Transcript A Randomized Trial of Empiric Antibiotics and Invasive

Rupinder Dhaliwal, RD
Nutrition & Rehabilitation Investigator’s Consortium
Clinical Evaluation Research Unit
Kingston General Hospital
Conflict of interest
 I have received speaker honoraria and/or I have been paid from
grants from the following companies:
– Nestlé Canada
– Fresenius Kabi AG
– Baxter
– Abbott Laboratories
Outline

incidence of underfeeding in the ICU
 nutritional screening tools available for use in ICU
 familiar with the novel approach used to assess the nutritional
risk of critically ill patients and implications of this risk
assessment for clinical practice.
Health Care Associated Malnutrition
“The Skeleton in the Hospital Closet,”
by Charles Butterworth 1974
Does iatrogenic underfeeding exist in the ICU
today?
2007: 158 ICUs, 20 countries, n = 2946 patients
Patients are receiving only 50% prescribed energy and protein needs
Cahill N Crit Care Med 2010
Current Practice in ICUs in 2011
120
% received/prescribed
100
80
60
40
20
0
1
2
3
4
5
6
7
8
9
10
11
12
ICU Day
Mean of All Sites
Best Performing Site
Worst Performing Site
n =211 ICUs, mean intake 56% prescribed calories
Heyland et al INS 2011
kcal
Calorie Debt Associated with worse Outcomes
Prescribed Engergy
2000
1800
1600
1400
1200
1000
800
600
400
200
0
Energy Received From Enteral Feed
Caloric Debt
1
3
5
7
9
11
13
15
17
19
21
Days
 Caloric debt associated with:
 longer ICU stay
 days on mechanical ventilation
 complications
  mortality
Rubinson CCM 2004; Villet Clin Nutr 2005; Dvir Clin Nutr 2006; Petros Clin Nutr 2006
multivariable logistic analysis found critically ill patients with low ED during the first
week of ICU admission to be at greater risk of ICU mortality than those with high ED
(OR 2.43)
Clinical Nutrition 2010
Mechanically vented patients >7days
(average ICU LOS 28 days)
mean deficit < 1200
kcal/day
mean deficit >1200
kcal/day
p =0.01
Faisy BJN 2009;101:1079
Not just about calories!
 113 select ICU patients with sepsis
or burns
 on average, receiving 1,900 kcal/day and
84 grams of protein
 No significant relationship with
energy intake but…
Allingstrup MJ, et al. Clin Nutr. 2012;31(4):462-8.
Quantify Lean Muscle Mass: CT Scan
• CTs becoming common research tool
• Measures tissue mass and changes over time
50 geriatric trauma pts
prevalence of sarcopenia (low
muscularity) on admission 78%
Despite the majority being
overweight!
M. Mourtzakis et al
Purpose of Nutrition Screening
Predict the probability of a better or worse
outcome due to nutrition
SCREENING
Malnutrition
goes
undetected
Guidelines ASPEN/SCCM 2009
Screening leads to Nutritional Care
Hospitals & healthcare organizations should have a policy and a
specific set of protocols for identifying patients at nutritional risk.
The following process is suggested:
»
»
»
»
»
Screening
Assessment
Monitoring & Outcome
Communication
Audit
Kondrup et al. Clin Nutr 22(4):415-421;2003.
 Underfeeding does occur in ICUs
 Prevalence of sarcopenia
• Existing tools for nutrition screening in ICU
Malnutrition Universal Screening Tool (MUST)
Nutritional Risk Screening (NRS 2002)
Mini Nutritional Assessment (MNA)
Short Nutritional Assessment Questionnaire (SNAQ)
Malnutrition Screening Tool (MST)
Subjective Global Assessment (SGA)
Anthony NCP 2008
All ICU patients
treated the same
Subjective Global Assessment
• n = 119, > 65 yrs, mostly medical patients, not all ICU
• 34% patients were malnourished
• serum protein values on admission, LOS, and mortality rate: no differences
between well-nourished and malnourished patients
2010
• When training provided in
advance, SGA can produce
reliable estimates of
malnutrition
• Note rates of missing data
(7-34%)
•n = 124, mostly surgical patients
•100% data available for SGA
•SGA predicted mortality
ICU patients are not all created equal…should
not expect all patients to respond to the same
nutrition therapy
• Point prevalence survey of nutrition practices in ICU’s around
the world conducted Jan. 27, 2007
• Enrolled 2772 patients from 158 ICU’s over 5 continents
• Included ventilated adult patients who remained in ICU >72
hours
Relationship of Caloric Intake, 60 day Mortality and BMI
60
BMI
All Patients
< 20
20-25
25-30
30-35
35-40
>40
Mortality (%)
50
40
30
20
10
0
0
500
1000
1500
Calories Delivered
2000
Malnutrition should be diagnosed on the
basis of etiology…. inflammation acute vs
chronic
In the ICU…..
Caloric & protein debt occurs
Malnutrition exists 34%
Historical data hard to obtain
Not all patients equal
Consider
•Inflammation
•Acute diseases
•Chronic diseases
How do we figure out who will benefit the
most from Nutrition Therapy?
A Conceptual Model for Nutrition Risk
Assessment in the Critically ill
Acute
Chronic
-Reduced po intake
-pre ICU hospital stay
-Recent weight loss
-BMI?
Starvation
Nutrition Status
micronutrient levels - immune markers - muscle mass
Inflammation
Acute
-IL-6
-CRP
-PCT
Chronic
-Comorbid illness
Objective
Develop a score using the variables in the model to
quantify the risk of ICU pts developing adverse
events that may be modified by nutrition
The Development of the NUTrition Risk in the
Critically ill Score (NUTRIC Score)
• When adjusting for age, APACHE II, and SOFA, what
effect of nutritional risk factors on clinical outcomes?
• Multi institutional data base of 598 patients
• Historical po intake and weight loss only available in
171 patients
• Outcome: 28 day vent-free days and mortality
What nutritional risk factors associated with mortality?
validation of candidate variables
Age
Baseline APACHE II score
Baseline SOFA
# of days in hospital prior to ICU admission
Baseline Body Mass Index
Body Mass Index
Non-survivors by day 28
(n=138)
Survivors by day 28
(n=460)
p values
71.7 [60.8 to 77.2]
61.7 [49.7 to 71.5]
<.001
26.0 [21.0 to 31.0]
20.0 [15.0 to 25.0]
<.001
9.0 [6.0 to 11.0]
6.0 [4.0 to 8.5]
<.001
0.9 [0.1 to 4.5]
0.3 [0.0 to 2.2]
<.001
26.0 [22.6 to 29.9]
26.8 [23.4 to 31.5]
0.13
0.66
<20
≥20
6 ( 4.3%)
122 ( 88.4%)
3.0 [2.0 to 4.0]
# of co-morbidities at baseline
Co-morbidity
Patients with 0-1 co-morbidity
20 (14.5%)
Patients with 2 or more co-morbidities
118 (85.5%)
¶
135.0 [73.0 to 214.0]
C-reactive protein
4.1 [1.2 to 21.3]
Procalcitionin¶
¶
158.4 [39.2 to 1034.4]
Interleukin-6
171 patients had data of recent oral intake and weight loss
% Oral intake (food) in the week prior to enrolment
% of weight loss in the last 3 month
25 ( 5.4%)
414 ( 90.0%)
3.0 [1.0 to 4.0]
<0.001
<0.001
140 (30.5%)
319 (69.5%)
108.0 [59.0 to 192.0]
0.07
1.0 [0.3 to 5.1]
<.001
72.0 [30.2 to 189.9]
<.001
Non-survivors by day 28
(n=32)
Survivors by day 28
(n=139)
p values
4.0[ 1.0 to 70.0]
50.0[ 1.0 to 100.0]
0.10
0.0[ 0.0 to
2.5]
0.0[ 0.0 to
0.0]
0.06
Development of NUTRIC Score
categorized variables
• % oral intake in the week prior dichotomized into
– patients who reported less than 100%
– all other patients
• Weight loss was dichotomized as
– patients who reported any weight loss
– all other patients
• BMI was dichotomized as
– <20
– all others
• Comorbidities was left as integer values range 0-5
Development of NUTRIC Score
categorized variables
All other variables (Age, APACHE 2, SOFA, Comorbidities, LOS pre ICU, IL 6)
were categorized into five equal sized groups (quintiles)
Exact quintiles and logistic parameters for age
Exact Quintile
Parameter
Points
19.3-48.8
referent
0
48.9-59.7
0.780
1
59.7-67.4
0.949
1
67.5-75.3
1.272
1
75.4-89.4
1.907
2
Logistic regression analyses
Each quintile compared to lowest risk
category
Rounded off to the nearest whole # to
provide points for the scoring system
The NUTRIC Score
Variable
Age
APACHE II
SOFA
# Comorbidities
Range
<50
50-<75
>=75
<15
15-<20
20-28
>=28
<6
6-<10
>=10
0-1
2+
Points
0
1
2
0
1
2
3
0
1
2
0
1
Days from hospital to ICU admit
0-<1
1+
0
1
IL6
0-<400
400+
0
1
AUC
Gen R-Squared
Gen Max-rescaled R-Squared
0.783
0.169
0.256
BMI, CRP, PCT, weight loss, and oral intake were excluded because they were not significantly
associated with mortality or their inclusion did not improve the fit of the final model.
Validation of NUTRIC Score
Observed
Model-based
20
40
higher
score =
higher
mortality
n=12
n=33
0
1
n=55
n=75
n=90
n=114
n=82
n=72
n=46
n=17
2
3
4
5
6
7
8
9
n=2
0
Mortality Rate (%)
60
80
Does the score predict mortality ?
Nutrition Risk Score
10
Validation of NUTRIC Score
Observed
Model-based
10
8
2
4
6
high score
= longer
ventilation
n=12
n=33
n=55
n=75
n=90
n=114
n=82
n=72
n=46
n=17
n=2
0
1
2
3
4
5
6
7
8
9
10
0
Days on Mechanical Ventilator
12
14
Does the score predict duration of ventilation ?
Nutrition Risk Score
Nutrition adequacy & mortality
Can NUTRIC score modify the
associationbetween
between NUTRIC
nutritionalScore
adequacy
Interaction
and
and
mortality?Adequacy
(n=211)
Nutritional
& mortality (n = 211)*
Highest score pts, more nutrition = associated with lower mortality!!
Lowest score pts, more nutrition: no effect, signal for harm?
Summarize: NUTRIC Score
• NUTRIC Score (0-10) based on
–
–
–
–
–
–
Age
APACHE II
SOFA
# comorbidities
Days in hospital pre ICU
IL 6
• High NUTRIC Score associated with worse outcomes (mortality,
ventilation)
• High NUTRIC Score: benefit the most from nutrition
• Low NUTRIC Score : no effect
Applications of NUTRIC Score
• Help determine which patients will benefit more from nutrition
– Supplemental PN
– Aggressive feeding
– Small bowel feeding
• Design & interpretation of future studies
– Negative studies, non high risk, heterogenous patients
Limitations
•
•
•
•
•
needs to be validated further
applies only to macronutrients
does not apply to pharmaconutrients
nutritional history is suboptimal
requires IL-6
Bedside nutrition tool
Conclusion
• calorie and protein debt (iatrogenic underfeeding) occurs in ICUs
• existing screening tools not helpful in ICU
• not all ICU patients are the same in terms of ‘risk’
• NUTRIC Score is one way to quantify that risk and can be used in
your ICU
• further refinement of this tool will ensure that the right patient gets
nutrition
Thanks
Dr. Heyland
Xuran Jiang
Andrew Day