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Malnutrition, poor food intake, and adverse healthcare outcomes in non-critically ill obese acute care hospital patients

01 Apr 2019-Clinical Nutrition (Churchill Livingstone)-Vol. 38, Iss: 2, pp 759-766

TL;DR: Although malnourished obese experienced significantly adverse health-related outcomes they were least likely to receive additional nutritional support, and BMI alone cannot be used as a surrogate measure for nutritional status and warrants routine nutritional screening for all hospital patients, and subsequent nutritional assessment and support for malnouredished patients.
Abstract: Summary Background & aims Obesity, defined as a BMI ≥ 30 kg/m2, has demonstrated protective associations with mortality in some diseases. However, recent evidence demonstrates that poor nutritional status in critically ill obese patients confounds this relationship. The purpose of this paper is to evaluate if poor nutritional status, poor food intake and adverse health-related outcomes have a demonstrated association in non-critically ill obese acute care hospital patients. Methods This is a secondary analysis of the Australasian Nutrition Care Day Survey dataset (N = 3122), a prospective cohort study conducted in hospitals from Australia and New Zealand in 2010. At baseline, hospital dietitians recorded participants' BMI, evaluated nutritional status using Subjective Global Assessment (SGA), and recorded 24-h food intake (as 0%, 25%, 50%, 75%, and 100% of the offered food). Post-three months, participants' length of stay (LOS), readmissions, and in-hospital mortality data were collected. Bivariate and regression analyses were conducted to investigate if there were an association between BMI, nutritional status, poor food intake, and health-related outcomes. Results Of the 3122 participants, 2889 (93%) had eligible data. Obesity was prevalent in 26% of the cohort (n = 750; 75% females; 61 ± 15 years; 37 ± 7 kg/m2). Fourteen percent (n = 105) of the obese patients were malnourished. Over a quarter of the malnourished obese patients (N = 30/105, 28%) consumed ≤25% of the offered meals. Most malnourished obese patients (74/105, 70%) received standard diets without additional nutritional support. After controlling for confounders (age, disease type and severity), malnutrition and intake ≤25% of the offered meals independently trebled the odds of in-hospital mortality within 90 days of hospital admission in obese patients. Conclusion Although malnourished obese experienced significantly adverse health-related outcomes they were least likely to receive additional nutritional support. This study demonstrates that BMI alone cannot be used as a surrogate measure for nutritional status and warrants routine nutritional screening for all hospital patients, and subsequent nutritional assessment and support for malnourished patients.
Topics: Body mass index (51%), Acute care (51%), Cohort (50%), Malnutrition (50%)

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Original article
Malnutrition, poor food intake, and adverse healthcare outcomes in
non-critically ill obese acute care hospital patients
Ekta Agarwal
a
,
b
,
c
,
*
, Maree Ferguson
a
,
c
, Merrilyn Banks
a
,
d
, Angela Vivanti
a
,
c
,
Marijka Batterham
e
, Judy Bauer
a
, Sandra Capra
a
, Elisabeth Isenring
a
,
b
,
c
a
Centre for Dietetics Research, School of Human Movement and Nutritional Sciences, The University of Queensland, St Lucia, QLD 4072, Australia
b
Master of Nutrition and Dietetic Practice Program, Faculty of Health Sciences and Medicine, Bond University, Robina, QLD 4229, Australia
c
Department of Nutrition and Dietetics, Princess Alexandra Hospital, Woolloongabba, QLD 4102, Australia
d
Department of Nutrition and Dietetics, Royal Brisbane and Women's Hospital, Herston, QLD 4029, Australia
e
National Institute for Applied Statistics Research Australia, The University of Wollongong, Wollongong, NSW 2522, Australia
article info
Article history:
Received 27 September 2017
Accepted 28 February 2018
Keywords:
Body mass index
Malnutrition
Sarcopenic obesity
Food intake
Length of stay
Hospital mortality
summary
Background & aims: Obesity, dened as a BMI 30 kg/m
2
, has demonstrated protective associations with
mortality in some diseases. However, recent evidence demonstrates that poor nutritional status in
critically ill obese patients confounds this relationship. The purpose of this paper is to evaluate if poor
nutritional status, poor food intake and adverse health-related outcomes have a demonstrated associ-
ation in non-critically ill obese acute care hospital patients.
Methods: This is a secondary analysis of the Australasian Nutrition Care Day Survey dataset (N ¼ 3122), a
prospective cohort study conducted in hospitals from Australia and New Zealand in 2010. At baseline,
hospital dietitians recorded participants' BMI, evaluated nutritional status using Subjective Globa l
Assessment (SGA), and recorded 24-h food intake (as 0%, 25%, 50%, 75%, and 100% of the offered food).
Post-three months, participants' length of stay (LOS), readmissions, and in-hospital mortality data were
collected. Bivariate and regression analyses were condu cted to investigate if there were an association
between BMI, nutritional status, poor food intake, and health-related outcomes.
Results: Of the 3122 participants, 2889 (93%) had eligible data. Obesity was prevalent in 26% of the
cohort (n ¼ 750; 75% females; 61 ± 15 years; 37 ± 7 kg/m
2
). Fourteen percent (n ¼ 105) of the obese
patients were malnourished. Over a quarter of the malnourished obese patients (N ¼ 30/105, 28%)
consumed 25% of the offered meals. Most malnourished obese patients (74/105, 70%) received standard
diets without additional nutritional support. After controlling for confounders (age, disease type and
severity), malnutrition and intake 25% of the offered meals independently trebled the odds of in-
hospital mortal ity within 90 days of hospital admission in obese patients.
Conclusion: Although malnourished obese experienced signicantly adverse health-related outcomes
they were least likely to receive additional nutritional support. This study demonstrates that BMI alone
cannot be used as a surrogate measure for nutritional status and warrants routine nutritional screening
for all hospital patients, and subsequent nutritional assessment and support for malnourished pati ents.
Crown Copyright © 2018 Published by Elsevier Ltd. All rights reserved.
1. Introduction
Recently, Cereda and colleagues investigated the association
between BMI and in-hospital mortality from the 2006e2014
combined nutritionDay worldwide dataset including over 97,000
adult patients from hospitals in 51 countries [1]. After controlling
for confounders such as demographics (age, gender), nutritional
factors (history of weight change, food intake in week preceding
data collection), and medical factors (reason for hospitalisation,
surgical procedures performed, intensive care admission, number
of medications) and mobility, researchers found that low BMI
(<18.5 kg/m
2
) was an independent predictor for in-hospital mor-
tality (odds ratio (OR): 1.35, 95% condence interval (CI): 1.20e1.53,
p value < 0.001) [1]. Cereda et al. concluded that overweight and
obesity had protective associations with 30-day in-hospital
* Corresponding author. Faculty of Health Sciences and Medicine, Level 2,
Building 18, Bond Institute of Health and Sport, Bond University, 2 Promethean
Way, Robina, QLD 4229, Australia.
E-mail address: eagarwal@bond.edu.au (E. Agarwal).
Contents lists available at ScienceDirect
Clinical Nutrition
journal homepage: http://www.elsevier.com/locate/clnu
https://doi.org/10.1016/j.clnu.2018.02.033
0261-5614/Crown Copyright © 2018 Published by Elsevier Ltd. All rights reserved.
Clinical Nutrition xxx (2018) 1e8
Please cite this article in press as: Agarwal E, et al., Malnutrition, poor food intake, and adverse healthcare outcomes in non-critically ill obese
acute care hospital patients, Clinical Nutrition (2018), https://doi.org/10.1016/j.clnu.2018.02.033

mortality given that mortality was lowest in patients in the obese
category (BMI 30 kg/m
2
; OR: 0.73, 95% CI: 0.62e0.86, p
value < 0.001) [1].
Despite strong associations with increased healthcare costs and
mortality in healthy populations [2e4], in 2002, Gruberg and col-
leagues noticed that obesity (BMI 30 kg/m
2
) had a protective
association in a cohort of post-percutaneous coronary in-
terventions [5]. Many studies since have demonstrated this phe-
nomenon, known as the obesity paradox or reverse
epidemiology, particularly in cardiovascular and metabolic disease,
some cancers and end-stage renal disease [5,6]. However, studies
demonstrating protective associations between obesity and
improved survival dene obesity using BMI, an inherent limitation
of which is that it does not distinguish lean body mass from fat
mass, which have different implications for health and survival [7].
In a large observational study of critically ill patients (N ¼ 6518)
admitted in medical and surgical ICUs from 2004 to 2011, Robinson
et al. demonstrated that the presence of malnutrition confounded
the positive association between obesity and 30-day in-hospital
mortality [8]. Critically ill obese patients (BMI 30 kg/m
2
) with
malnutrition had greater odds of 30-day in-hospital mortality (OR:
1.58; CI: 1.21e2.07, p ¼ 0.001) than well-nourished counterparts
[8].
Malnutrition is the result of nutritional intake that is inadequate
to support physiological requirements [9]. Several factors can
contribute to inadequate nutritional intake, including physical,
physiological, psychological, and socio-environmental [10].
Evidence-based guidelines support the use of a range of validated
nutrition screening tools (such as Malnutrition Screening Tool
(MST) [11]) and assessment methods (such as Subjective Global
Assessment (SGA) [12]) to identify malnutrition [13]. Further, the
International Classication of Diseases and Related Health Prob-
lems, version 10, Australian modication (ICD-10-AM), denes
malnutrition as BMI < 18.5 kg/m
2
or unintentional weight loss of at
least 5% with evidence of sub-optimal intake resulting in subcu-
taneous fat loss and/or muscle wasting [14].
The Australasian Nutrition Care Day Survey (ANCDS) conducted
in 2010 reported the prevalence of malnutrition, poor food intake
and associated health-related outcomes in over 3000 acute care
patients admitted in 56 hospitals across Australia and New Zealand
[15,16]. Malnutrition was observed in 30% of the cohort and dened
as low BMI (<18.5 kg/m
2
) and moderate/severe malnutrition as
determined by SGA [15]. Food intake observed over a 24-h period
indicated that one-in-four participants consumed no more than
25% of the offered food [15]. After controlling for confounders (age,
disease type and severity, and type of admission), the hazard ratio
of 90-day in-hospital mortality for malnourished patients who
consumed up to a quarter of the offered food was 2.3 times greater
than well-nourished patients (CI: 1.39e3.76, p < 0.001) [16].
The contrasting results from the studies by Cereda et al. [1] and
Robinson et al. [8] prompted this secondary analysis of the ANCDS
dataset with the aim to determine nutritional issues (presence of
malnutrition and poor food intake) and their independent associ-
ation with health-related outcomes specically in obese acute care
patients. This paper will also provide insight on malnutrition cod-
ing and nutrition support offered to not critically ill obese acute
care patients who were malnourished.
2. Methods
2.1. Study design
The ANCDS was a prospective cohort study conducted over two
phases. Phase I (baseline) was conducted in JuneeJuly 2010 [15]
and Phase II was conducted after three months [16].
2.2. Study setting
The ANCDS was conducted in 56 acute care hospitals across
Australia and New Zealand [15,16].
2.3. Study population
Acute care patients aged 18 years of age were invited to
participate in the study by providing written informed consent
[15]. Patients were excluded if they were likely to be discharged or
undergo surgery during the baseline data collection period, were
either terminally ill or undergoing end-of-life palliative care, had
disordered eating, were outpatients or admitted in certain wards
(including maternity and obstetrics, high dependency units,
emergency departments, intensive care units, rehabilitation) [15].
Further details on inclusion and exclusion criteria, patient recruit-
ment and data elements have been previously published [15].
2.4. Ethics
Ethics approval for the ANCDS was provided by the Human
Research Ethics Committees of The University of Queensland and
the participating hospitals [15].
2.5. Data collection
Details on data collection methodology for both phases have
been previously reported [15,16] and a brief summary has been
provided below:
2.5.1. Phase I
Dietitians from participating hospitals recorded participants'
age, gender, self-reported ethnicity, weight and height at baseline
[15]. Using these measurements the rst author calculated each
participants' BMI and then categorised as per WHO classication:
underweight (BMI < 18.5 kg/m
2
), normal weight (18.5 e24.9 kg/
m
2
), overweight (25e29.9 kg/m
2
), class I obese (30e34.9 kg/m
2
),
class II obese (35e39.9 kg/m
2
), and class III obese (40 kg/m
2
) [17].
Dietitians also screened the participants for nutrition risk using the
MST [11]. The MST includes two questions related to appetite and
recent unintentional weight loss and provides a score ranging from
0 to 5, with a score of 2 indicating nutritional risk [11]. Dietitians
used the valid and reliable Subjective Global Assessment (SGA) to
comprehensively assess patients with an MST score 2 to deter-
mine a diagnosis of malnutrition [12]. The SGA is a valid and reliable
measure that considers changes in two components: medical his-
tory (body weight, dietary intake, presence of nutrition impact
symptoms, and functional capacity); and physical examinations
(subcutaneous fat and muscle mass stores) [12]. Results from both
components are combined to provide an overall rating of well-
nourished (SGA-A), moderately malnourished (SGA-B) or severely
malnourished (SGA-C) [12]. Participants who had an MST score of
<2 or were rated as well-nourished (SGA-A) were grouped in the
well-nourished category. In keeping with the International Clas-
sication of Diseases and Related Health Problems, version 10,
Australian modication (ICD-10-AM), malnutrition was dened as
BMI < 18.5 kg/m
2
or unintentional weight loss of at least 5% with
evidence of sub-optimal intake resulting in subcutaneous fat loss
and/or muscle wasting [14]. Therefore, participants with a
BMI < 18.5 kg/m
2
and/or assessed as SGA-B or SGA-C were grouped
in the malnourished category [14].
Dietitians also recorded the type of diet offered to participants
along with observing their food intake over the 24-h data collection
period after each main meal (breakfast, lunch and dinner) and
snack (morning and afternoon tea) [15]. Intake for supper was
E. Agarwal et al. / Clinical Nutrition xxx (2018) 1e82
Please cite this article in press as: Agarwal E, et al., Malnutrition, poor food intake, and adverse healthcare outcomes in non-critically ill obese
acute care hospital patients, Clinical Nutrition (2018), https://doi.org/10.1016/j.clnu.2018.02.033

recorded by visual estimation, nursing records or patient recall the
following morning [15]. Intake was recorded on a ve-point scale
(0%, 25%, 50%, 75%, and 100%) [15]. From a list of possible options,
patients selected their reason/s for not consuming all the offered
food at each main meal and snack [15].
2.5.2. Phase II
Staff members of health information records departments of
participating hospitals compiled their respective participants'
admission-related information 90 days after baseline data collec-
tion [16]. This included admission status, type of admission, clinical
diagnosis, disease severity (as per the Patient Clinical Complexity
Level Scores (PCCL)), and health-related outcomes information
including LOS in hospital at baseline, number of readmissions, and
in-hospital mortality (Table 1) [16].
2.6. Statistical analyses
Data were analysed using IBM SPSS Statistics for Windows
(Release 23.0, 2015; IBM Corp, Armonk, New York). Categorical
variables are presented as frequency and percentage. Continuous
variables were not normally distributed (age, LOS, BMI) and
therefore presented as median and range. Comparisons of pro-
portions were undertaken using Chi-square tests. Comparisons of
means were performed using non-parametric tests.
The dataset le was split to identify variables that demonstrated
signicant associations with outcome variables at a bivariate level
for obese patients (BMI 30 kg/m
2
). These variables were then
incorporated into regression models to identify independent as-
sociations with outcome variables. Survival analysis was conducted
using the KaplaneMeier test to evaluate differences between par-
ticipants that were obese and malnourished versus those who were
non-obese and well-nourished or malnourished. Preliminary
assumption testing was conducted to ensure no violation of the
assumptions, including multicollinearity. High inter-correlations
were observed between diet type and nutritional status, and
therefore diet type was excluded from the regression models. A p-
value < 0.05 was considered statistically signicant.
3. Results
After data cleaning, analyses were completed for 2889 of the
3122 recruited participants (93%) who had complete data.
3.1. Comparison of characteristics within the cohort as per BMI
Over 25% of the cohort were classied as obese (n ¼ 750; Me-
dian BMI: 34 kg/m
2
(range: 30e85 kg/m
2
)) (Table 1). Participants in
the obese category were signicantly younger, had the highest
proportion of females and those who identied themselves as
Maori (p < 0.001) (Table 1).
Obese participants had a signicantly higher proportion of
elective admissions and a signicantly lower proportion of severe/
catastrophic disease severity (p < 0.001) (Table 1).
Malnutrition risk was signicantly lower in obese participants
(p < 0.001) (Table 1). The average prevalence of malnutrition in the
obese group was 14% (n ¼ 105) which was signicantly lower than
other BMI categories (Table 1). In comparison to other BMI cate-
gories, a signicantly greater proportion of patients in the obese
categories consumed 100% of the offered meals during Phase I of
the study (Table 1).
Overweight and obese participants had a signicantly lower LOS
in comparison to participants in other BMI categories (p < 0.001)
(Table 1). There was no signicant difference in readmission rates
and 30-day in-hospital mortality amongst the participants in the
underweight, normal weight, overweight and obese categories
(Table 1). Ninety day in-hospital mortality rates were signicantly
higher in participants in the underweight category and signicantly
lower in participants in the overweight category (p ¼ 0.030)
(Table 1).
3.2. Comparison of food intake and provision of nutritional support
as per nutritional status within BMI categories
When BMI categories were compared as per nutritional status,
one-in-three malnourished participants across all BMI categories
consumed 25% of the offered meals during Phase I of the study
(p < 0.001) (Table 2). Seventy percent of malnourished obese par-
ticipants were offered diets without additional nutritional support
during Phase 1 of the study, which was signicantly higher than
malnourished patients in other BMI categories (p ¼ 0.018) (Table 2).
3.3. Comparison of health-related outcomes as per nutritional
status within BMI categories
Malnourished participants across all BMI categories had signif-
icantly longer median LOS in comparison to their well-nourished
counterparts (p
¼ 0.005) (Table 3). However, sub-group analyses
indicated that malnourished participants in the obese class III
category had the longest median LOS (23 days (range: 3e199),
p ¼ 0.009) (Table 3). There was no signicant difference for read-
missions amongst the participants (p ¼ 0.183) (Table 3). The highest
proportion of 30-day and 90-day in-hospital mortality was
observed in malnourished obese participants (p < 0.001) (Table 3).
3.4. Malnutrition coding
A signicantly lower proportion of malnourished overweight
and obese participants were coded for malnutrition (p < 0.001)
(Table 4).
3.5. Regression analyses
3.5.1. LOS
The multiple regression analysis model explained 26% of the
variance in LOS in obese participants (BMI 30 kg/m
2
;R
2
¼ 0.26,
adjusted R
2
¼ 0.25, F (9, 766) ¼ 29.62, p-value < 0.0001). PCCL
scores were the largest unique contribution (beta: 0.256, CI:
0.929e1.240, p-value < 0.0001). Nutritional status made a signi-
cant contribution (beta: 0.116, CI: 0.283e0.980, p-value < 0.0001).
Percentage food intake made no signicant contribution.
3.5.2. Readmissions
Logistic regression analyses did not nd nutritional status and/
or food intake to be a signicant risk factor for readmissions in
obese participants. Neoplastic disease, discharge to other health-
care facilities, and disease severity were the independent risk fac-
tors that increased the risk of readmissions within 90 days of index
hospitalisation (p < 0.005).
3.5.3. In-hospital mortality
After controlling for confounding factors, consumption of 25%
of the offered food increased the odds of in-hospital mortality
within 30 days of admission by more than 5.5 times (Table 5).
Malnutrition did not have a signicant association with 30-day in-
hospital mortality (Table 5). However, both, malnutrition and
consumption of 25% of the offered food trebled the odds of in-
hospital mortality within 90 days of hospital admission (Table 5).
Malnourished obese patients had signicantly lower survival than
those who were not obese and were either well-nourished or
E. Agarwal et al. / Clinical Nutrition xxx (2018) 1e8 3
Please cite this article in press as: Agarwal E, et al., Malnutrition, poor food intake, and adverse healthcare outcomes in non-critically ill obese
acute care hospital patients, Clinical Nutrition (2018), https://doi.org/10.1016/j.clnu.2018.02.033

Table 1
Characteristics of the ANCDS cohort as per body mass index (N ¼ 2889).
Variable Underweight
a
(n ¼ 227) Normal weight
b
(n ¼ 1048) Overweight
c
(n ¼ 864) Obese
d
(n ¼ 750) p-value
Demographic
Gender
e
Male 106 (47%) 579 (55%) 514 (60%) 340 (46%) 0.000
Female 121 (53%) 468 (45%) 350 (40%) 408 (54%)
Ethnicity
e
Caucasian 190 (86%) 950 (92%) 771 (91%) 643 (87%) 0.000
Aboriginal & Torres Strait Islander 8 (3%) 15 (2%) 21 (2%) 15 (2%)
Maori 3 (1%) 14 (1%) 17 (2%) 46 (6%)
Asian 12 (5%) 25 (2%) 25 (3%) 2 (0.5%)
Other 11 (5%) 29 (3%) 18 (2%) 33 (5%)
Median Age (Range), years 73 (18e99) 72 (18e99) 68 (18e110) 62 (18e95) 0.023
Age
e
<65 years 85 (38%) 394 (38%) 355 (41%) 436 (59%) 0.000
65 years 141 (62%) 650 (62%) 504 (59%) 306 (41%)
Clinical
Admission status
e
Emergency 176 (78%) 789 (75%) 619 (72%) 523 (70%) 0.000
Elective 29 (12%) 204 (20%) 190 (22%) 181 (24%)
Other 22 (10%) 53 (5%) 54 (6%) 46 (6%)
Admission type
e
Surgical 74 (32%) 430 (41%) 397 (46%) 327 (44%) 0.001
Medical 135 (60%) 563 (54%) 412 (48%) 393 (53%)
Other 18 (8%) 52 (5%) 52 (6%) 29 (4%)
Major diagnostic category
e
Circulatory 16 (7%) 133 (12%) 129 (15%) 98 (13%) 0.003
Digestive 39 (17%) 206 (20%) 165 (19%) 139 (19%)
Endocrine 3 (1%) 25 (2%) 24 (3%) 22 (3%)
Musculoskeletal 38 (16%) 152 (15%) 127 (14%) 119 (16%)
Neoplastic 6 (3%) 27 (3%) 38 (4%) 10 (1%)
Nervous 25 (11%) 99 (9%) 70 (8%) 67 (9%)
Renal 8 (4%) 27 (3%) 38 (4%) 32 (4%)
Respiratory 43 (19%) 146 (14%) 82 (9%) 89 (12%)
Other 49 (22%) 230 (22%) 188 (24%) 173 (23%)
Disease severity
e
Not severe 62 (28%) 376 (36%) 353 (41%) 330 (44%) 0.000
Severe/catastrophic 163 (72%) 668 (64%) 504 (59%) 417 (56%)
Nutritional
Median BMI (kg/m
2
, range) 17 (10e18.4) 22 (18.5e24.9) 27 (25e29.9) 34 (30e85) 0.000
Malnutrition risk
e,f
Not at risk of malnutrition 72 (32%) 516 (49%) 566 (66%) 547 (73%) 0.000
At risk of malnutrition 152 (68%) 531 (51%) 292 (34%) 201 (27%)
SGA
e
A (well-nourished)
g
10 (7%) 116 (22%) 105 (36%) 89 (12%) 0.000
B (moderately malnourished) 80 (53%) 341 (64%) 162 (55%) 101 (14%)
C (severely malnourished) 60 (40%) 67 (14%) 19 (9%) 4 (3%)
Overall nutritional status
e
Well-nourished
g
0 632 (61%) 671 (79%) 636 (86%) 0.000
Malnourished
h
226 (100%) 408 (39%) 181 (20%) 105 (14%)
Food intake
e
0% 31 (14%) 108 (10%) 71 (8%) 31 (8%) 0.000
25% 41 (18%) 151 (15%) 110 (13%) 42 (10%)
50% 54 (24%) 218 (21%) 162 (19%) 74 (18%)
75% 75 (25%) 295 (28%) 231 (27%) 110 (27%)
100% 41 (18%) 265 (26%) 286 (33%) 158 (38%)
Health-related outcomes
Length of stay (LOS; days (range)) 16 (2e245) 13 (2e395) 11 (2e467) 11 (2e224) 0.000
Readmissions
e
77 (34%) 338 (32%) 273 (32%) 247 (33%) 0.896
In-hospital mortality
e
Within 30 days
i
6 (3%) 20 (2%) 9 (1%) 13 (2%) 0.300
Within 90 days
i
13 (6%) 28 (2.5%) 14 (1.5%) 18 (3%) 0.007
Note: Reported percentage values indicate proportion of participants within the BMI category.
a
BMI: <18.5 kg/m
2
.
b
BMI: 18.5e24.9 kg/m
2
.
c
BMI: 25e29.9 kg/m
2
.
d
BMI: 30 kg/m
2
[34].
e
Presented as n (%).
f
Malnutrition Risk assessed using Malnutrition Screening Tool (MST) [11].
g
Includes SGA-A [12] and MST < 2 [11].
h
Includes moderate (SGA-B) and severe (SGA-C) malnutrition [12], and BMI < 18.5 kg/m
2
[14].
i
Within 30 or 90 days of hospital admission.
E. Agarwal et al. / Clinical Nutrition xxx (2018) 1e84
Please cite this article in press as: Agarwal E, et al., Malnutrition, poor food intake, and adverse healthcare outcomes in non-critically ill obese
acute care hospital patients, Clinical Nutrition (2018), https://doi.org/10.1016/j.clnu.2018.02.033

malnourished (p ¼ 0.043). After controlling for potential con-
founders, the hazard ratio of 90-day in-hospital mortality for
malnourished obese patients who also consumed 25% of the
offered food was 2.9 times greater (CI: 1.13e7.54, p ¼ 0.027) than
well-nourished obese patients who ate >25% of the offered food
(Fig. 1).
4. Discussion
The aims of the present paper were to determine if malnutrition
and poor food intake exists in obese, non-critically ill acute care
patients and the independent association of these nutritional issues
with health-related outcomes. In comparison to other BMI cate-
gories, the prevalence of malnutrition, poor food intake, and risk of
adverse outcomes was signicantly lower in obese participants.
However, when BMI categories were further classied by nutri-
tional status as assessed by SGA, malnourished obese patients were
least likely to be offered diets with additional nutritional support
and experienced the highest in-hospital mortality in comparison to
all other participants. Malnourished obese participants who also
consumed a quarter or less of the offered meals were three times
more likely to experience 90-day in-hospital mortality in compar-
ison to well-nourished obese patients who consumed at least half
the offered meals. Therefore, these results highlight the limitation
of using BMI as a surrogate measure for nutritional status and
emphasise the importance of validated nutrition screening and
assessment methods to routinely determine nutritional status in
acute care hospital patients.
Table 2
Food intake and diets without additional nutritional support as per nutritional status within BMI categories (N ¼ 2889).
Variable Underweight
a
(n ¼ 227)
Normal weight
b
(n ¼ 1048)
Overweight
c
(n ¼ 864)
Obese
d
(n ¼ 750)
p-value
WN
e
(n ¼ 0)
MN
f
(n ¼ 227)
WN
e
(n ¼ 617)
MN
f
(n ¼ 401)
WN
e
(n ¼ 655)
MN
f
(n ¼ 175)
WN
e
(n ¼ 636)
MN
f
(n ¼ 105)
25% food intake 0 72 (32%) 134 (22%) 124 (31%) 122 (18%) 55 (30%) 90 (14%) 30 (29%) 0.000
Diets without additional nutritional support 0 134 (59%) 504 (82%) 239 (60%) 568 (87%) 118 (67%) 288 (87%) 49 (70%) 0.021
Note: Reported percentage values indicate proportion of participants within the BMI category.
WN: well-nourished; MN: Malnourished.
a
BMI: <18.5 kg/m
2
.
b
BMI: 18.5e24.9 kg/m
2
.
c
BMI: 25e29.9 kg/m
2
.
d
BMI: 30 kg/m
2
[34].
e
Includes SGA-A [12] and MST < 2 [11].
f
Includes moderate (SGA-B) and severe (SGA-C) malnutrition [12], and BMI < 18.5 kg/m
2
[14].
Table 3
Health-related outcomes as per body mass index (BMI) and nutritional status (N ¼ 2889).
Variable Underweight
a
(n ¼ 227)
Normal weight
b
(n ¼ 1048)
Overweight
c
(n ¼ 864)
Obese
d
(n ¼ 750)
p-value
WN
e
(n ¼ 0)
MN
f
(n ¼ 227)
WN
e
(n ¼ 617)
MN
f
(n ¼ 401)
WN
e
(n ¼ 655)
MN
f
(n ¼ 175)
WN
e
(n ¼ 636)
MN
f
(n ¼ 105)
LOS (days (range)) e 16 (2e245) 12 (2e395) 16 (2e259) 10 (2e291) 17 (2e467) 10 (2e222) 16 (2e224) 0.005
Readmission e 76 (34%) 187 (30%) 148 (36%) 200 (30%) 67 (37%) 203 (32%) 42 (40%) 0.062
In-hospital mortality within 30 days
g
e 6 (3%) 9 (1.5%) 11 (3%) 5 (1%) 3 (2%) 8 (1%) 5 (5%) 0.027
In-hospital mortality within 90 days
g
e 13 (6%) 12 (2%) 16 (4%) 6 (1%) 7 (4%) 10 (2%) 8 (8%) 0.000
Note: Reported percentage values indicate proportion of participants within the BMI category.
LOS: Length of stay; MN: Malnourished; WN: Well-nourished.
a
BMI: <18.5 kg/m
2
.
b
BMI: 18.5e24.9 kg/m
2
.
c
BMI: 25e29.9 kg/m
2
.
d
BMI: 30 kg/m
2
[34].
e
Includes SGA-A [12] and MST < 2 [11].
f
Includes moderate (SGA-B) and severe (SGA-C) malnutrition [12], and BMI < 18.5 kg/m
2
[14].
g
Within 30 or 90 days of hospital admission.
Table 4
Malnutrition coding in malnourished participants as per body mass index (BMI).
Malnutrition coding Underweight
a
Malnourished
e
(n ¼ 227)
Normal weight
b
Malnourished
e
(n ¼ 401)
Overweight
c
Malnourished
e
(n ¼ 175)
Obese
d
Malnourished
e
(n ¼ 105)
p-value
Not coded 181 (82%) 322 (79%) 161 (90%) 92 (88%) 0.000
Coded 39 (18%) 83 (21%) 17 (10%) 10 (10%)
Note: Reported percentage values indicate proportion of participants within the BMI category.
a
BMI: <18.5 kg/m
2
.
b
BMI: 18.5e24.9 kg/m
2
.
c
BMI: 25e29.9 kg/m
2
.
d
BMI: 30 kg/m
2
[34].
e
Includes moderate (SGA-B) and severe (SGA-C) malnutrition [12], and BMI < 18.5 kg/m
2
[14].
E. Agarwal et al. / Clinical Nutrition xxx (2018) 1e8 5
Please cite this article in press as: Agarwal E, et al., Malnutrition, poor food intake, and adverse healthcare outcomes in non-critically ill obese
acute care hospital patients, Clinical Nutrition (2018), https://doi.org/10.1016/j.clnu.2018.02.033

Citations
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Journal ArticleDOI
Abstract: Summary Background Nutrition screening and assessment tools often include body mass index (BMI) as a component in identifying malnutrition risk. However, rising obesity levels will impact on the relevancy and applicability of BMI cut-off points which may require re-evaluation. This study aimed to explore the relationship between commonly applied BMI cut-offs and diagnosed malnutrition. Methods Data (age, gender, BMI and Subjective Global Assessment (SGA) ratings) were analysed for 1152 inpatients aged ≥65 years across annual malnutrition audits (2011–2015). The receiver operation characteristic (ROC) curve analysed the optimal BMI cut-off for malnutrition and concurrent validity of commonly applied BMI cut-offs in nutritional screening and assessment tools. Results Malnutrition prevalence was 36.0% (n = 372) using SGA criteria (not malnourished, moderate or severe malnutrition). Median age was 78.7 (IQR 72–85) years, median BMI 25.4 (IQR 21.8–29.7) kg/m2; 52.1% male and 51.2% overweight/obese. ROC analysis identified an optimal BMI cut-off of Conclusions Both malnutrition and overweight/obesity are common in older inpatients. Continuing increases in the prevalence of overweight and obesity will impact on the sensitivity of BMI as a screening component for malnutrition risk. The current study suggests tools developed over a decade ago may need to be revisited in future.

11 citations


Journal ArticleDOI
Arved Weimann1Institutions (1)
30 Nov 2019
TL;DR: Assessment and monitoring of functional and nutritional status should be routinely performed in Enhanced Recovery after Surgery (ERAS) programs in order to increase patient compliance.
Abstract: In order to increase patient compliance in Enhanced Recovery after Surgery (ERAS) programs, assessment and monitoring of functional and nutritional status should be routinely performed. Sarcopenic obesity is frequently underestimated and has been shown to be a significant risk factor for the development of postoperative complications. With special regard to gastrointestinal cancer patients undergoing neoadjuvant treatment, nutritional deficiencies may develop stepwise and increase during therapy. In the case of proven deficits, recent strategies including "prehabilitation" focus on making the patient fit for an ERAS program. Evidence-based guidelines for perioperative nutrition therapy have been available.

8 citations


Kelti Hope1, Maree Ferguson2, Maree Ferguson3, Dianne P. Reidlinger4  +1 moreInstitutions (4)
01 Mar 2017
TL;DR: Multilevel and multidisciplinary interventions based on a shared understanding of food and nutrition as an important component of hospital care are essential to improve dietary intake and reduce the risk of adverse clinical outcomes.
Abstract: Background Inadequate dietary intake is a common problem amongst older acute-care patients and has been identified as an independent risk factor for in-hospital mortality. This study aimed to explore whether food and mealtime experiences contribute to inadequate dietary intake in older people during hospitalisation. Methods This was a qualitative phenomenological study, data for which were collected using semi-structured interviews over a three-week period. During this time, 26 patients aged 65 years or more, admitted to medical and surgical wards in a tertiary acute-care hospital, were asked to participate if they were observed to eat less than half of the meal offered at lunch. Participants provided their perspectives on food and mealtimes in hospital. Responses were recorded as hand-written notes, which were agreed with the interviewee, and analysed thematically using the framework method. Results Twenty-five older people were interviewed across six wards. Two main themes, ‘validating circumstances’ and ‘hospital systems’, were identified. Each theme had several sub-themes. The sub-themes within validating circumstances included ‘expectations in hospital’, ‘prioritising medical treatment’, ‘being inactive’, and ‘feeling down’. Those within ‘hospital systems’ were ‘accommodating inconvenience’, ‘inflexible systems’, and ‘motivating encouragement’. Conclusion Inadequate dietary intake by older hospital patients is complex and influenced by a range of barriers. Multilevel and multidisciplinary interventions based on a shared understanding of food and nutrition as an important component of hospital care are essential to improve dietary intake and reduce the risk of adverse clinical outcomes. Improving awareness of the importance of food for recovery amongst hospitalised older people and healthcare staff is a priority.

5 citations


Journal ArticleDOI
TL;DR: This research analyzes the different trends of each BMI category and finds that BMI ≥ 25 has the highest AUC and Predictive accuracy compares to other BMI, which claims a good rank of performance.
Abstract: Obesity is the most common chronic disease, due to its ignorance in society. It gives birth to other diseases such as endocrine. The objective of this research is to analyze the different trends of each BMI category and predict its related serious consequences. Data mining based Support Vector Machine (SVM) technique has been applied for this and the accuracy of each BMI category has been calculated using Receiver Operating Characteristics (ROC), which is an effective method and potentially applied to medical data sets. The Area Under Curve (AUC) of ROC and predictive accuracy have been calculated for each classified BMI category. Our analysis shows interesting results and it is found that BMI ≥ 25 has the highest AUC and Predictive accuracy compares to other BMI, which claims a good rank of performance. From our trends, it has been explored that at each BMI precaution is mandatory even if the BMI < 18.5 and at ideal BMI too. Development of effective awareness, early monitoring and interventions can prevent its harmful effects on health.

4 citations


Cites background from "Malnutrition, poor food intake, and..."

  • ...BMI has a potential relationship with underweight and declares as malnutrition, which results in severe health issues such as mental illness, health-related behaviors, and other biological risk factors [52,53]....

    [...]

  • ...99 states that the person is now acknowledged as overweight but not obese [12,53]....

    [...]

  • ...Secondly, if the person does not take a nutritious healthy diet, a person may suffer from underweight due to the decline in BMI [53]....

    [...]


Journal ArticleDOI
TL;DR: Older adults in Indian acute care hospitals have a noticeable prevalence of malnutrition risk and poor food intake and there is an opportunity for future research to focus on identifying and managing nutritional issues.
Abstract: Aim Current literature regarding the prevalence and consequences of poor dietary intake and risk of malnutrition in older adults is limited to wealthier regions including the United States, Europe and Australasia. With a rapidly ageing population in India, this prospective observational study aimed to evaluate hospital food intake and malnutrition risk and their impact on hospital length of stay, readmission rates and in-hospital mortality of older adults in Indian hospitals. Methods Data collected during nutritionDay worldwide audits (2014-2016), in five urban, private hospitals in India included baseline demographic and clinical data on patients aged ≥60 years. Proportion of food consumed at one main meal was recorded and data on length of stay, readmissions and in-hospital mortality were collected 30 days post-baseline. Results A total of 262 participants (mean age: 69 ± 8 years; 65% males) were recruited. Mapped malnutrition risk (mapped Malnutrition Screening Tool [mMST] score ≥ 2) on admission was 31% and increased to 44% during the course of hospitalisation. Over one quarter of participants consumed ≤50% of their meal (28%). Over half the participants were found to be eating poorly (59%) and those identified as at risk of malnutrition were not offered additional nutrition support. The median LOS was 8 days (range: 1-92), 30-day readmission rates were 7% and in-hospital mortality was 0.4%. Malnutrition risk and poor food intake were not associated with health-related outcomes. Conclusion Older adults in Indian acute care hospitals have a noticeable prevalence of malnutrition risk and poor food intake. There is an opportunity for future research to focus on identifying and managing nutritional issues.

3 citations


References
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Journal ArticleDOI
Marie Ng1, Tom P Fleming1, Margaret Robinson1, Blake Thomson1  +138 moreInstitutions (71)
TL;DR: The global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013 is estimated using a spatiotemporal Gaussian process regression model to estimate prevalence with 95% uncertainty intervals (UIs).
Abstract: Summary Background In 2010, overweight and obesity were estimated to cause 3·4 million deaths, 3·9% of years of life lost, and 3·8% of disability-adjusted life-years (DALYs) worldwide. The rise in obesity has led to widespread calls for regular monitoring of changes in overweight and obesity prevalence in all populations. Comparable, up-to-date information about levels and trends is essential to quantify population health effects and to prompt decision makers to prioritise action. We estimate the global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013. Methods We systematically identified surveys, reports, and published studies (n=1769) that included data for height and weight, both through physical measurements and self-reports. We used mixed effects linear regression to correct for bias in self-reports. We obtained data for prevalence of obesity and overweight by age, sex, country, and year (n=19 244) with a spatiotemporal Gaussian process regression model to estimate prevalence with 95% uncertainty intervals (UIs). Findings Worldwide, the proportion of adults with a body-mass index (BMI) of 25 kg/m 2 or greater increased between 1980 and 2013 from 28·8% (95% UI 28·4–29·3) to 36·9% (36·3–37·4) in men, and from 29·8% (29·3–30·2) to 38·0% (37·5–38·5) in women. Prevalence has increased substantially in children and adolescents in developed countries; 23·8% (22·9–24·7) of boys and 22·6% (21·7–23·6) of girls were overweight or obese in 2013. The prevalence of overweight and obesity has also increased in children and adolescents in developing countries, from 8·1% (7·7–8·6) to 12·9% (12·3–13·5) in 2013 for boys and from 8·4% (8·1–8·8) to 13·4% (13·0–13·9) in girls. In adults, estimated prevalence of obesity exceeded 50% in men in Tonga and in women in Kuwait, Kiribati, Federated States of Micronesia, Libya, Qatar, Tonga, and Samoa. Since 2006, the increase in adult obesity in developed countries has slowed down. Interpretation Because of the established health risks and substantial increases in prevalence, obesity has become a major global health challenge. Not only is obesity increasing, but no national success stories have been reported in the past 33 years. Urgent global action and leadership is needed to help countries to more effectively intervene. Funding Bill & Melinda Gates Foundation.

7,968 citations


Journal ArticleDOI
01 May 2014
TL;DR: There is substantial global variation in the relative burden of stroke compared with IHD, and the disproportionate burden from stroke for many lower-income countries suggests that distinct interventions may be required.
Abstract: Background—Although stroke and ischemic heart disease (IHD) have several well-established risk factors in common, the extent of global variation in the relative burdens of these forms of vascular disease and reasons for any observed variation are poorly understood. Methods and Results—We analyzed mortality and disability-adjusted life-year loss rates from stroke and IHD, as well as national estimates of vascular risk factors that have been developed by the World Health Organization Burden of Disease Program. National income data were derived from World Bank estimates. We used linear regression for univariable analysis and the Cuzick test for trends. Among 192 World Health Organization member countries, stroke mortality rates exceeded IHD rates in 74 countries (39%), and stroke disability-adjusted life-year loss rates exceeded IHD rates in 62 countries (32%). Stroke mortality ranged from 12.7% higher to 27.2% lower than IHD, and stroke disability-adjusted life-year loss rates ranged from 6.2% higher to 10.2% lower than IHD. Stroke burden was disproportionately higher in China, Africa, and South America, whereas IHD burden was higher in the Middle East, North America, Australia, and much of Europe. Lower national income was associated with higher relative mortality (P 0.001) and burden of disease (P 0.001) from stroke. Diabetes mellitus prevalence and mean serum cholesterol were each associated with greater relative burdens from IHD even after adjustment for national income. Conclusions—There is substantial global variation in the relative burden of stroke compared with IHD. The disproportionate burden from stroke for many lower-income countries suggests that distinct interventions may be required. (Circulation. 2011; 124:314-323.)

6,287 citations


Journal ArticleDOI
TL;DR: It is concluded that SGA can easily be taught to a variety of clinicians (residents, nurses), and that this technique is reproducible.
Abstract: Presented and described in detail is a clinical technique called subjective global assessment (SGA), which assesses nutritional status based on features of the history and physical examination. Illustrative cases are presented. To clarify further the nature of the SGA, the method was applied before gastrointestinal surgery to 202 hospitalized patients. The primary aim of the study was to determine the extent to which our clinician's SGA ratings were influenced by the individual clinical variables on which the clinicians were taught to base their assessments. Virtually all of these variables were significantly related to SGA class. Multivariate analysis showed that ratings were most affected by loss of subcutaneous tissue, muscle wasting, and weight loss. A high degree of interobserver agreement was found (kappa = 0.78, 95% confidence interval 0.624 to 0.944, p less than 0.001). We conclude that SGA can easily be taught to a variety of clinicians (residents, nurses), and that this technique is reproducible.

2,591 citations


"Malnutrition, poor food intake, and..." refers background or methods in this paper

  • ...9 kg/m2; dBMI: ≥30kg/m2 (34); g presented as n(%); hMalnutrition Risk assessed using Malnutrition Screening Tool (MST) (11); iincludes SGA-A (12) and MST<2(11); jincludes moderate (SGA-B) and severe (SGA-C) malnutrition (12), and BMI < 18....

    [...]

  • ...Dietitians used the valid and 178 reliable Subjective Global Assessment (SGA) to comprehensively assess patients 179 with an MST score ≥ 2 to determine a diagnosis of malnutrition (12)....

    [...]

  • ...(SGA) (12)) to identify malnutrition (13)....

    [...]

  • ...Participants who had an MST score of 2 or were rated 186 as well-nourished (SGA-A) were grouped in the “well-nourished” category....

    [...]

  • ...Results from both components are combined to provide an overall rating 184 of well-nourished (SGA-A), moderately malnourished (SGA-B) or severely 185 malnourished (SGA-C) (12)....

    [...]


Journal ArticleDOI
Rebecca M. Puhl1, Chelsea A. Heuer1Institutions (1)
01 May 2009-Obesity
TL;DR: This review expands upon previous findings of weight bias in major domains of living, documents new areas where weight bias has been studied, and highlights ongoing research questions that need to be addressed to advance this field of study.
Abstract: Obese individuals are highly stigmatized and face multiple forms of prejudice and discrimination because of their weight (1,2). The prevalence of weight discrimination in the United States has increased by 66% over the past decade (3), and is comparable to rates of racial discrimination, especially among women (4). Weight bias translates into inequities in employment settings, health-care facilities, and educational institutions, often due to widespread negative stereotypes that overweight and obese persons are lazy, unmotivated, lacking in selfdiscipline, less competent, noncompliant, and sloppy (2,5–7). These stereotypes are prevalent and are rarely challenged in Western society, leaving overweight and obese persons vulnerable to social injustice, unfair treatment, and impaired quality of life as a result of substantial disadvantages and stigma. In 2001, Puhl and Brownell published the first comprehensive review of several decades of research documenting bias and stigma toward overweight and obese persons (2). This review summarized weight stigma in domains of employment, health care, and education, demonstrating the vulnerability of obese persons to many forms of unfair treatment. Despite evidence of weight bias in important areas of living, the authors noted many gaps in research regarding the nature and extent of weight stigma in various settings, the lack of science on emotional and physical health consequences of weight bias, and the paucity of interventions to reduce negative stigma. In recent years, attention to weight bias has increased, with a growing recognition of the pervasiveness of weight bias and stigma, and its potential harmful consequences for obese persons. The aim of this article is to provide an update of scientific evidence on weight bias toward overweight and obese adults through a systematic review of published literature since the 2001 article by Puhl and Brownell. This review expands upon previous findings of weight bias in major domains of living, documents new areas where weight bias has been studied, and highlights ongoing research questions that need to be addressed to advance this field of study. A systematic literature search of studies published between January 2000 and May 2008 was undertaken on computerized psychological, medical, social science, sport, and education databases including PsycINFO, PubMed, SCOPUS, ERIC, and SPORTDiscus. The following keyword combinations were used: weight, obese, obesity, overweight, BMI, fat, fatness, size, heavy, large, appearance, big, heavyweight, bias, biased, discrimination, discriminatory, discriminate, stigma, stigmatized, stigmatization, prejudice, prejudicial, stereotype(s), stereotypical, stereotyping, victimization, victimize(d), blame(d), blaming, shame(d), shaming, teasing, tease(d), unfair, bully, bullying, harassment, assumptions, attributions, education, health, health care, sales, employment, wages, promotion, adoption, jury, customer service, housing, media, television. Reference lists of retrieved articles and books were also reviewed, and manual searches were conducted in the databases and journals for authors who had published in this field. Most studies retrieved for this review were published in the United States. Any articles published internationally are noted with their country of origin. Research on weight stigma in adolescents and children was excluded from this review, as this literature was recently reviewed elsewhere (8). Unpublished manuscripts and dissertations were also excluded. In addition, issues pertaining to measurement of weight stigmatization, and demographic variables affecting vulnerability to weight bias such as gender, age, race, and body weight are not addressed in this review. This article instead primarily reviews the evidence of specific areas where weight bias occurs toward adults and its consequences for those affected. This article is organized similarly to the first review published by Puhl and Brownell (2), with sections on weight bias in settings of employment, health care, and education. New sections have been added including weight bias in interpersonal relationships and the media, as well as psychological and physical health consequences of weight bias, and the status of stigma-reduction research. As with the 2001 article, this review also provides an update on legal initiatives to combat weight discrimination, and outlines specific questions for future research.

2,460 citations


"Malnutrition, poor food intake, and..." refers background in this paper

  • ...Variable Underweighta (n=227) Normal weightb (n=1048) Overweightc (n=864) Obesed (n= 750) p-value WNg (n=0) MNh (n=227) WNg (n=617) MNh (n=401) WNg (n=655) MNh (n=175) WNg (n=636) MNh (n=105) LOS (days (range)) 16 (2-245) 12 (2-395) 16 (2-259) 10 (2-291) 17 (2-467) 10 (2-222) 16 (2-224) 0....

    [...]

  • ...Health-related outcomes Length of stay (LOS; days (Range)) 16 (2-245) 13 (2-395) 11 (2-467) 11 (2-224) 0....

    [...]

  • ...Median Age (Range), years 73 (18-99) 72 (18-99) 68 (18-110) 62 (18-95) 0....

    [...]

  • ...A review by Puhl 346 and Heuer (2013) concluded that negative and biased attitudes towards obesity, and subsequent 347 inequities with treatment provision have been reported amongst healthcare professionals 348 including physicians, nurses, allied health staff members and students-in-training (28)....

    [...]


Journal ArticleDOI
Tommy Cederholm1, Ingvar Bosaeus2, Rocco Barazzoni3, Juergen M. Bauer4  +9 moreInstitutions (12)
TL;DR: In individuals identified by screening as at risk of malnutrition, the diagnosis of malnutrition should be based on either a low BMI (<18.5 kg/m(2)), or on the combined finding of weight loss together with either reduced BMI (age-specific) or a low FFMI using sex-specific cut-offs.
Abstract: summary Objective: To provide a consensus-based minimum set of criteria for the diagnosis of malnutrition to be applied independent of clinical setting and aetiology, and to unify international terminology. Method: The European Society of Clinical Nutrition and Metabolism (ESPEN) appointed a group of clinical scientists to perform a modified Delphi process, encompassing e-mail communications, face-toface meetings, in group questionnaires and ballots, as well as a ballot for the ESPEN membership. Result: First, ESPEN recommends that subjects at risk of malnutrition are identified by validated screening tools, and should be assessed and treated accordingly. Risk of malnutrition should have its own ICD Code. Second, a unanimous consensus was reached to advocate two options for the diagnosis of malnutrition. Option one requires body mass index (BMI, kg/m

865 citations


"Malnutrition, poor food intake, and..." refers background in this paper

  • ...Variable Underweighta (n=227) Normal weightb (n=1048) Overweightc (n=864) Obesed (n= 750) p-value WNg (n=0) MNh (n=227) WNg (n=617) MNh (n=401) WNg (n=655) MNh (n=175) WNg (n=636) MNh (n=105) LOS (days (range)) 16 (2-245) 12 (2-395) 16 (2-259) 10 (2-291) 17 (2-467) 10 (2-222) 16 (2-224) 0....

    [...]

  • ...Health-related outcomes Length of stay (LOS; days (Range)) 16 (2-245) 13 (2-395) 11 (2-467) 11 (2-224) 0....

    [...]

  • ...Median Age (Range), years 73 (18-99) 72 (18-99) 68 (18-110) 62 (18-95) 0....

    [...]

  • ...involuntary weight loss, body composition analyses, and measurement of functional strength 379 and capacity are better indicators of malnutrition (32, 33)....

    [...]