjects, or of patients with various
levels of metabolic stress in order to obtain immediate results
[3,38]. Nevertheless, the lack of sufcient knowledge to interpret
the results generated by IC may lead to erroneous prescription. The
conditions of IC measurement are of paramount importance. The
general statement is: the more stable the clinical situation, the
more reliable the IC results. Whenever a situation is changing, IC
should be repeated. For instance, IC obtained during the early phase
of a critical illness should be repeated within the next 24e48 h to
obtain a result reecting the dynamic evolution of the disease
[Fig. 3].
4. Developments of indirect calorimetry
4.1. The global initiative to promote calorimetry
Commercially available calorimeters are usually of large size
and heavy weight, need time-consuming warm-up and calibra-
tion before measurement, require PCs to record and analyze re-
sults, require cumbersome disinfection of the device and
repeated-use components after measurements, and are sold at
relatively high costs [27,28]. The best way to promote IC is to make
Fig. 2. Sources of air leaks during indirect calorimetry in spontaneously breathing patients and on those on mechanical ventilation. The avoidance of respiratory gas leaks is crucial
to the accuracy of the energy expenditure measurement. 1: Tube connections with gas collection devices and calorimeters must be air-tight. 2: For patients on mechanical
ventilation, leaks from the cuff of the endotracheal tube must be detected, as they can be signicant in cases of high airway pressure. 3: Pathologies (e.g. bronchial stula) and
treatments (e.g. chest drain) causing air leaks from the lung must be detected. 4: Canopy and drape must be inspected for cracks and tears, and tting tightly to each other. The
drape should fully cover the surroundings of the canopy to avoid leaks.
Table 6
The Fick method (thermodilution) and related equations.
Calculation of O
2
content in blood
CaO
2
¼(Hb) 1.38
#
SaO
2
þ(0.003 PaO
2
)
CvO
2
¼(Hb) 1.38
#
SvO2 þ(0.003 PvO
2
)
#: O
2
carrying capacity of Hb (1.34e1.39/gram,
depending on literature)
Fick equation
VO
2
¼(CaO
2
CvO2) CO 10 or
VO
2
¼1.38 (Hb) (CO) (SaO
2
SvO
2
)/10
Ca(v)O
2
: content of O
2
in arterial (venous) blood, Sa(v)O
2
:O
2
saturation of arterial
(venous) blood, Pa(v)O
2
: partial pressure of O
2
in arterial (venous) blood, CO: cardiac
output (L/min).
Table 7
Clinical situations requiring careful interpretation of energy expenditure measured
by indirect calorimetry [26,27].
Physical agitation or unstable sedation and/or analgesia
Air leaks (>10% of minute volume)
Unstable body temperature (1
C change over last 1 h)
Unstable pH (0.1 change over last 1 h)
Oxygen enrichment (FiO
2
>60%)
Organ support therapies: renal replacement or liver support therapy (pH
alterations when conducted intermittently), ECMO (direct O
2
supply to the
blood and CO
2
removal from the blood)
FiO
2
: fraction of inspired oxygen, ECMO: extracorporeal membrane oxygenation.
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available an easy-to-use, accurate and affordable device for daily
use in in- and out-patients. The requirements of an ideal calo-
rimeter have been dened using a bottom-up process [Table 9]. As
current calorimeters were unable to meet the requirements
dened by physicians, the International Multicentric Study Group
for Indirect Calorimetry (ICALIC) was formed to develop and test
an optimal device with the nancial support of two international
academic societies (The European Society of Intensive Care
Medicine (ESICM) and The European Society for Clinical Nutrition
and Metabolism (ESPEN)).
The new calorimeter has been validated against the gold stan-
dard technology for gas composition measurements, i.e. mass
spectrometer (MAX300-LG, Extrel, Pittsburgh, USA). The mass
spectrometer was specially tuned for breath gas analysis, to be able
to measure O
2
and CO
2
with resolution up to 10 ppm and accuracy
of ±0.0025%. The analyzers for O
2
and CO
2
were evaluated for their
accuracy in static concentration measurements using precision gas
mixtures to simulate various clinically relevant O
2
(16%e21% for
canopy measurements, and up to 70% for ventilator measurements)
and CO
2
(0.3e5.0%) concentrations. Accuracy and response to dy-
namic concentration changes were evaluated in in-vivo evaluation
in volunteers, by direct comparison of measured O
2
and CO
2
con-
centrations. The overall performance of the calorimeterconsisting
of the newly developed dynamic mixing chamber was tested by the
Table 8
Checkpoints for successful indirect calorimetry.
Mechanically ventilated Spontaneously breathing
Planning measurement
1. Frequency Conduct calorimetry within 3e4 days after admission
Repeat calorimetry every 2e3 days during the ICU stay
Repeat calorimetry in case of changes in
patient or disease conditions
Unsuitable conditions
2. Respiration FiO
2
>60%
PEEP >10 cm H
2
O
Peak airway pressure >30 cm H
2
O
O
2
enrichment:
Difcult with canopy
Possible with leak-tight O
2
mask using breath by breath device
3. Agitation Unstable sedation and/or analgesia Intolerance for canopy and/or facemask
Uncontrolled seizure and/or involuntary movements
4. Treatments Air leaks from ventilator circuit and/or
endotracheal tube cuff
Air leaks from chest drains
Special consideration: Renal replacement therapy,
liver support therapy, ECMO
5. Immediate changes
(<60 min before IC)
1
C change of body temperature
Change of drug dose: catecholamine,
sedatives, analgesics, etc.
Invasive procedures, mobilization, physical exercise
Before measurement
6. Device Warm up and calibration (as required)
Secure connections of tubes and components
Search for any air leaks
7. Feeding status Continuous feeding preferred Fasting preferred for out-patients (>8 h before IC)
If fed, record: energy prescription and intake,
duration (hrs) since last meal
8. Environment Record: ventilation setting Adjust canopy ventilation to maintain FeCO
2
0.8e1.2%
Maintain room temperature at 20e25
C
Ensure comfortable body position
During and after measurement
9. Quality of Measurement Duration: 30 min or until stable state
(calculated CV
a
<5% for VO
2
and VCO
2
for >5 min,
CV of <10% for 25 min)
RQ: <0.7 and >1.0 may suggest inadequate measurement
Record:
- agitation and body movements
- any events affecting breathing pattern
- changes in vasoactive drugs
10. Disinfection Disinfect device and components in contact with patients
Discard single use components
a
Coefcient of variation.
Fig. 3. Evolution of energy expenditure in critically ill patients. The evolution of energy
expenditure (EE) of critically ill patients is dynamic according to the phase and the
severity of the illness, treatment and extended bed rest. An illustration of such evo-
lution in a septic patient with relapsed of the disease is presented. Dynamic change of
EE during the early phase, and the difference in the evolution of EE during the rst
onset and the relapse due to factors such as bed rest and immobilization in the late
phases are impossible to estimate accurately by predictive equations [11]. (Circle: EE by
indirect calorimetry (kcal/d); triangle: EE by Faisy's equation; grey line (kcal/d): pre-
scribed energy (kcal/d); dotted line: delivered energy).
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conventional mixing chamber method, using the mass spectrom-
eter as the gas analyzer. The practical characteristics will be eval-
uated in a multicenter study which will start during the 1st
semester of 2016, to ensure that the device is easy to use and ts the
conditions found in various clinical settings.
Market release of the device is anticipated for 2017. Training
courses are organized by ESPEN as part of the Life Long Learning
(LLL) Courses and will be multiplied to allow optimal use of IC.
5. Why you should use indirect calorimetry
5.1. Rationale for measuring energy expenditure by indirect
calorimetry
Energy expenditure of a patient is massively inuenced by a
number of intrinsic and extrinsic factors [Table 10][19]. These
factors have synergic or antagonist impact on the EE level and the
estimation of the EE using a predictive equation based on anthro-
pometric characteristics (i.e. body weight, height, gender, and age)
is frequently inaccurate [3,20,39]. The use of multiplicative factor
usually called stress factorhas been proven to further deteriorate
the estimation of EE based on equations. For example, obese pa-
tients present signicant EE variations due to their underlying ill-
nesses, variable body composition and degree of malnutrition [40].
Patients with chronic obstructive pulmonary disease or those with
cancer have an elevated EE, which can be easily underestimated by
predictive formula [41e43]. Critically ill patients with trauma or
sepsis have dynamic changes of their EE during the successive
phases of their critical illness [3,44e47]. Although much effort has
been made to create predictive equations adapted to the clinical
evolution of acute illness [48], IC remains the gold standard to
measure EE [3,19]. The full benets of nutrition support may be
expected only if the patient specicEEisreected in the nutrition
prescription, according to the changes that occur during the course
of the illness.
5.2. Is measured EE always reecting the energy needs?
This critical question has been asked many times and is
frequently investigated, but the answer remains controversial. In
general, the measured EE denes the energy target for the pre-
scription of nutrition. However, during the early phase of an acute
illness, endogenous energy supply covers most of the energy needs,
a condition that is marginally affected by exogenous energy sup-
plementation [Fig. 4][49,50]. The energy administered may then
massively exceed the requirements and generate relative over-
feeding [3], a condition associated with deleterious consequences
[Table 11] and poor outcome. This transitory period generally ends
as soon the patient's overall condition improves. However, the
value of IC measurements to evaluate the evolution of endogenous
energy production needs further investigation. Therefore, careful
interpretation of EE by IC in this phase is necessary for the adequate
prescription of energy to avoid overfeeding. However, excessive
restriction of energy will result in underfeeding, which has been
associated with progressive loss of lean body mass [51], leading to
poor outcomes. It should also be noted that predictive equations
will not be able to take into account this type of metabolic alter-
ation, and the degree of error in the estimation of EE will be
unpredictable.
5.3. Respiratory quotient: another advantage of indirect
calorimetry
IC allows for non-invasive measurement of EE in spontaneously
breathing patients or those on mechanical ventilation, with or
without O
2
enrichment [9,22,25,28]. An advantage of IC over other
methods to measure EE is the capacity to derive the respiratory
quotient (RQ) from direct measurements. The RQ corresponds to
the quotient of VCO
2
andVO
2
(RQ ¼VCO
2
/VO
2
)[25,28], which en-
ables the calculations of the substrate oxidation rates for glucose
and lipids. This would especially allow detecting net lipogenesis.
For patients with chronic illnesses, EE reects the energy needs
while the RQ reects the composition of oxidized substrates [5].
This information is helpful to tailor the prescription of the nutrition
regimen [28] by observing the match between the energy intake
and the food quotient, i.e. the RQ of the energy substrates according
to their food composition.
For critically ill patients, it allows to visualize the metabolic al-
terations, especially during the early phase. IC measurements
should be repeated to monitor the dynamic changes, and to opti-
mize the prescription of energy [3].
5.4. Routine use of indirect calorimetry
IC is rarely routinely used in medical institutions across the
world [7,25] in spite of its value for a wide range of patients. Such a
limited use of IC is mainly due to the unavailability of calorimeters,
the insufcient awareness about the impact of optimal nutrition
support on the patients outcome [20], the lack of expertise for
interpretation of results, costs of device and related-manpower.
This section aims at clarifying these issues.
5.5. Critical illness
Patients in the ICU for >4 days or those after major surgery are
good candidates for IC as they undergo severe stress related to
variable metabolic needs [52]. Indeed, these patients are at high
nutritional risk, as they are unable to resume sufcient oral intake
instantly and often require enteral or parenteral nutrition [7,53,54].
Studies in critically ill patients have repeatedly reported gross un-
derfeeding during the ICU stay [55]. Various factors such as gut
Table 9
Characteristics of the new indirect calorimeter dened by a bottom-up process of
development.
Characteristics Description
Accuracy
Gas analyzers ±0.02% for O
2
and CO
2
(after calibration)
Flow analyzer ±2% (after calibration)
Ease-of-use
Portable <2 kg, maximum foot print:15e30 cm
Interface Intuitive software, user manual not required
Calibration Gas analyzer: Automatic periodic calibration
against room air (no calibration gas required)
Flow analyzer: Automatic
Measurement
Duration <10 min for standard measurement
Recording Local memory buffer
Various exportation formats (Excel, CSV, etc)
Connectivity Wireless or USB
Battery operated Up to 10 measurements (duration 20 min),
4hrs (continuous measurement)
Safety
Approval EC certication
Disinfection Device covered by easy to clean material
Single use components for patient contact
(sampling tube, ow meter)
Compatible Hospital devices
Availability
Cost <10000 US $
Market Worldwide
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intolerance and treatment interventions delay full enteral feeding,
resulting in insufcient energy provision. Underfeeding is closely
associated with higher complication rates and poor outcomes
[13,17,56e59]. Overfeeding has also been repeatedly associated
with poor outcome and results often from the use of predictive
equations [60]. Recent evidence points out the importance of
optimal nutrition starting within 3e4 days after ICU admission
[16,18,20,61e63], at a time when predictive equations are exceed-
ingly unreliable due to the variable responses of individual patients
to the critical illness [8]. In other words, optimal nutrition promotes
better clinical outcome and IC is necessary to tailor the prescription
to the real needs of the patient [62e64].
The course of EE of severely ill patients features dynamic
changes as a consequence of stress, prolonged bed rest, atrophy of
the metabolically active lean tissue mass (i.e. 300e600 g of tissue/
day), medications (catecholamine, sedatives, neuromuscular
blocking agents, etc) [44,65e68], and modied by mechanical or-
gan support therapies such as mechanical ventilation, renal
replacement and liver support therapies. Thus, IC should be
repeated as the clinical condition changes to accurately dene the
energy target [16,63].
The obese patients constitute an increasing proportion of the
ICU patient population. Their energy requirements are particularly
poorly addressed by predictive equations [19]. IC is the only way to
determine their metabolic requirements accurately.
In summary, it is recommended to perform IC on days 3 or 4
after ICU admission, major surgery or trauma in order to set the
energy target [Fig. 5].
5.6. In patients and outpatients with chronic conditions
Patients with chronic conditions are good candidates for IC,
although their changes of EE are not as dynamic as in ICU or surgical
patients. Indeed, chronic diseases or treatments modify the meta-
bolically active lean body mass and the level of daily physical ac-
tivity, which in turn signicantly alter the energy needs and
challenge the estimation of EE by predictive equations. Typically,
important modications of the body composition or of the physical
activity deeply inuence EE. Table 12 shows the most common
pathologies with important EE alterations.
IC is necessary to conrm the energy expenditure and optimize
the recommendation for food intake or the prescription of nutrition
support. Repetition of IC should be considered according to the
appearance of substantial modication of the patient status. Con-
ducting IC together with the measurement of the body weight and
the body composition is useful to further optimize the nutrition
prescription by observing the effect of energy intake on these pa-
rameters [25,74].
Table 10
Factors inuencing energy expenditure.
Age, sex, body height, body mass,
body temperature Brain activity, endocrine prole,
systemic inammation
Muscle contractions or paralysis, physical activity
Fasting or post-absorptive state
Environmental temperature
Drugs (e.g. alpha adrenergic stimulant, beta-blockers, sedatives, muscle
relaxants)
Fig. 4. Conceptual presentation of the relative overfeeding frequently related to
parenteral nutrition during the early phase of critical illness. During the acute phase of
the critical illness, the release of endogenous energy substrates is increased and meets
total energy expenditure (TEE), and administering energy does not immediately
terminate this response. Introducing full feeding in this early phase usually results in
overfeeding, as the endogenous energy production is not attenuated by energy
administration thus creates an excessive energy source above TEE. (Solid bold line:
Total energy expenditure; grey bold line: adapted endogenous energy production;
dotted bold line: early energy administration; thin line: combined endogenous and
exogenous energy administration).
Table 11
Effects of overfeeding and underfeeding.
Insufcient energy intake Excessive energy intake
Early signs Hypoglycemia
Hypothermia
Hyperglycemia
Hyperlipidemia (triglycerides)
Hypercapnea
Delayed signs Infectious complications
Impaired immunity
Impaired healing
Loss of lean and fat body mass
Impaired muscle function
Infectious complications
Impaired immunity
Liver steatosis
Increased fat mass
Fig. 5. Conceptual presentation of optimal feeding strategy to avoid both overfeeding
and underfeeding in critical illness: Introducing the adequate amount of feeding in
proportion to the body's capacity to down-regulate endogenous substrate production
avoids both early overfeeding and late underfeeding. Repeated calorimetry is needed
to monitor the dynamic changes of energy expenditure, however, providing the
optimal amount of energy still requires special attention to avoid both underfeeding
and overfeeding. (Solid bold line: Total energy expenditure; grey bold line: adapted
endogenous energy production; dotted bold line: energy administration by EN; grey
dotted bold line: energy administration by PN; thin line: combined endogenous and
exogenous energy administration).
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5.7. Impact of IC on patient care and hospital economy
Malnutrition is associated with increased morbidity, length of
stay and costs [75]. Oral nutritive supplements, and enteral and
parenteral nutrition are related with improved outcome, but both
underfeeding and overfeeding have been shown to mitigate the
impact of nutrition support [63,76,77]. The prescription of nutrition
therapy aims at matching the energy target as dened by predictive
formulas. Unfortunately, these formulas are often inaccurate.
Therefore, we hypothesize that promoting a large-scale use of IC to
measure EE of in- and outpatients should optimize nutrition care,
clinical outcome and costs.
6. Conclusion
Calorimetry is needed to optimize nutrition care for patients
with various clinical conditions. The use of calorimetry is currently
limited by various setbacks, mainly related to the lack of an
adequate device. An ongoing initiative to develop a new calorim-
eter is expected to provide practical solutions for the current lim-
itations, and make available a calorimeter corresponding to the
requirements by clinicians for in- and outpatients, featuring accu-
racy, ease-of-use and affordable cost. Online and live educational
courses will further mount the optimal use of calorimetry.
Conict of interest statement
All authors have declared that they have no conict of interest
related to this project.
Statement of authorship
Taku Oshima and Claude Pichard have outlined this manuscript,
which was developed, enriched, reviewed and approved by each of
the co-authors.
Funding sources, and acknowledgments
Financial support came from the European Society of Clinical
Nutrition and Metabolism (ESPEN) and the European Society of
Intensive Care Medicine (ESCIM), APSI-ICU quality funds of the
Geneva University Hospital, Public Foundation Nutrition 2000Plus.
References
[1] Bursztein SED, Askanazi JA, Kinney JM. Energy metabolism, indirect calorim-
etry, and nutrition. Baltimore, Maryland, USA: Williams and Wilkins; 1989.
[2] Weir JB. New methods for calculating metabolic rate with special reference to
protein metabolism. J Physiol 1949;109:1e9.
[3] Fraipont V, Preiser JC. Energy estimation and measurement in critically ill
patients. J Parenter Enter Nutr 2013;37:705e13.
[4] Wilmore JH, Costill DL. Adequacy of the Haldane transformation in the
computation of exercise VO2 in man. J Appl Physiol 1973;35:85e9.
[5] Frayn KN. Calculation of substrate oxidation rates in vivo from gaseous ex-
change. J Appl Physiol Respir Environ Exerc Physiol 1983;55:628e34.
[6] Kyle UG, Arriaza A, Esposito M, Coss-Bu JA. Is indirect calorimetry a necessity
or a luxury in the pediatric intensive care unit? J Parenter Enter Nutr 2012;36:
177e82.
[7] De Waele E, Spapen H, Honore PM, Mattens S, Van Gorp V, Diltoer M, et al.
Introducing a new generation indirect calorimeter for estimating energy re-
quirements in adult intensive care unit patients: feasibility, practical consid-
erations, and comparison with a mathematical equation. J Crit Care
2013;28(884):e881e886.
[8] McClave SA, Lowen CC, Kleber MJ, Nicholson JF, Jimmerson SC, McConnell JW,
et al. Are patients fed appropriately according to their caloric requirements?
J Parenter Enter Nutr 1998;22:375e81.
[9] Graf S, Karsegard VL, Viatte V, Heidegger CP, Fleury Y, Pichard C, et al. Eval-
uation of three indirect calorimetry devices in mechanically ventilated pa-
tients: which device compares best with the Deltatrac II((R))? A prospective
observational study. Clin Nutr 2015;34:60e5.
[10] Preiser JC, van Zanten AR, Berger MM, Biolo G, Casaer MP, Doig GS, et al.
Metabolic and nutritional support of critically ill patients: consensus and
controversies. Crit Care 2015;19:35.
[11] Faisy C, Guerot E, Diehl JL, Labrousse J, Fagon JY. Assessment of resting energy
expenditure in mechanically ventilated patients. Am J Clin Nutr 2003;78:
241e9.
[12] Wang Z, Heshka S, Gallagher D, Boozer CN, Kotler DP, Heymseld SB. Resting
energy expenditure-fat-free mass relationship: new insights provided by
body composition modeling. Am J Physiol Endocrinol Metab 2000;279:
E539e45.
Table 12
Common chronic pathologies and treatments with important alterations of energy expenditure.
Conditions Effects on energy expenditure
Respiratory diseases
COPD [Increased respiratory efforts [69]
Cystic brosis [[70]
Metabolic diseases
Adrenal gland disease [or [Y Increased release of catecholamine [71]
Unpredictable change after surgical treatment
Thyroid diseases [or YAltered release of thyroxine [72]
Muscle tone alteration
Neuromuscular degenerative diseases YDegeneration and disuse of muscle tissue
Paralysis YDisuse and atrophy of paralyzed body area
Seizure, involuntary movements [Increased muscle activity [73]
Cachexic conditions
Cancer [or YCancer growth and inammation
Progressive reduction of lean body mass
AIDS [or YChronic infection and inammation
Progressive cachexia
Cardiomyopathy YProgressive reduction of lean body mass
Malnutrition
Obesity [or YIncreased lean body mass, unless obesity is
associated with sarcopenia
Anorexia YLow energy intake and reduced lean body mass
Organ support therapies
Hemodialysis/peritoneal dialysis [or YChronic inammation
Progressive reduction of lean body mass
Continuous positive airway pressure (CPAP) [or YIncreased respiratory efforts, modied by mechanical support
T. Oshima et al. / Clinical Nutrition xxx (2016) 1e1210
Please cite this article in press as: Oshima T, et al., Indirect calorimetry in nutritional therapy. A position paper by the ICALIC study group, Clinical
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[13] Faisy C, Candela Llerena M, Savalle M, Mainardi JL, Fagon JY. Early ICU energy
decit is a risk factor for Staphylococcus aureus ventilator-associated pneu-
monia. Chest 2011;140:1254e60.
[14] Ekpe K, Novara A, Mainardi JL, Fagon JY, Faisy C. Methicillin-resistant Staph-
ylococcus aureus bloodstream infections are associated with a higher energy
decit than other ICU-acquired bacteremia. Intensive Care Med 2014;40:
1878e87.
[15] 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;365:506e17.
[16] Singer P, Anbar R, Cohen J, Shapiro H, Shalita-Chesner 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;37:601e9.
[17] Petros S, Horbach M, Seidel F, Weidhase L. Hypocaloric vs normocaloric
nutrition in critically ill patients: a prospective randomized pilot trial.
J Parenter Enter Nutr 2014;40:242e9.
[18] Wei X, Day AG, Ouellette-Kuntz H, Heyland DK. The association between
nutritional adequacy and long-term outcomes in critically ill patients
requiring prolonged mechanical ventilation: a multicenter cohort study. Crit
Care Med 2015;43:1569e79.
[19] Frankeneld DC, Ashcraft CM. Estimating energy needs in nutrition support
patients. J Parenter Enter Nutr 2011;35:563e70.
[20] Guttormsen AB, Pichard C. Determining energy requirements in the ICU. Curr
Opin Clin Nutr Metab Care 2014;17:171e6.
[21] Sundstrom M, Tjader I, Rooyackers O, Wernerman J. Indirect calorimetry in
mechanically ventilated patients. A systematic comparison of three in-
struments. Clin Nutr 2013;32:118e21.
[22] Graf S, Karsegard VL, Viatte V, Maisonneuve N, Pichard C, Genton L. Com-
parison of three indirect calorimetry devices and three methods of gas
collection: a prospective observational study. Clin Nutr 2013;32:1067e72.
[23] Black C, Grocott MP, Singer M. Metabolic monitoring in the intensive care
unit: a comparison of the Medgraphics Ultima, Deltatrac II, and Douglas bag
collection methods. Br J Anaesth 2014;114:261e8.
[24] Sundstr
om M, Fiskaare E, Tj
ader I, Norberg Å, Rooyackers O, Wernerman J.
Measuring Energy Expenditure in the Intensive Care Unit: a comparison of
indirect calorimetry by E-sCOVX and Quark RMR with Deltatrac II in me-
chanically ventilated critically ill patients. Crit Care 2016. http://dx.doi.org/
10.1186/s13054-016-1232-6.
[25] Psota T, Chen KY. Measuring energy expenditure in clinical populations: re-
wards and challenges. Eur J Clin Nutr 2013;67:436e42.
[26] Compher C, Frankeneld D, Keim N, Roth-Yousey L. Best practice methods to
apply to measurement of resting metabolic rate in adults: a systematic re-
view. J Am Diet Assoc 2006;106:881e903.
[27] AARC. AARC Clinical Practice Guideline. Metabolic measurement using indi-
rect calorimetry during mechanical ventilation.2004 Revision &Update.
Respir Care 2004;49:1073e9.
[28] Haugen HA, Chan LN, Li F. Indirect calorimetry: a practical guide for clinicians.
Nutr Clin Pract 2007;22:377e88.
[29] Mansell PI, Macdonald IA. Reappraisal of the Weir equation for calculation of
metabolic rate. Am J Physiol 1990;258:R1347e54.
[30] Takala J, Keinanen O, Vaisanen P, Kari A. Measurement of gas exchange in
intensive care: laboratory and clinical validation of a new device. Crit Care
Med 1989;17:1041e7.
[31] Zhao D, Xian X, Terrera M, Krishnan R, Miller D, Bridgeman D, et al. A pocket-
sized metabolic analyzer for assessment of resting energy expenditure. Clin
Nutr 2014;33:341e7.
[32] Stapel SN, de Grooth HJ, Alimohamad H, Elbers PW, Girbes AR, Weijs PJ, et al.
Ventilator-derived carbon dioxide production to assess energy expenditure in
critically ill patients: proof of concept. Crit Care 2015;19:370.
[33] Sandstrom R, Drott C, Hyltander A, Arfvidsson B, Schersten T, Wickstrom I,
et al. The effect of postoperative intravenous feeding (TPN) on outcome
following major surgery evaluated in a randomized study. Ann Surg
1993;217:185e95.
[34] Phang PT, Rich T, Ronco J. A validation and comparison study of two metabolic
monitors. J Parenter Enter Nutr 1990;14:259e61.
[35] Tissot S, Delafosse B, Bertrand O, Bouffard Y, Viale JP, Annat G. Clinical vali-
dation of the Deltatrac monitoring system in mechanically ventilated patients.
Intensive Care Med 1995;21:149e53.
[36] El-Orbany M, Salem MR. Endotracheal tube cuff leaks: causes, consequences,
and management. Anesth Analg 2011;117:428e34.
[37] De Waele E, van Zwam K, Mattens S, Staessens K, Diltoer M, Honore PM, et al.
Measuring resting energy expenditure during extracorporeal membrane
oxygenation: preliminary clinical experience with a proposed theoretical
model. Acta Anaesthesiol Scand 2015;59:1296e302.
[38] De Waele E, Spapen H, Honore PM, Mattens S, Rose T, Huyghens L. Bedside
calculation of energy expenditure does not guarantee adequate caloric pre-
scription in long-term mechanically ventilated critically ill patients: a quality
control study. ScienticWorldJournal 2012;2012:909564.
[39] Frankeneld DC, Coleman A, Alam S, Cooney RN. Analysis of estimation
methods for resting metabolic rate in critically ill adults. J Parenter Enter Nutr
2009;33:27e36.
[40] Mogensen KM, Andrew BY, Corona JC, Robinson MK. Validation of the society
of critical care medicine and American society for parenteral and enteral
nutrition recommendations for caloric provision to critically ill obese patients:
a pilot study. J Parenter Enter Nutr 2015;40:713e21.
[41] Ramires BR, de Oliveira EP, Pimentel GD, McLellan KC, Nakato DM,
Faganello MM, et al. Resting energy expenditure and carbohydrate oxidation
are higher in elderly patients with COPD: a case control study. Nutr J 2011;11:
37.
[42] Lieffers JR, Mourtzakis M, Hall KD, McCargar LJ, Prado CM, Baracos VE.
A viscerally driven cachexia syndrome in patients with advanced colorectal
cancer: contributions of organ and tumor mass to whole-body energy de-
mands. Am J Clin Nutr 2009;89:1173e9.
[43] Fearon K, Strasser F, Anker SD, Bosaeus I, Bruera E, Fainsinger RL, et al. De-
nition and classication of cancer cachexia: an international consensus. Lancet
Oncol 2011;12:489e95.
[44] Puthucheary ZA, Rawal J, McPhail M, Connolly B, Ratnayake G, Chan P,
et al. Acute skeletal muscle wasting in critical illness. Jama 2013;310:
1591e600.
[45] Hartl WH, Jauch KW. Metabolic self-destruction in critically ill patients: ori-
gins, mechanisms and therapeutic principles. Nutrition 2014;30:261e7.
[46] Cerra FB, Siegel JH, Coleman B, Border JR, McMenamy RR. Septic autocanni-
balism. A failure of exogenous nutritional support. Ann Surg 1980;192:
570e80.
[47] Preiser JC, Ichai C, Orban JC, Groeneveld AB. Metabolic response to the stress
of critical illness. Br J Anaesth 2014;113:945e54.
[48] Savard JF, Faisy C, Lerolle N, Guerot E, Diehl JL, Fagon JY. Validation of a
predictive method for an accurate assessment of resting energy expenditure
in medical mechanically ventilated patients. Crit Care Med 2008;36:1175e83.
[49] Wolfe RR. Sepsis as a modulator of adaptation to low and high carbohydrate
and low and high fat intakes. Eur J Clin Nutr 1999;53(Suppl. 1):S136e42.
[50] Rennie MJ. Anabolic resistance in critically ill patients. Crit Care Med 2009;37:
S398e9.
[51] Hoshino E, Pichard C, Greenwood CE, Kuo GC, Cameron RG, Kurian R, et al.
Body composition and metabolic rate in rat during a continuous infusion of
cachectin. Am J Physiol 1991;260:E27e36.
[52] Finnerty CC, Mabvuure NT, Ali A, Kozar RA, Herndon DN. The surgically
induced stress response. J Parenter Enter Nutr 2013;37:21Se9S.
[53] Heyland DK, Dhaliwal R, Lemieux M, Wang M, Day AG. Implementing the PEP
uP protocol in critical care units in Canada: results of a multicenter, quality
improvement study. J Parenter Enter Nutr 2014;39:698e706.
[54] Hiesmayr M. Nutrition risk assessment in the ICU. Curr Opin Clin Nutr Metab
Care 2012;15:174e80.
[55] Alberda C, Gramlich L, Jones N, Jeejeebhoy K, Day AG, Dhaliwal R, et al. The
relationship between nutritional intake and clinical outcomes in critically ill
patients: results of an international multicenter observational study. Intensive
Care Med 2009;35:1728e37.
[56] Thibault R, Graf S, Clerc A, Delieuvin N, Heidegger CP, Pichard C. Diarrhoea in
the ICU: respective contribution of feeding and antibiotics. Crit Care 2013;17:
R153.
[57] Heidegger CP, Romand JA, Treggiari MM, Pichard C. Is it now time to promote
mixed enteral and parenteral nutrition for the critically ill patient? Intensive
Care Med 2007;33:963e9.
[58] 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;24:502e9.
[59] Dvir D, Cohen J, Singer P. Computerized energy balance and complications in
critically ill patients: an observational study. Clin Nutr 2006;25:37e44.
[60] Berger MM, Pichard C. Development and current use of parenteral nutrition in
critical care - an opinion paper. Crit Care 2014;18:478.
[61] Oshima T, Pichard C. Parenteral nutrition: never say never. Crit Care
2015;19(Suppl. 3):S5.
[62] Weijs P, Looijaard W, Beishuizen A, Girbes A, Oudemans-van Straaten HM.
Early high protein intake is associated with low mortality and energy over-
feeding with high mortality in non-septic mechanically ventilated critically ill
patients. Crit Care 2014;18:701.
[63] Heidegger CP, Berger MM, Graf S, Zingg W, Darmon P, Costanza MC, et al.
Optimisation of energy provision with supplemental parenteral nutrition in
critically ill patients: a randomised controlled clinical trial. Lancet 2013;381:
385e93.
[64] Singer P, Hiesmayr M, Biolo G, Felbinger TW, Berger MM, Goeters C, et al.
Pragmatic approach to nutrition in the ICU: expert opinion regarding which
calorie protein target. Clin Nutr 2014;33:246e51.
[65] Grosu HB, Lee YI, Lee J, Eden E, Eikermann M, Rose KM. Diaphragm muscle
thinning in patients who are mechanically ventilated. Chest 2012;142:
1455e60.
[66] English KL, Paddon-Jones D. Protecting muscle mass and function in older
adults during bed rest. Curr Opin Clin Nutr Metab Care 2010;13:34e9.
[67] Honore PM, De Waele E, Jacobs R, Mattens S, Rose T, Joannes-Boyau O, et al.
Nutritional and metabolic alterations during continuous renal replacement
therapy. Blood Purif 2013;35:279e84.
[68] Biolo G, Ciocchi B, Lebenstedt M, Barazzoni R, Zanetti M, Platen P, et al. Short-
term bed rest impairs amino acid-induced protein anabolism in humans.
J Physiol 2004;558:381e8.
[69] Rabinovich RA, Louvaris Z, Raste Y, Langer D, Van Remoortel H, Giavedoni S,
et al. Validity of physical activity monitors during daily life in patients with
COPD. Eur Respir J 2013;42:1205e15.
T. Oshima et al. / Clinical Nutrition xxx (2016) 1e12 11
Please cite this article in press as: Oshima T, et al., Indirect calorimetry in nutritional therapy. A position paper by the ICALIC study group, Clinical
Nutrition (2016), http://dx.doi.org/10.1016/j.clnu.2016.06.010
[70] Frankeneld DC, Ashcraft CM, Drasher TL, Reid EK, Vender RL. Characteristics
of resting metabolic rate in critically ill, mechanically ventilated adults with
cystic brosis. J Parenter Enter Nutr 2015. http://dx.doi.org/10.1177/
0148607115617152.
[71] Xu WP, Cao DX, Lin ZM, Wu GH, Chen L, Zhang JP, et al. Analysis of energy
utilization and body composition in kidney, bladder, and adrenal cancer pa-
tients. Urol Oncol 2012;30:711e8.
[72] Spadafranca A, Cappelletti C, Leone A, Vignati L, Battezzati A, Bedogni G, et al.
Relationship between thyroid hormones, resting energy expenditure and
cardiometabolic risk factors in euthyroid subjects. Clin Nutr 2015;34:674e8.
[73] Capecci M, Petrelli M, Emanuelli B, Millevolte M, Nicolai A, Provinciali L, et al.
Rest energy expenditure in Parkinson's disease: role of disease progression
and dopaminergic therapy. Park Relat Disord 2013;19:238e41.
[74] Mourtzakis M, Wischmeyer P. Bedside ultrasound measurement of skeletal
muscle. Curr Opin Clin Nutr Metab Care 2014;17:389e95.
[75] Milte RK, Ratcliffe J, Miller MD, Crotty M. Economic evaluation for protein and
energy supplementation in adults: opportunities to strengthen the evidence.
Eur J Clin Nutr 2013;67:1243e50.
[76] Schulman RC, Mechanick JI. Metabolic and nutrition support in the chronic
critical illness syndrome. Respir Care 2012;57:958e77. discussion 977e958.
[77] Hughes MJ, Harrison EM, Wigmore SJ. Energy expenditure after liver
resection: validation of a mobile device for estimating resting energy
expenditure and an investigation of energy expenditure change after liver
resection. J Parenter Enter Nutr 2015. http://dx.doi.org/10.1177/
0148607115601969.
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Please cite this article in press as: Oshima T, et al., Indirect calorimetry in nutritional therapy. A position paper by the ICALIC study group, Clinical
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... Esta fórmula es implementada en caso de no tener a la disposición la calorimetría indirecta; sin embargo, no está validada para el paciente quemado en estado crítico. 32,33 En ausencia de esta herramienta, la ecuación de Toronto, que se basa en un análisis de regresión múltiple de un número importante de estudios calorimétricos, es una alternativa validada. 32 (NIa, RA) ...
... 32 Se debe tener en cuenta que, para la estimación del GE con la ecuación predictiva, igual que en adultos, se requiere de conocimiento sobre la condición subyacente del caso, los factores que alteran la respuesta metabólica a la enfermedad y las limitaciones de la ecuación que se utiliza. 33 ...
... Estimating energy requirements (ER) is a crucial step in providing nutritional care to any population, especially to those at risk of malnutrition (1). Patients with chronic kidney disease (CKD) undergo a variety of physiological changes that may affect their ER and, thus, their nutritional status (2,3). ...
... Specifically for kidney patients, providing an appropriate and individualized nutritional treatment should consider the risk that this population has of developing malnutrition and attempt to avoid adverse outcomes associated with this phenomenon, even in the early stages of the disease (21). Today, various reference guides recommend estimating energy using the reference standard (CI) (1,7,22). However, since this method is not widely available, having validated energy estimation equations based on the reference standard is critical. ...
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Background Estimating energy requirements (ER) is crucial for nutritional attention to chronic kidney disease (CKD) patients. Current guidelines recommend measuring ER with indirect calorimetry (IC) when possible. Due to clinical settings, the use of simple formulas is preferred. Few studies have modeled equations for estimating ER for CKD. Nevertheless, variables of interest such as nutritional status and strength have not been explored in these models. This study aimed to develop and validate a model for estimating REE in patients with CKD stages 3–5, who were not receiving renal replacement therapy (RTT), using clinical variables and comparing it with indirect calorimetry as the gold standard. Methods In this study 80 patients with CKD participated. Indirect calorimetry (IC) was performed in all patients. The calorimeter analyzed metabolic measurements every minute for 15 min after autocalibration with barometric pressure, temperature, and humidity. Bioelectrical Impedance Analysis (BIA) was performed. Fat-free mass (FFM) was registered among other bioelectrical components. Handgrip strength (HGS) was evaluated and an average of 3 repetitions was recorded. Nutritional status was assessed with the subjective global assessment (SGA). Patients categorized as B or C were then considered as having malnutrition. Results We analyzed 71 patients and 3 models were generated. Model 1a included FFM; Model 2a included weight; Model 3c included handgrip strength (HGS). All other variables were stepwise, computer-selected with a p < 0.01 significance level; Malnutrition was consistently associated with ER among other clinical variables in all models ( p < 0.05). The model that included BIA-FFM had R ² adjusted = 0.46, while the model that included weight (Kg) had an adjusted R ² adjusted = 0.44. The models had moderate concordance, LC = 0.60–0.65 with the gold standard, whereas other energy expenditure estimation equations had LC = 0.36 and 0.55 with indirect calorimetry. Using these previously validated equations as a reference, our models had concordance values ranging from 0.66 to 0.80 with them. Conclusion Models incorporating nutritional status and other clinical variables such as weight, FFM, comorbidities, gender, and age have a moderate agreement with REE. The agreement between our models and others previously validated for the CKD patient is good; however, the agreement between the latter and IC measurements is moderate. The KDOQI lowest recommendation (25 Kcals/kg body weight) considering the 22% difference with respect to the IC for total energy expenditure rather than for REE.
... The canopy was used to measure resting energy expenditure (REE) in spontaneously breathing subjects. The procedure recommended by Oshima et al. (27) was followed. The subject was placed under a clear canopy with a plastic drape to avoid air leakage. ...
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The way different food consumption habits in healthy normal-weight individuals can shape their emotional and cognitive relationship with food and further disease susceptibility has been poorly investigated. Documenting the individual consumption of Western-type foods (i.e., high-calorie, sweet, fatty, and/or salty) in relation to psychological traits and brain responses to food-related situations can shed light on the early neurocognitive susceptibility to further diseases and disorders. We aimed to explore the relationship between eating habits, psychological components of eating, and brain responses as measured by blood oxygen level-dependent functional magnetic resonance imaging (fMRI) during a cognitive food choice task and using functional connectivity (FC) during resting-state fMRI (rsfMRI) in a population of 50 healthy normal-weight young women. A Food Consumption Frequency Questionnaire (FCFQ) was used to classify them on the basis of their eating habits and preferences by principal component analysis (PCA). Based on the PCA, we defined two eating habit profiles, namely, prudent-type consumers (PTc, N = 25) and Western-type consumers (WTc, N = 25), i.e., low and high consumers of western diet (WD) foods, respectively. The first two PCA dimensions, PCA1 and PCA2, were associated with different psychological components of eating and brain responses in regions involved in reward and motivation (striatum), hedonic evaluation (orbitofrontal cortex, OFC), decision conflict (anterior cingulate cortex, ACC), and cognitive control of eating (prefrontal cortex). PCA1 was inversely correlated with the FC between the right nucleus accumbens and the left lateral OFC, while PCA2 was inversely correlated with the FC between the right insula and the ACC. Our results suggest that, among a healthy population, distinct eating profiles can be detected, with specific correlates in the psychological components of eating behavior, which are also related to a modulation in the reward and motivation system during food choices. We could detect different patterns in brain functioning at rest, with reduced connectivity between the reward system and the frontal brain region in Western-type food consumers, which might be considered as an initial change toward ongoing modified cortico-striatal control.
... However, few studies have investigated the use of exclusive or supplemental PN early in the acute phase of sepsis [14,15]. Moreover, Weijs et al. reported that an increased protein intake (1.2 g/kg/day) did not improve outcomes in patients with sepsis when compared with outcomes in patients without sepsis [6,16,17]; however, corresponding adverse effects were not observed. Moreover, a few well-established studies have investigated low-energy, low-protein nutrition for critically ill patients with sepsis. ...
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The initial nutritional delivery policy for patients with sepsis admitted to the intensive care unit (ICU) has not been fully elucidated. We aimed to determine whether an initial adequate nutrition supply and route of nutrition delivery during the first week of sepsis onset improve clinical outcomes of critically ill patients with sepsis. We reviewed adult patients with sepsis and septic shock in the ICU in a single tertiary teaching hospital between 31 November 2013 and 20 May 2017. Poisson log-linear and Cox regressions were performed to assess the relationships between clinical outcomes and sex, modified nutrition risk in the critically ill score, sequential organ failure assessment score, route of nutrition delivery, acute physiology and chronic health evaluation score, and daily energy and protein delivery during the first week of sepsis onset. In total, 834 patients were included. Patients who had a higher protein intake during the first week of sepsis onset had a lower in-hospital mortality (adjusted hazard ratio (HR), 0.55; 95% confidence interval (CI), 0.39–0.78; p = 0.001). A higher energy intake was associated with a lower 30-day mortality (adjusted HR, 0.94; 95% CI, 0.90–0.98; p = 0.003). The route of nutrition delivery was not associated with 1-year mortality in the group which was underfed; however, in patients who met > 70% of their nutritional requirement, enteral feeding (EN) with supplemental parenteral nutrition (PN) was superior to only EN (p = 0.016) or PN (p = 0.042). In patients with sepsis and septic shock, a high daily average protein intake may lower in-hospital mortality, and a high energy intake may lower the 30-day mortality, especially in those with a high modified nutrition risk in the critically ill scores. In patients who receive adequate energy, EN with supplemental PN may be better than only EN or PN, but not in underfed patients.
... Due to the exchange of solutes during CRRT, nutrition therapy is more challenging. First, CRRT induces CO 2 exchange outside the lungs, leading to inaccurate REE measurements with IC [13]. The earlier manuscript of this MECCIAS trial clarified how the CO 2 is exchanged during CVVH and how a correction factor can be integrated into the REE measurements by IC to calculate the 'true REE' [14,15]. ...
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(1) Background: Nutrition therapy guided by indirect calorimetry (IC) is the gold standard and is associated with lower morbidity and mortality in critically ill patients. When performing IC during continuous venovenous hemofiltration (CVVH), the measured VCO2 should be corrected for the exchanged CO2 to calculate the ‘true’ Resting Energy Expenditure (REE). After the determination of the true REE, the caloric prescription should be adapted to the removal and addition of non-intentional calories due to citrate, glucose, and lactate in dialysis fluids to avoid over- and underfeeding. We aimed to evaluate this bioenergetic balance during CVVH and how nutrition therapy should be adapted. (2) Methods: This post hoc analysis evaluated citrate, glucose, and lactate exchange. Bioenergetic balances were calculated based on these values during three different CVVH settings: low dose with citrate, high dose with citrate, and low dose without citrate. The caloric load of these non-intentional calories during a CVVH-run was compared to the true REE. (3) Results: We included 19 CVVH-runs. The bioenergetic balance during the low dose with citrate was 498 ± 110 kcal/day (range 339 to 681 kcal/day) or 26 ± 9% (range 14 to 42%) of the true REE. During the high dose with citrate, it was 262 ± 222 kcal/day (range 56 to 262 kcal/day) or 17 ± 11% (range 7 to 32%) of the true REE. During the low dose without citrate, the bioenergetic balance was −189 ± 77 kcal/day (range −298 to −92 kcal/day) or −13 ± 8% (range −28 to −5%) of the true REE. (4) Conclusions: Different CVVH settings resulted in different bioenergetic balances ranging from −28% up to +42% of the true REE depending on the CVVH fluids chosen. When formulating a caloric prescription during CVVH, an individual approach considering the impact of these non-intentional calories is warranted.
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Purpose of review: To discuss how nutritional management could be optimized to promote protective metabolism in sepsis and associated acute kidney injury. Recent findings: Recent evidence suggests that sepsis is a metabolically distinct critical illness and that certain metabolic alterations, such as activation of fasting metabolism, may be protective in bacterial sepsis. These findings may explain the lack of survival benefit in recent randomized controlled trials of nutrition therapy for critical illness. These trials are limited by cohort heterogeneity, combining both septic and nonseptic critical illness, and the use of inaccurate caloric estimates to determine energy requirements. These energy estimates are also unable to provide information on specific substrate preferences or the capacity for substrate utilization. As a result, high protein feeding beyond the capacity for protein synthesis could cause harm in septic patients. Excess glucose and insulin exposures suppress fatty acid oxidation, ketogenesis and autophagy, of which emerging evidence suggest are protective against sepsis associated organ damage such as acute kidney injury. Summary: Distinguishing pathogenic and protective sepsis-related metabolic changes are critical to enhancing and individualizing nutrition management for critically ill patients.
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Article
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Article
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Indirect calorimetry allows the determination of energy expenditure in critically ill patients by measuring oxygen consumption (VO 2 ) and carbon dioxide production (VCO 2 ). Recent studies have demonstrated variable performance of “breath-by-breath” instruments compared to mixing chamber technology. The aim of this study was to validate two modern devices (E-sCOVX and Quark RMR) against a reference method (Deltatrac II). Measurements of VO 2 /VCO 2 with the test and reference devices were performed simultaneously over a 20-min period in mechanically ventilated adult intensive care unit patients. Accuracy and precision of instruments were analyzed using Bland-Altman plots. Forty-eight measurements in 22 patients were included for analysis. Both E-sCOVX and Quark RMR overestimated VO 2 and VCO 2 compared to Deltatrac II, corresponding to a 10 % higher mean resting energy expenditure. Limits of agreement of resting energy expenditure within ±2 standard deviations were ±461 kcal/24 h (±21 % expressed as percentage error) for ΔE-sCOVX–Deltatrac II and ±465 kcal/24 h (±22 %) for ΔQuark RMR–Deltatrac II. Both test devices overestimate VO 2 and VCO 2 compared to Deltatrac II. The observed limits of agreement are comparable to those commonly accepted in evaluations of circulatory monitoring, and significantly less than results from predictive equations. We hypothesize that the discrepancy between methods is due to patient/ventilator-related factors that affect the synchronization of gas and spirometry waveforms. Trial registration Australian New Zealand Clinical Trials Registry, Trial ID ACTRN12615000205538. Date registered 3 March 2015.
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