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

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

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

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 Malnutrition: 30% ICU patients (SGA) Existing tools for nutrition screening

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

All ICU patients treated the same

Subjective Global Assessment

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

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

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

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

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

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

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?

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

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 (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

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 1034.4] 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[ 1.0 to 70.0] 50.0[ 1.0 to 100.0] 0.10 % of weight loss in the last 3 month 0.0[ 0.0 to 2.5] 0.0[ 0.0 to 0.0] 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

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 -0.1891 <.0001 598 Baseline APACHE II score -0.3914 Baseline SOFA -0.3857 594 % Oral intake (food) in the week prior to enrollment 0.1676 0.0234 183 number of days in hospital prior to ICU admission -0.1387 0.0007 % of weight loss in the last 3 month -0.1828 0.0130 184 Baseline BMI 0.0581 0.1671 567 # of co-morbidities at baseline -0.0832 0.0420 Baseline CRP -0.1539 0.0002 589 Baseline Procalcitionin -0.3189 582 Baseline IL-6 -0.2908 581 Variables on Vfdays…higher the age, lower the VFdays BMI: no effect on Vent free days

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

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 19.3-48.8 referent 48.9-59.7 0.780 1 59.7-67.4 0.949 67.5-75.3 1.272 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 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.

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.

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

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

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

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?

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

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

Bedside nutrition tool

Thanks Dr. Daren Heyland Xuran Jiang Andrew Day