1. Nutrition Screening and Assessment in Critically ill patients
Rupinder Dhaliwal, RD
Clinical Evaluation Research Unit
Kingston General Hospital

2. 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

3. Does underfeeding in ICUs exist?
Mean intake 56% International Nutrition Survey, n =211 ICUs

4. Purpose of Nutrition Screening
Predict the probability of a better or worse outcome due to nutrition SCREENING
Malnutrition goes undetected

5. Guidelines ASPEN/SCCM 2009

6. 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): ;2003.

7. Underfeeding does occur in ICUs
Malnutrition: 30% ICU patients (SGA)
Existing tools for nutrition screening

8. Nutritional Risk Screening (NRS 2002)
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

9. All ICU patients treated the same

10. Subjective Global Assessment

11. When training provided in advance, SGA can produce reliable estimates of malnutrition Note rates of missing data (7-34%)
2010 article

12. n = 119, > 65 yrs, mostly medical patients, not all ICU
no difference between well-nourished and malnourished patients with regard to the serum protein values on admission, LOS, and mortality rate

13. n = 124, mostly surgical patients 100% data available for SGA
Historical data not always available
n = 124, mostly surgical patients
100% data available for SGA
SGA predicted mortality

14. Quantify Lean Muscle Mass: CT Scan
Body composition tools:
BIA, skin fold: low precision , DEXA: not specific, $$
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!
- Several modalities have been used to study body composition – including BIA, skin-fold – most have poor precision – DXA – high precision, not as specific and not as accessible
CT imaging is becoming a common research tool - powerful in measuring different tissues and their changes over time
M. Mourtzakis et al

15. ICU patients are not all created equal…should we expect the impact of nutrition therapy to be the same across all patients?

16. Malnutrition should be diagnosed on the basis of etiology…
Malnutrition should be diagnosed on the basis of etiology…. inflammation acute vs chronic
Malnutrition should be diagnosed on the basis of etiology…inflammation vs acute vs chronic

17. How do we figure out who will benefit the most from Nutrition Therapy?
In the ICU…..
Caloric debt/underfeeding
Malnutrition exists 34% or >
Historical nutrition data n/a
Not all patients equal
Consider
Inflammation
Acute diseases
Chronic diseases
Need picture of malnourshed child
How do we figure out who will benefit the most from Nutrition Therapy?

18. A Conceptual Model for Nutrition Risk Assessment in the Critically ill
Chronic
Recent weight loss
BMI?
Acute
Reduced po intake
pre ICU hospital stay
Starvation
Nutrition Status
micronutrient levels - immune markers - muscle mass
Inflammation
Linked starvation inflammation and nutritional status to outcomes
Acute
IL-6
CRP
PCT
Chronic
Comorbid illness

19. 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

20. 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 (3 ICUs)
Historical po intake and weight loss only available in 171 patients
Outcome: 28 day vent-free days and mortality
3 ICUs med/sx mix

21. What are the nutritional risk factors associated with mortality
What are the nutritional risk factors associated with mortality? (validation of our candidate variables)
Non-survivors by day 28
(n=138)
Survivors by day 28
(n=460)
p values
Age
71.7 [60.8 to 77.2]
61.7 [49.7 to 71.5]
<.001
Baseline APACHE II score
26.0 [21.0 to 31.0]
20.0 [15.0 to 25.0]
Baseline SOFA
9.0 [6.0 to 11.0]
6.0 [4.0 to 8.5]
# of days in hospital prior to ICU admission
0.9 [0.1 to 4.5]
0.3 [0.0 to 2.2]
Baseline Body Mass Index
26.0 [22.6 to 29.9]
26.8 [23.4 to 31.5]
0.13
Body Mass Index
0.66
<20
6 ( 4.3%)
25 ( 5.4%)
≥20
122 ( 88.4%)
414 ( 90.0%)
# of co-morbidities at baseline
3.0 [2.0 to 4.0]
3.0 [1.0 to 4.0]
<0.001
Co-morbidity
Patients with 0-1 co-morbidity
20 (14.5%)
140 (30.5%)
Patients with 2 or more co-morbidities
118 (85.5%)
319 (69.5%)
C-reactive protein¶
135.0 [73.0 to 214.0]
108.0 [59.0 to 192.0]
0.07
Procalcitionin¶
4.1 [1.2 to 21.3]
1.0 [0.3 to 5.1]
Interleukin-6¶
158.4 [39.2 to ]
72.0 [30.2 to 189.9]
171 patients had data of recent oral intake and weight loss
(n=32)
(n=139)
% Oral intake (food) in the week prior to enrolment
4.0[ to ]
50.0[ to ]
0.10
% of weight loss in the last 3 month
0.0[ to ]
0.0[ to ]
0.06
Based on the conceptual model, we identified variables that predicted mortality. Looked at their affect by survivors vs non surviviors….validated the variables. ALL sign different hence predicted mortality EXCEPT BMI, CRP, oral intake and % wt loss

22. Spearman correlation with VFD within 28 days Number of observations
What are the nutritional risk factors associated with Vent Free days? (validation of our candidate variables)
Variable
Spearman correlation with VFD within 28 days
p values
Number of observations
Age
<.0001
598
Baseline APACHE II score
Baseline SOFA
594
% Oral intake (food) in the week prior to enrollment
0.1676
0.0234
183
number of days in hospital prior to ICU admission
0.0007
% of weight loss in the last 3 month
0.0130
184
Baseline BMI
0.0581
0.1671
567
# of co-morbidities at baseline
0.0420
Baseline CRP
0.0002
589
Baseline Procalcitionin
582
Baseline IL-6
581
Variables on Vfdays…higher the age, lower the VFdays
BMI: no effect on Vent free days

23. The Development of the NUTrition Risk in the Critically ill Score (NUTRIC Score)
% 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
BMI was dichotomized as
<20
all others
Comorbidities was left as integer values range 0-5
Step 2:variables defined and validated, how to move to next stage:
Categorized the groups up and do regression analyses

24. The Development of the NUTrition Risk in the Critically ill Score (NUTRIC Score)
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
referent
0.780 1
0.949
1.272
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
Logistoc regression analyses qunitiles were compared to lowest risk (reference)
The parameters for each logistic regression model estimate the log of the odds ratio (logit) for each category (usually quintile) of the variable compared to the lowest risk (reference) category.
These parameters were rounded to whole numbers to provide the points used in the NUTRIC risk score. Equal point categories were collapsed, and the exact quintile ranges were subsequently rounded to convenient values.

25. The Development of the NUTrition Risk in the Critically ill Score (NUTRIC Score)
Variable
Range
Points
Age
<50
50-<75 1
>=75 2
APACHE II
<15
15-<20
20-28
>=28 3
SOFA
<6
6-<10
>=10
# Comorbidities
0-1 2+
Days from hospital to ICU admit
0-<1 1+
IL6
0-<400
400+
AUC
0.783
Gen R-Squared
0.169
Gen Max-rescaled R-Squared 
0.256
Similary techniques were done for each of the variables
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.

26. higher score = higher mortality
The Validation of the NUTrition Risk in the Critically ill Score (NUTRIC Score)
Statistical modeling
higher score = higher mortality
Further statisitcal modelling to see if the SCORING predicted mortality
Predicted and observed model
HIGHER SCORE = HIGHER MORTALITY

27. high score = longer ventilation
The Validation of the NUTrition Risk in the Critically ill Score (NUTRIC Score)
high score = longer ventilation

28. The Validation of the NUTrition Risk in the Critically ill Score (NUTRIC Score)
Can NUTRIC score modify the association between nutritional adequacy and mortality? (n=211)
Highest score pts,
low nutrition is associated with higher mortality!!
Lowest score pts, more nutrition may be associated with higher mortality ?
P value for the interaction=0.01
P value for the interaction=0.01

29. 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 worse outcomes (mortality, ventilation)
High NUTRIC Score benefit the most from nutrition
Low NUTRIC Score : harmful?

30. 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

31. Limitations Applies only to macronutrients
Does not apply to pharmaconutrients
Nutritional history is suboptimal
Requires IL-6

33. Conclusion Iatrogenic underfeeding in ICUs exist
Nutrition Screening/audits* detect underfeeding
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

34. Bedside nutrition tool

35. Thanks
Dr. Daren Heyland
Xuran Jiang
Andrew Day