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Background When indirect calorimetry is not available, predictive equations are used to estimate resing energy expenditure (REE). There is no consensus about which equation to use in hospitalized patients. The objective of this study is to examine the validity of REE predictive equations for underweight, normal weight, overweight, and obese inpatients and outpatients by comparison with indirect calorimetry. Methods Equations were included when based on weight, height, age, and/or gender. REE was measured with indirect calorimetry. A prediction between 90 and 110% of the measured REE was considered accurate. The bias and root-mean-square error (RMSE) were used to evaluate how well the equations fitted the REE measurement. Subgroup analysis was performed for BMI. A new equation was developed based on regression analysis and tested. Results513 general hospital patients were included, (253 F, 260 M), 237 inpatients and 276 outpatients. Fifteen predictive equations were used. The most used fixed factors (25 kcal/kg/day, 30 kcal/kg/day and 2000 kcal for female and 2500 kcal for male) were added. The percentage of accurate predicted REE was low in all equations, ranging from 8 to 49%. Overall the new equation performed equal to the best performing Korth equation and slightly better than the well-known WHO equation based on weight and height (49% vs 45% accurate). Categorized by BMI subgroups, the new equation, Korth and the WHO equation based on weight and height performed best in all categories except from the obese subgroup. The original Harris and Benedict (HB) equation was best for obese patients. ConclusionsREE predictive equations are only accurate in about half the patients. The WHO equation is advised up to BMI 30, and HB equation is advised for obese (over BMI 30). Measuring REE with indirect calorimetry is preferred, and should be used when available and feasible in order to optimize nutritional support in hospital inpatients and outpatients with different degrees of malnutrition.
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R E S E A R C H Open Access
Predicting resting energy expenditure in
underweight, normal weight, overweight,
and obese adult hospital patients
Hinke M. Kruizenga
1*
, Geesje H. Hofsteenge
1
and Peter J.M. Weijs
1,2
Abstract
Background: When indirect calorimetry is not available, predictive equations are used to estimate resing energy
expenditure (REE). There is no consensus about which equation to use in hospitalized patients. The objective of this
study is to examine the validity of REE predictive equations for underweight, normal weight, overweight, and obese
inpatients and outpatients by comparison with indirect calorimetry.
Methods: Equations were included when based on weight, height, age, and/or gender. REE was measured
with indirect calorimetry. A prediction between 90 and 110% of the measured REE was considered accurate.
The bias and root-mean-square error (RMSE) were used to evaluate how well the equations fitted the REE
measurement. Subgroup analysis was performed for BMI. A new equation was developed based on regression
analysis and tested.
Results: 513 general hospital patients were included, (253 F, 260 M), 237 inpatients and 276 outpatients.
Fifteen predictive equations were used. The most used fixed factors (25 kcal/kg/day, 30 kcal/kg/day and
2000 kcal for female and 2500 kcal for male) were added. The percentage of accurate predicted REE was low
in all equations, ranging from 8 to 49%. Overall the new equation performed equal to the best performing
Korth equation and slightly better than the well-known WHO equation based on weight and height (49% vs
45% accurate). Categorized by BMI subgroups, the new equation, Korth and the WHO equation based on
weight and height performed best in all categories except from the obese subgroup. The original Harris and
Benedict (HB) equation was best for obese patients.
Conclusions: REE predictive equations are only accurate in about half the patients. The WHO equation is
advised up to BMI 30, and HB equation is advised for obese (over BMI 30). Measuring REE with indirect
calorimetry is preferred, and should be used when available and feasible in order to optimize nutritional
support in hospital inpatients and outpatients with different degrees of malnutrition.
Keywords: Resting energy expenditure, Equation, BMI, Prediction, Validity, Underweight, Normal weight,
Overweight, Obese, Indirect calorimetry
* Correspondence: h.kruizenga@vumc.nl
1
Department of Nutrition and Dietetics, Internal Medicine, VU University
Medical Center, P.O. Box 7057, Amsterdam 1007 MB, The Netherlands
Full list of author information is available at the end of the article
© The Author(s). 2016 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.
Kruizenga et al. Nutrition & Metabolism (2016) 13:85
DOI 10.1186/s12986-016-0145-3
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Background
In clinical practice, an adequate measurement of rest-
ing energy expenditure (REE) for adult patients is im-
portant for optimal nutritional therapy in order to
prevent under- and over nutrition [1]. REE in adult
patients can be measured by indirect calorimetry,
based on oxygen consumption and carbon dioxide
production [2]. Indirect calorimetry is considered as
the most accurate method [3] for determining the
REE in adult patients [4, 5]; however, this measure-
ment is time-consuming and not available in most
clinical settings. As an alternative, REE is usually cal-
culated with various REE predictive equations, based
on healthy subjects [1, 6].
Only few studies have validated REE predictive
equations in hospitalized patients [79]. The num-
ber of validated predictive equations is small [7, 8]
and studies have small sample sizes [7, 9]. There-
fore, there is no consensus about which equation to
use in hospitalized patients. According to Boullata
et al. [8], the Harris & Benedict (1918) (HB1918)
[10] equation is the best equation to predict REE,
when using an illness factor of 1.1. It appeared 62%
of the patients were predicted accurately using this
equation. Anderegg et al. [7] suggests HB1918 with
adjusted bodyweight and a stress factor, which led
to 50% accurately predicted patients. Weijs et al. [9]
suggest the WHO and adjusted Harris & Benedict
(HB1984) [11] equations, predicting about 50% of
the patients accurately. More recently, Jesus et al.
[12] showed that the original Harris & Benedict
equation (HB1918) performed reasonably, but no
equation was adequate for extreme BMI groups
(<16 and >40).
Therefore, it is unclear which REE predictive
equation performs most uniform across BMI sub-
groups for hospital patients. The aim of this study
is to examine the validity of REE predictive equa-
tions for underweight, normal weight, overweight,
and obese patients by comparison with indirect
calorimetry.
Methods
Patients
Between March 2005 and December 2015, data
were collected at the VU University Medical Center
Amsterdam. Patients who had an indication for nu-
tritional assessment by the dietitian were included
in this study. All measurements were performed
according to a standardized operating procedure
(SOP), and personal was trained in a standardized
manner. Patients were measured as part of patient
care. As malnutrition is the main reason for
measurement, withholding food for longer than
absolutely necessary is questionable and maybe un-
ethical. All patients were restricted from food for at
least 2 h before the measurement. None of the
patients were restricted from food for 8 h, as the
guideline [13] indicates.
Only adult patients with complete data (height,
weight, age, and gender) were included. When re-
peated REE measurements were available, only the
first measurement was included. Exclusion criteria
were patients at ICU, pregnant women, and REE
measurements shorter than 15 min. All procedures
were in accordance with ethical standards of the
institution.
Indirect calorimetry and anthropometric measurements
Indirect calorimetry measurements were performed by
using a metabolic monitor (Deltatrac 2 MBM-200,
Datex-Ohmeda, Helsinki, Finland; Vmax Encore n29,
Viasys Healthcare, Houten, The Netherlands). Both
devices were calibrated every day before use and
Vmax also every 5 min during measurement. The
Deltatrac was calibrated with one reference gas mix-
ture (95% O
2
,5%CO
2
), whereas Vmax was calibrated
with two standard gases (26% O
2
,0%CO
2,
and 16%
O
2
,4%CO
2
). Patients were measured in supine pos-
ition. Calibration and measurements were performed
by a trained dietitian. Oxygen analyser sensitivity was
checked yearly by supplier.
Body weight was measured using a calibrated elec-
tronic stand-up scale (Seca Alpha, Hamburg, Germany).
In case of severe oedema or when weighing was not pos-
sible, even weighing in bed, self-reported weight was
used. Height of the patient was measured or self-
reported. BMI was calculated as weight (kg) divided by
the square of height (m
2
).
REE predictive equations
Predictive equations were obtained by a systematic
search using PubMed. Mesh-derived keys energy me-
tabolism,basal metabolismand indirect calorimetry
and additional terms (predict*,estimat*,equation*
and formula*) were applied in every possible com-
bination. Applied limitations were English language,
humansand the age of 18 years and older. Add-
itional publications were checked based on reference
lists. Equations were included when based on body
weight, height, age, and/or gender.
The Weijs equation for overweight patients [14]
was tested in patients with BMI > 25. For the BMI
< 25 subgroup, a new REE predictive equation was
developed in this subpopulation with BMI < 25
using regression analysis with measured REE (kcal/
day) as dependent and body weight (kg), height
Kruizenga et al. Nutrition & Metabolism (2016) 13:85 Page 2 of 9
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(m), age (y), and sex (F = 0, M = 1) as independent
variables.
Statistical analysis
An independent samples T-test was used for differ-
ences in weight, BMI, age, and REE between inpa-
tients and outpatients, as well as between males and
females. BMI subgroups were analysed: underweight
(BMI < 18.5 kg/m
2
), normal weight (BMI 18.5- <
25 kg/m
2
), overweight (BMI 25- < 30 kg/m
2
), and
obese patients (BMI 30 kg/m
2
). The difference be-
tween the REE predictive equation and REE measured
was calculated as percentage. A prediction between
90 and 110% of the REE measured was considered as
accurate prediction. A prediction below 90% was con-
sidered as under prediction and a prediction over
110% was considered as over prediction. The bias in-
dicates the mean percentage error between REE pre-
dictive equation and REE measured. The root-mean-
square-error (RMSE), expressed in kcal/day, was used
to measure how well the equations fitted the REE
measurement.
To check whether in underweight and obese patients
adjustment of weight in the REE predictive equation re-
sulted in a better performance of the equation, body weight
adjustment was applied (BMI < 18.5: weight adjusted to
BMI = 18.5); BMI > 30: weight adjusted to BMI = 30). The
criterion for improvement of performance was percentage
accurate predictions. Statistical significance was
reached when p< 0.05. Data was analysed with IBM
SPSS Statistics 20.
Results
Patients
Table 1 shows characteristics of study populations.
REE measurements of 593 patients were available.
Eighty had incomplete data. In total, 513 general
hospital patients were included, (253 F, 260 M),
237 inpatients and 276 outpatients. These patients
were often complex patients with multimobidity
and were categorised as oncology (29%), gastro-
enterology (19%, Diabetes/overweight (14%), Neph-
rology (10%), Lung diseases (7%), Neurology (5%),
diagnostics in unintentional weight loss (5%) and a
rest group (8%) of cardiology, anorexia nervosa,
auto immune disease, spinal cord injury and RA
patients.
REE predictive equations
In total, 15 predictive equations were used. The most
used fixed factors (25 kcal/kg/day, 30 kcal/kg/day and
2000 kcal for female and 2500 kcal for male) were
added. These fixed factors calculate total energy expend-
iture and in order to provide REE, they were divided by
a physical activity and/or stress factor of 1.3. Appendix 1
shows the descriptives of the included REE predictive
equations.
Accuracy of predictive equations
Based on REE data of patients with BMI < 25 a new
equation was developed in the current population:
BMI < 25: REE (kcal/day) = 11.355 × weight (kg) +
7.224 × height (cm) - 4.649 × age (y) + 135.265 × sex
(F = 0; M = 1) - 137.475; for BMI 25 an equation had
been developed on healthy overweight and/or obese
subjects by Weijs and Vansant [14]: BMI 25: REE
(kcal/day) = 14.038 × weight (kg) + 4.498 × height
(cm) - 0.977 × age (y) + 137.566 × sex (F = 0; M = 1) -
221.631.
Table 2 shows statistics of the REE predictive
equations for all patients. The percentage of accur-
ate predicted REE was low in all equations, ranging
from 8 to 49%. Overall the new equation per-
formed equal to the best performing Korth equa-
tion and slightly better than the well-known WHO
equation based on weight and height (49% vs 45%
accurate).
Table 3 shows statistics for the best predictive equa-
tions categorized by BMI subgroups. The new equa-
tion, Korth and the WHO equation based on weight
and height performed best in all categories except
from the obese subgroup. HB1918 was best for obese
patients.
Figure 1 shows the percentage of accurately predicted
underweight and obese patients with actual as well as
adjusted weight using the WHO equation with weight
and height [15] and HB1918 [10]. Adjusting the weight
in the equation in underweight and obese patient did
not improve the percentage of patients with an accurate
predicted REE.
Discussion
This study shows that for hospital inpatients and out-
patients the generally applied WHO [15] and the ori-
ginal Harris & Benedict equation (HB1918) [10] can
only predict resting energy expenditure accurately in
one of two to three patients. The generally used fixed
25 kcal/kg body weight was only accurate in 28% of
the patients. The Korth equation also performed well,
but not significantly better than the well implemented
WHO and H&B equations. The newly developed
equation performed equal to the best performing
equations but showed no additional value. Generally
applied weight adjustments all failed to improve ac-
curacy. Hospital inpatients and outpatients may still
benefit from using indirect calorimetry for assessment
of energy needs.
Kruizenga et al. Nutrition & Metabolism (2016) 13:85 Page 3 of 9
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Studies by Anderegg et al. [7] and Boullata et al. [8] ana-
lysed (in part) mechanically ventilated patients and are
therefore more difficult to compare to current inpatient
and outpatient analysis. However, in general they also
showed rather inaccurate estimates using different REE es-
timating equations. Based on a similar analysis with a
much smaller sample size, Weijs et al. [9] concluded that
the WHO equation (1985) [15] based on weight and
height and Harris & Benedict (1984) [11] were the best
predictive equations. The current analysis confirms that
the overall accuracy of REE predictive equations is only
about 50%, however this study extends this analysis to
BMI subgroups for which predictive accuracy may in fact
be much worse.
Table 1 Patient characteristics for the total group and per BMI group
Total group BMI < 18,5 BMI 18,525 BMI 2530 BMI > 30
N (%) 513 141 (27%) 209 (41%) 77 (15%) 86 (17%)
Mean SD Mean SD Mean SD Mean SD Mean SD
Age (y) 53.0 15.6 51.3 17.0 54.1 15.2 55.3 15.2 50.9 14.2
% Male 51% 44% 58% 53% 41%
Weight (kg) 70.1 22.9 49.4 7.3 64.2 8.7 83.2 11.0 106.7 21.3
Height (m) 1.73 0.10 1.72 0.10 1.74 0.09 1.74 0.10 1.71 0.12
BMI (kg/m
2
) 23.4 7.2 16.6 1.5 21.3 1.8 27.3 1.4 36.3 5.4
REE (kcal/day) 1678 408 1448 318 1696 358 1730 352 1966 488
REE in kcal/kg/day (range) 25.1 (1253) 6.2 29.4 (1843) 5.5 26.6 (1453) 5.3 20.8 (1231) 3.3 18.5 (1329) 3.2
% inpatients 46% 57% 55% 35% 17%
Table 2 Statistics of REE prediction equation performance, N = 513
REE (kcal/day) SD Under prediction (%)
a
Accurate prediction (%)
b
Over prediction (%)
c
BIAS
d
RMSE
e
REE by calorimetrie 1678 408
New equation 1698 313 19 49 32 4 286
Korth [18] 1621 344 30 49 22 1 295
WHO-wtht [15] 1540 288 40 45 14 6 321
Schofield-wtht [19] 1513 282 46 42 12 7 333
Henry-wtht [20] 1489 291 51 39 10 9 344
WHO-wt [15] 1504 304 49 39 13 8 345
Harris& Benedict 1918 [10] 1490 324 51 38 11 9 350
Muller [21] 1493 308 52 37 11 9 347
H&B by Roza [11] 1494 321 53 37 11 9 344
Schofield-wt [19] 1483 293 53 36 12 9 355
Mifflin [22] 1444 304 60 32 8 12 369
Henry2005-wt [20] 1458 320 58 31 10 11 370
MullerBMI [21] 1396 435 60 31 9 16 450
30 kcal/kg 1618 527 44 28 28 2 435
Livingston [23] 1405 284 66 27 7 14 399
25 kcal/kg 1348 440 68 23 9 19 502
2000 kcal for female and
2500 kcal for male
2253 250 3 11 87 41 689
Bernstein [24] 1208 271 90 8 2 26 557
a
The percentage of subjects predicted by this predictive equation < 10% of the measured value
b
The percentage of subjects predicted by this predictive equation within 10% of the measured value
c
The percentage of subjects predicted by this predictive equation > 10% of the measured value
d
Mean percentage error between predictive equation and measured value
e
Root mean squared prediction error
Kruizenga et al. Nutrition & Metabolism (2016) 13:85 Page 4 of 9
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Table 3 REE predictive accuracy of prediction equations in BMI subgroups
Total group (n = 513) BMI <18.5 (n = 141) BMI 18.525 (n = 209) BMI 2530 (n = 77) BMI > 30 (n = 86)
Under
predic-tion
Accu-
rate
Over
predic-tion
Under
predic-tion
Accu-
rate
Over
predic-tion
Under
predic-tion
Accu-
rate
Over
predic-tion
Under
predic-tion
Accu-
rate
Over
predic-tion
Under
predic-tion
Accu-
rate
Over
predic-tion
%%%%%%%%%%%%%%%
New equation 19 49 32 21 44 35 22 51 27 14 58 27 14 44 42
Korth [18] 30 492235 402434 5214175627224830
WHO-wtht [15] 40 45 14 40 45 14 48 43 9 27 55 18 33 44 23
Schofield-wtht [19] 46 42 12 43 44 13 54 40 7 36 48 16 40 40 21
Henry-wtht [20] 51 39 10 50 37 13 60 35 4 40 45 14 38 45 16
WHO-wt [15] 49 39 13 52 35 12 60 33 7 30 53 17 31 45 23
Harris & Benedict 1918 [10] 51 38 11 60 27 13 63 33 3 34 53 13 26 53 21
Muller [21] 52 37 11 59 29 12 62 33 5 38 48 14 28 51 21
H&B by Roza [11] 53 37 11 57 30 13 65 33 3 39 45 16 29 50 21
Schofield-wt [19] 53 36 12 54 33 13 61 32 7 42 47 12 40 40 21
Mifflin [22] 58 33 8 60 28 12 66 30 4 45 45 9 48 40 13
Henry-wt [20] 60 32 8 60 28 12 67 29 4 49 43 8 52 37 10
MullerBMI [21] 60 32 8 63 27 10 70 27 3 48 43 9 43 43 14
30 kcal/kg 58 31 10 67 23 11 69 27 4 40 45 14 35 43 22
Livingston [23] 60 31 9 99 1 0 55 36 9 39 47 14 29 50 21
25 kcal/kg 44 28 28 78 16 6 51 39 11 8 40 52 5 10 85
2000 kcal for female and
2500 kcal for male
66 27 7 73 19 8 73 23 3 53 39 8 50 38 12
Bernstein [24] 68 23 9 91 9 0 86 12 2 40 49 10 14 47 40
Accurate prediction: the percentage of subjects predicted by this predictive equation within 10% of the measured value
Underprediction: the percentage of subjects predicted by this predictive equation <10% of the measured value
Overprediction: the percentage of subjects predicted by this predictive equation > 10% of the measured value
Kruizenga et al. Nutrition & Metabolism (2016) 13:85 Page 5 of 9
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Jesus et al. [12] showed that the overall accuracy of the
Harris & Benedict equation was reasonable for the out-
patient sample. The authors stress that predictive accuracy
is much worse in extreme BMI subgroups with BMI under
16 and BMI over 40. The current study generally supports
these conclusions, however extend these observations in
two ways. First, the general accuracy is not that much
higher in the normal weight patient group, in fact accur-
acy increases to highest level in overweight subgroup. Sec-
ondly, we agree that the subgroup of patients with BMI
less than 16 has a low prediction accuracy, however we
have also shown low prediction accuracy for a large cohort
of malnourished hospitalized elderly with mean BMI 21
(SD 4) [16]. Therefore, the suggestion that predictive
equations perform well between BMI 16 and BMI 40 is
largely false for hospital patients.
According to Frankenfield et al. [17], adjusting body
weight in obese patients leads to underestimation of the
energy expenditure. When this is done with a fixed BMI
level, the adjustment appears too large and does not re-
sult in a higher accuracy of REE prediction. How-
ever, accuracy remains low in all predictions.
This study has several strengths and limitations. The
sample size of 513 patients was large enough for subgroup
analysis, namely BMI subgroups. Furthermore, these data
were derived from daily clinical practice and therefore the
study population is representative for the inpatient and
outpatient population. Another advantage is the exclusion
of ICU patients that may not be entirely comparable to
the general hospital population. Therefore, this study has
a large generalizability to other hospitals and patients.
However, this study has some limitations as well. The
measurements were performed in clinical practice and
therefore patients were not measured in overnight fasted
state. However, since patients were measured because of
nutritional problems, the thermic effect of larger meals,
if any, were not a problem in this patient sample. This
could have been a problem in obese outpatients, how-
ever according to the results the estimations are most
accurate in this subgroup. Only when the dietitian indi-
cated the patient for nutritional assessment, a measure-
ment was performed. This may have led to selection bias
as only patients who were difficult to assess and/or treat
were included in this study. This may largely explain the
low level of accuracy in the current analysis.
This study population was too small to develop a new
equation for the hospital in and outpatients. The vari-
ation of REE between patients and probably between
disease groups is too large. A possible way forward, is to
develop new equations in more homogenous subgroups.
For this purpose a very large database would be needed
on REE in hospital patients. We propose to develop an
REE repository for clinical data, comparable to the
Oxford database on REE in healthy subjects. This could
be jointly organised within ESPEN and ASPEN.
Conclusions
In conclusion, REE predictive equations are only accur-
ate in about half the patients. The WHO equation is ad-
vised up to BMI 30, and HB1918 equation is advised for
obese (over BMI 30). Measuring REE with indirect calor-
imetry is preferred, and should be used when available
and feasible in order to optimize nutritional support in
hospital inpatients and outpatients with different degrees
of malnutrition.
Fig. 1 The percentage of accurately predicted underweight and obese patients with actual as well as adjusted weight (BMI < 18.5: weight adjusted to
BMI = 18.5); BMI > 30: weight adjusted to BMI = 30)
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Appendix 1
Table 4 Descriptives of included predictive equations
Author, year of publication and
referred to as
Study population and n Age (mean ± SD or
range)
REE Equations (kcal/day)
Bernstein, 1983 [24] Obese individuals; patients who
enrolled the Weight Control Unit
of the Obesity Research Center IC
instrument: Beckman
n: 48 M/154 F
M: 39 ± 12 y M: 11.02 × WT + 10.23 × HTCM - 5.8 × AGE 1032
F: 40 ± 13 y F: 7.48 × WT - 0.42 × HTCM - 3.0 × AGE + 844
FAO/WHO/UNU, 1985 [15]
WHO-wt
WHO-wtht
n: 575 M/734 F All: 3082y M 1830: (15.3 × WT) + 679
F1830: (14.7 × WT) + 496
M3060: (11.6 × WT) + 879
F3060: (8.7 × WT) + 829
M 60+: (13.5 × WT) + 487
F 60+: (10.5 × WT) + 596
Equations based on weight and height
M1830: (15.4 × WT) (27 × HTM) + 717
F1830: (13.3 × WT) + (334 × HTM) + 35
M3060: (11.3 × WT) + (16 × HTM) + 901
F3060: (8.7 × WT) - (25 × HTM) + 865
M 60+: (8.8 × WT) + (1128 × HTM) 1071
F 60+: (9.2 × WT) + (637 × HTM) 302
Harris & Benedict, 1918 [10]n: 136 M/103 F M: 27 ± 9 (1663) y M: 66.4730 + (13.7516 × WT) + (5.0033 × HTCM)
(6.7550 × AGE)
F: 31 ± 14 (1574) y F: 655.0955 + (9.5634 × WT) + (1.8496 × HTCM)
(4.6756 × AGE)
Harris & Benedict, 1984 Roza
& Shizgal [11]
H&B by Roza
Data of Harris & Benedict (1918)
and data of two further studies
by Benedict with data on
additional subjects (n: 168 M/169 F)
M: 30 ± 14 y M: 88.362 + (13.397 × WT) + (4.799 × HTCM)
(5.677 × AGE)
F: 44 ± 22 y F: 447.593 + (9.247 × WT) + (3.098 × HTCM)
(4.330 × AGE)
Korth, 2007 [18] Healthy euthyroid weight stable
subjects who were recruited by
local announcements
n: 50 M/54 F
M: 39 ± 14 (2168) y All: (41.5 × WT) (19.1 × AGE) + (35.0 × HTCM) +
(1107.4 × SEX) 1731.2/4.184
F: 35 ± 15 (2066) y
Livingston, 2005 [23]Institute of Medicine population
n: 299 M/356 F
M: 36 ± 15 (1895) y M: 293 × WT
0.4330
(5.92 × AGE)
F: 39 ± 13 (1877) y F: 248 × WT
0.4356
(5.09 × AGE)
Mifflin, 1990 [22] IC instrument: metabolic
measurement cart with a canopy
hood (Metabolic Measurement
Cart Horizons System)
n: 251 M (122 obese)/247 F
(112 obese)
M: 44 ± 14 (1978) y M: (9.99 × WT) + (6.25 × HTCM) (4.92 × AGE) + 5
F: 45 ± 14 (2076) y F: (9.99 × WT) + (6.25 × HTCM) (4.92 × AGE) 161
Muller, 2004 [21] Data from seven different research
centers in Germany
IC instruments: Deltatrac, Beckman,
Mouthpiece (metabolic chamber)
nBMI < 18.5: 58
nBMI 18.525: 444
nBMI 2530: 266
nBMI > 30: 278
BMI 18.5: 32 ± 12
y
All: (0.047 × WT) +(1.009 × SEX) (0.01452 × AGE) +
3.21/4.184 ×1000
BMI > 18.525: 38 ±
17 y
BMI 18.5: (0.07122 × WT) (0.02149 × AGE) +
(0.82 × SEX) + 0.731/4.184 ×1000
Muller BMI > 2530: 53 ±
16 y
BMI > 18.525: (0.02219 × WT) + (0.02118 × HTCM) +
(0.884 × SEX) (0.01191 × AGE) + 1.233/4.184 ×1000
MullerBMI BMI 30: 47 ± 13 y BMI > 2530: (0.04507 × WT) +(1.006 × SEX)
(0.01553 × AGE) + 3.407/4.184 ×1000
BMI 30: (0.05 × WT) + (1.103 × SEX)
(0.01586 × AGE) + 2.924/4.184 ×1000
Kruizenga et al. Nutrition & Metabolism (2016) 13:85 Page 7 of 9
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Abbreviations
BMI: Body Mass Index; H&B: Harris & Benedict; ICU: Intensive Care Unit;
REE: Resting energy expenditure; WHO: World Health Organisation
Acknowledgments
This study was supported by the VU University Medical Center, Amsterdam,
the Netherlands. We thank the dietetic team of the VU University Medical
Center Amsterdam for indirect calorimetry measurements and students for
their support in the project.
Funding
Not applicable.
Availability of data and materials
Please contact author for data requests.
Authorscontributions
HK, GH and PW designed the study, performed literature search, data
analysis, and writing of the manuscript, and confirmed final draft of the
manuscript.
Competing interests
The authors declare that they have no competing interests.
Consent for publication
Not applicable.
Ethics approval and consent to participate
All measurements took place in daily clinical practice. Data were
anonymised. All procedures were in accordance with ethical standards of the
institution.
Table 4 Descriptives of included predictive equations (Continued)
Henry, 2005 [20] Worldwide population (excluded
Italian subjects) from several papers
M1830 y: 2821/2816
F1830 y: 1664/1655
M3060 y: 1010/1006
F3060 y: 1023/1023
M 60+ y: 534/533
F 60+ y: 334/324
M1830: 22 y M 1830 y: (16 × WT) + 545
Henry-wt F 1830: 22 y F 1830 y: (13.1 × WT) + 558
M3060: 40 y M 3060 y: (14.2 × WT) + 593
F3060: 41 y F 3060 y: (9.74 × WT) + 694
M 60+: 70 y M 60+ y: (13.5 × WT) + 514
F 60+: 69 y F 60+ y: (10.1 × WT) + 569
Equations based on weight and height
Henry-wtht M1830 y: (14.4 × WT) + (313 × HTM) + 113
F1830 y: (10.4 × WT) + (615 × HTM) 282
M3060 y: (11.4 × WT) + (541 × HTM) 137
F3060 y: (8.18 × WT) + (502 × HTM) 11.6
M 60+ y: (11.4 × WT) + (541 × HTM) 256
F 60+ y: (8.52 × WT) + (421 × HTM) + 10.7
Schofield, 1985 [19] Collection of different authors and
papers
M1830 y: 2879
M3060 y: 646
M 60+ y: 50
F1830 y: 829
F3060 y: 372
F 60+ y: 38
M1830: 22 y M 1830 y: (0.063 × WT) + 2.896/4.184 × 1000
Schofield-wt F 1830: 22 y F 1830 y: (0.062 × WT) + 2.036/4.184 × 1000
M3060: 40 y M 3060 y: (0.048 × WT) + 3.653/4.184 × 1000
F3060: 40 y F 3060 y: (0.034 × WT) + 3.538/4.184 × 1000
M 60+: 72 y M 60+ y: (0.049 × WT) +2.459/4.184 × 1000
F 60+: 66 y F 60+ y: (0.038 × WT) + 2.755/4.184 × 1000
Equations based on weight and height
Schofield-wtht M1830 y: (0.063 × WT) (0.042 × HTM) +
2.953/4.184 × 1000
F1830 y: (0.057 × WT) + (1.184 × HTM) +
0.411/4.184 × 1000
M3060 y: (0.048 × WT) (0.011 × HTM) +
3.67/4.184 × 1000
F3060 y: (0.034 × WT) + (0.006 × HTM) +
3.53/4.184 × 1000
M 60+ y: (0.038 × WT) + (4.068 × HTM)
3.491/4.184 × 1000
F 60+ y: (0.033 × WT) + (1.917 × HTM) +
0.074/4.184 × 1000
Mmale, Ffemale, yyears, WT weight in kilogram, HTM height in meters, HTCM height in centimetres; SEX (male = 1, female = 0) sex, REE resting energy
expenditure; kcal/d kilocalories a day, IC indirect calorimetry
Kruizenga et al. Nutrition & Metabolism (2016) 13:85 Page 8 of 9
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Author details
1
Department of Nutrition and Dietetics, Internal Medicine, VU University
Medical Center, P.O. Box 7057, Amsterdam 1007 MB, The Netherlands.
2
Department of Nutrition and Dietetics, School of Sports and Nutrition,
Amsterdam University of Applied Sciences, Amsterdam, The Netherlands.
Received: 21 September 2016 Accepted: 15 November 2016
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Purpose The aim of this study is to provide insight into the causes, frequency and periods of enteral nutrition interruption (ENI) that occur in the Intensive Care Unit (ICU). Materials and methods This was a prospective, observational cohort study conducted at the ICU. Demographic data, admission and discharge data, mortality, days of intubation, use of prokinetic drugs, initiation time of enteral nutrition (EN), daily calculated targets of calories and protein, actual daily calories and protein delivered, and duration and causes of ENI were registered and analysed. Results In total 165 patients were assessed for eligibility during the study inclusion period, of which 61 patients were included in our study. Mean age was 60.8 ± 14.3 years, and majority of the patients were male (41 (67.2%)). In the first four study days, approximately 20% of patients had at least one episode of ENI, which gradually decreased until the seventh study day. A total 115 ENIs occurred in our seven-day follow-up period. The most ENIs occurred in the first three days of ICU admission. In the first four days, there was a significant difference between mean percentage ‘goal feeding’ reached in the ENI group without periods of ENI (p<0.001). Conclusion The prevalence of unplanned ENIs in ICU patients is highest in the first three days of admission. The main reason for ENIs was because of diagnostic reasons. The ENIs resulted in on average approximately a quarter of patients who failed in calculated caloric and protein requirements during first four days of admission.
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Background: The American and European guidelines recommend measuring resting energy expenditure (REE) using indirect calorimetry (IC). Predictive equations (PEs) are used to estimate REE, but there is limited evidence for their use in critically ill patients. The aim of this study is to evaluate the degree of agreement and accuracy between IC-measured REE (REE-IC) and 10 different PEs in mechanically ventilated critically ill patients with surgical trauma who met their estimated energy requirement. Methods: REE-IC was retrospectively compared with REE-PE by 10 PEs. The degree of agreement between REE-PE and REE-IC was analyzed by the Bland-Altman test (BAt) and the concordance correlation coefficient (CCC). The accuracy was calculated by the percentage of patients whose REE-PE values differ by up to ±10% in relation to REE-IC. All analyses were stratified by gender and body mass index (BMI; <25 vs ≥25). Results: We analyzed 104 patients and the closest estimate to REE-IC was the modified Harris-Benedict equation (mHB) by the BAt with a mean difference of 49.2 overall (61.6 for males, 28.5 for females, 67.5 for BMI <25, and 42.5 for BMI ≥25). The overall CCC between the REE-IC and mHB was 0.652 (0.560 for males, 0.496 for females, 0.570 for BMI <25, and 0.598 for BMI ≥25). The mHB equation was the most accurate with an overall accuracy of 44.2%. Conclusion: The effectiveness of PEs for estimating the REE of mechanically ventilated surgical-trauma critically ill patients with is limited. Nonetheless, of the 10 equations examined, the closest to REE-IC was the mHB equation.
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The estimation of caloric needs of critically ill patients is usually based on energy expenditure (EE), while current recommendations for caloric intake most often rely on a fixed amount of calories. In fact, during the early phase of critical illness, caloric needs are probably lower than EE, as a substantial proportion of EE is covered by the non-inhibitable endogenous glucose production. Hence, the risk of overfeeding is higher during the early phase than the late phase, while the risk of underfeeding is higher during the late phase of critical illness. Therefore, an accurate measurement of EE can be helpful to prevent early overfeeding and late underfeeding. Available techniques to assess EE include predictive equations, calorimetry, and doubly labeled water, the reference method. The available predictive equations are often inaccurate, while indirect calorimetry is difficult to perform for several reasons, including a shortage of reliable devices and technical limitations. In this review, the authors intend to discuss the different techniques and the influence of the method used on the interpretation of the results of clinical studies.
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Background: Predicting resting energy expenditure (REE) in malnourished hospitalized older patients is important for establishing optimal goals for nutritional intake. Measuring REE by indirect calorimetry is hardly feasible in most clinical settings. Objective: To study the most accurate and precise REE predictive equation for malnourished older patients at hospital admission and again three months after discharge. Design: Twenty-three equations based on weight, height, gender, age, fat free mass (FFM) and/or fat mass (FM) and eleven fixed factors of kcal/kg were compared to measured REE. REE was measured by indirect calorimetry. Accuracy of REE equations was evaluated by the percentage patients predicted within 10% of REE measured, the mean percentage difference between predicted and measured values (bias) and the Root Mean Squared prediction Error (RMSE). Results: REE was measured in 194 patients at hospital admission (mean 1473 kcal/d) and again three months after hospital discharge in 107 patients (mean 1448 kcal/d). The best equations predicted 40% accuracy at hospital admission (Lazzer, FAO/WHO-wh and Owen) and 63% three months after discharge (FAO/WHO-wh). Equations combined with FFM, height or illness factor predicted slightly better. Fixed factors produce large RMSE's. All predictive equations showed proportional bias, with overestimation of low REE values and underestimation of high REE values. Correction by regression analysis did not improve results. Conclusions: The REE predictive equations are not adequate to predict REE in malnourished hospitalized older patients. There is an urgent need for either a new accurate REE predictive equation, or accurate easy-to-use equipment to measure REE in clinical practice.
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Individual energy requirements of overweight and obese adults can often not be measured by indirect calorimetry, mainly due to the time-consuming procedure and the high costs. To analyze which resting energy expenditure (REE) predictive equation is the best alternative for indirect calorimetry in Belgian normal weight to morbid obese women. Predictive equations were included when based on weight, height, gender, age, fat free mass and fat mass. REE was measured with indirect calorimetry. Accuracy of equations was evaluated by the percentage of subjects predicted within 10% of REE measured, the root mean squared prediction error (RMSE) and the mean percentage difference (bias) between predicted and measured REE. Twenty-seven predictive equations (of which 9 based on FFM) were included. Validation was based on 536 F (18-71 year). Most accurate and precise for the Belgian women were the Huang, Siervo, Muller (FFM), Harris-Benedict (HB), and the Mifflin equation with 71%, 71%, 70%, 69%, and 68% accurate predictions, respectively; bias -1.7, -0.5, +1.1, +2.2, and -1.8%, RMSE 168, 170, 163, 167, and 173kcal/d. The equations of HB and Mifflin are most widely used in clinical practice and both provide accurate predictions across a wide range of BMI groups. In an already overweight group the underpredicting Mifflin equation might be preferred. Above BMI 45kg/m(2), the Siervo equation performed best, while the FAO/WHO/UNU or Schofield equation should not be used in this extremely obese group. In Belgian women, the original Harris-Benedict or the Mifflin equation is a reliable tool to predict REE across a wide variety of body weight (BMI 18.5-50). Estimations for the BMI range between 30 and 40kg/m(2), however, should be improved.
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Background & aims The resting energy expenditure (REE) predictive formulas are often used in clinical practice to adapt the nutritional intake of patients or to compare to REE measured by indirect calorimetry. We aimed to evaluate which predictive equations was the best alternative to REE measurements according to the BMI. Methods 28 REE prediction equations were studied in a population of 1726 patients without acute or chronic high-grade inflammatory diseases followed in a nutrition unit for malnutrition, eating disorders or obesity. REE was measured by indirect calorimetry for 30 min after a fasting period of 12h. Some formulas requiring fat mass and free-fat mass, body composition was measured by bioelectrical impedance analysis. The percentage of accurate prediction (± 10% / REE measured) and Pearson r correlations were calculated. Results Original Harris & Benedict equation provided 73.0% of accurate predictions in normal BMI group but only 39.3% and 62.4% in patients with BMI < 16 kg.m-2 and BMI ≥ 40 kg.m-2, respectively. In particularly, this equation overestimated the REE in 51.74% of patients with BMI < 16 kg.m-2. Huang equation involving body composition provided the highest percent of accurate prediction, 42.7% and 66.0% in patients with BMI < 16 and >40 kg.m-2, respectively. Conclusion Usual predictive equations of REE are not suitable for predicting REE in patients with extreme BMI, in particularly in patients with BMI < 16 kg.m-2. Indirect Calorimetry may still be recommended for an accurate assessment of REE in this population until the development of an adapted predictive equation.backgroun
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European Journal of Clinical Nutrition is a high quality, peer-reviewed journal that covers all aspects of human nutrition.
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Background & aims: Indirect calorimetry is the gold standard in determining energy expenditure to dose nutritional therapy for critically ill patients. The most commonly used system for indirect calorimetry in the ICU setting (Deltatrac Metabolic Monitor) is no longer in production. The aim of this study was to compare two new instruments for IC (Quark RMR, CCM Express) to the Deltatrac in mechanically ventilated patients. Methods: Sequential measurements with all three instruments were performed in randomized order on 24 mechanically ventilated ICU patients. Resting energy expenditure (REE), respiratory quotient (RQ), oxygen consumption and carbon dioxide production were recorded during a stable 10-30 min period. Results: There was no difference in mean REE measurements between Deltatrac, 1749 ± 389 kcal/24 h and Quark RMR, 1788 ± 494 kcal/24 h (P = 0.166). CCM Express produced 64% higher mean REE values (2876 ± 656 kcal/24 h) than Deltatrac (P < 0.0001). All instruments registered different values for RQ and expiratory minute volume. Conclusion: Available instruments for indirect calorimetry give conflicting estimates of energy expenditure in mechanically ventilated patients. Whilst the Quark RMR compares better with the Deltatrac than CCM Express, the mechanisms behind this difference needs to be further explored.
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Several methods are available to estimate caloric needs in hospitalized, obese patients who require specialized nutrition support; however, it is unclear which of these strategies most accurately approximates the caloric needs of this patient population. The purpose of this study was to determine which strategy most accurately predicts resting energy expenditure in this subset of patients. Patients assessed at high nutrition risk who required specialized nutrition support and met inclusion and exclusion criteria were enrolled in this observational study. Adult patients were included if they were admitted to a medical or surgical service with a body mass index > or = 30 kg/m(2). Criteria excluding patient enrollment were pregnancy and intolerance or contraindication to indirect calorimetry procedures. Investigators calculated estimations of resting energy expenditure for each patient using variations on the following equations: Harris-Benedict, Mifflin-St. Jeor, Ireton-Jones, 21 kcal/kg body weight, and 25 kcal/kg body weight. For nonventilated patients, the MedGem handheld indirect calorimeter was used. For ventilated patients, the metabolic cart was used. The primary endpoint was to identify which estimation strategy calculated energy expenditures to within 10% of measured energy expenditures. The Harris-Benedict equation, using adjusted body weight with a stress factor, most frequently estimated resting energy expenditure to within 10% measured resting energy expenditure at 50% of patients. Measured energy expenditure with indirect calorimetry should be employed when developing nutrition support regimens in obese, hospitalized patients, as estimation strategies are inconsistent and lead to inaccurate predictions of energy expenditure in this patient population.
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Two portable metabolic cart systems of indirect calorimetry (Deltatrac Metabolic Monitor, 2900 MMC System) were validated. CO2 and N2 were delivered at precise rates into a constructed lung model to simulate CO2 production (VCO2) and O2 consumption (VO2). VCO2 (200-400 ml/min) and VO2 (250-750 ml/min) were measured at varying combinations of minute ventilation (VE) (6, 10, 20 liter/min) and FiO2 (0.21, 0.30, 0.60, 0.80). VCO2 was measured with overall errors of 1.5% and 2.4% for the Deltatrac and 2900 monitors, respectively. VO2 was measured with overall errors of 1.9% and 3.2% by the Deltatrac and 2900 monitors, respectively. Both monitors performed equally well for measurement of VO2 at FiO2 up to 0.6, but the Deltatrac had less error for measurements of VO2 at FiO2 of 0.8.