SMILEKids : Simple Malnutrition indicators for Improving nutritionaL Elements in hospitalized Kids

Thanit Vinitchagoon, B.Sc., CDT. Clinical Dietitian, Nutrition Support Department

Bumrungrad International Hospital

R2R to Transformation #22072015 “Creating knowledge with quality and safety”

Introduction There are problems everywhere, it’s just you see it or not.

Malnutrition is a common problem in hospitalized

pediatric patients ...

Prevalence of malnutrition in hospitalized pediatric patients

(Koen FM, et al. Curr Opin Pediatr. 2008.) (Tienboon P. Asia Pac J Clin Nutr. 2002.)

1 Aurangzeb B et al. Clin Nutr. 2012. 2 de Menezes FS et al. Nutrition. 2012. 3 Leite HP et al. JPEN. 2012.

• Longer length of stay in the hospital1 (> 3 days)

17.8% vs 38.7% (p=.0001) • Longer use of ventilation2

OR=1.76 (1.08-2.88) (p=.024) • Increased mortality3

OR=3.98 (1.14-13.94) (p=.03)

... which affects clinical outcomes including …

To prevent ,especially, hospital-acquired malnutrition, the risk needs to be identified

at the time of admission so that appropriate nutrition intervention

can be initiated early ...

Nutrition Assessment Nutrition Screening

(Joosten KF et al. Curr Opin Pediatr. 2008.)

Population

... in which nutrition screening and assessment

play a big role.

There are several nutrition assessment tools being

developed and published ...

(Moeeni V et al. Int J Child Health Nutr. 2012.)

Nutrition Risk Score (NRS)

Simple Pediatric Nutritional Risk Score (SPNRS)

Screening Tool for Assessment of Malnutrition in Pediatrics (STAMP)

Pediatric Yorkhill Malnutrition Score (PYMS)

Screening Tool for Risk On Nutritional Status and Growth (STRONGkids)

Subjective Global Nutritional Assessment for Children (SGNA)

Both adult and pediatric populations (Relly HM et al. Clin Nutr. 1995.)

Pediatric patients aged > 1 month (Sermet-Gaudelus I et al. Am J Clin Nutr. 2000.)

Pediatric patients aged 2-17 years (McCarthy H et al. J Hum Nutr Diet. 2008.)

Pediatric patients aged 1 month – 16 years (Gerasimidis K et al. Br J Nutr. 2010.)

Pediatric patients aged 1 month – 16 years (Hulst JM et al. Clin Nutr. 2010.)

Pediatric patients aged 30 days – 17.9 years (Secker DJ et al. Am J Clin Nutr. 2012.)

... but there is no gold standard for nutrition assessment tool in

pediatric population yet.

Why Private Hospital? Clickpoints of starting R2R

Due to JCI standards , nutrition screening and assessment need to be routinely done by healthcare

professionals in which ...

Nurses = nutrition screening Dietitians = nutrition assessment

Usual pediatric nutrition assessment tool in BI Hospital ...

... was not able to detect significant differences in anthromopetry ...

Tested using One-way ANOVA with variances of the distributions equal No statistical difference at p < .05 (n=59)

... nor clinical parameters.

Tested using Welch statistic due to unequal variances of the distributions No statistical difference at p < .05 (n=59)

Therefore, our pediatric nutrition assessment tool needs to be revised due

to 4 reasons which are ...

• Several nutritional parameters not available ex. Height for age, weight for age, etc.

• Several nutritional parameters not applicable ex. Ideal body weight, body mass index, etc.

• Clinical outcomes not correlated with nutritional status from the assessment

• No evidences support the use of current form

So we decide to ... develop nutrition assessment tool for hospitalized pediatric patients in BIH which is more precise and

accurate than the usual form and be able to detect patients at risk

of malnutrition with different clinical outcomes.

The Methods Doing research is like playing board game.

Suggestions & Adjustments

1st (pilot) Data Collection (n=59)

Data Analysis I 2nd Data Collection (n=49)

Data Analysis II

3rd Data

Collection (n=40)

Data Analysis III

Evaluation FI

NIS

H ST

ART

Literature Review

Content Analysis

‘SMILEkids’ Development

|- Jan 2014 -|

|--------------- Feb 2014 ----------------|

|- Mar 2014 -| |----------- Apr - May 2014 ------------|

|- Jun 2014 -| |- Jul 2014 -| |- Aug 2014 -|

Sep2014

|- Oct 2014 -| |---- Nov - Dec 2014 ----|

Implementation Let’s see what we got from this R2R

SMILEkids nutrition assessment tool

SMILEkids nutrition assessment tool

Data Collection I (n=59)

Tested using One-way ANOVA with variances of the distributions equal * statistical difference at p < .05

%W/A parameters of pediatric patients with different nutritional status (n=59)

*

*

*

Tested using One-way ANOVA with variances of the distributions equal *,** statistical difference at p < .05

%H/A parameters of pediatric patients with different nutritional status (n=59)

* **

* **

Length of stay of pediatric patients with different nutritional status (n=59)

Tested using Welch statistic due to unequal variances of the distributions No statistical difference at p < .05

Length of stay in ICU of pediatric patients with different nutritional status (n=59)

Tested using Welch statistic due to unequal variances of the distributions No statistical difference at p < .05

Days of ventilator use of pediatric patients with different nutritional status (n=59)

Tested using Welch statistic due to unequal variances of the distributions No statistical difference at p < .05

Discussion

•There are still limitations of interpreting of data from pilot study (ex. excluding patients with LOS < 4 days due to no protocol for routine visit before 4 days) •Characteristics of pediatric patients admitted in regular ward of BIH may not be so critical that nutritional status play significant role in differentiating LOS.

Data collection II (Critical care (CC) pediatric patients admitted from 1/1/2014 to 30/9/2014) (n=49)

Tested using One-way ANOVA with variances of the distributions equal no statistical difference at p < .05

%W/A parameters of CC pediatric patients with different nutritional status (n=59)

Tested using One-way ANOVA with variances of the distributions equal no statistical difference at p < .05

%H/A parameters of CC pediatric patients with different nutritional status (n=59)

Length of stay of CC pediatric patients with different nutritional status (n=59)

Tested using Welch statistic due to unequal variances of the distributions * statistical difference at p < .05

*

*

Length of stay in ICU of CC pediatric patients with different nutritional status (n=59)

Tested using Welch statistic due to unequal variances of the distributions *,** statistical difference at p < .05

*

* **

**

Days of ventilator use of CC pediatric patients with different nutritional status (n=59)

Tested using Welch statistic due to unequal variances of the distributions *,** statistical difference at p < .05

*

*

**

**

Discussion

•There is a trend of difference between growth and length of stay of patients using SMILEkids, but post-hoc analysis couldn’t be done because only 1 patient is categorized in ‘high risk’ using SMILEkids •Revision of indicators used (along with increasing number of subjects) may be needed to yield better results.

Data collection III (Critical care (CC) pediatric patients admitted in the year 2013) (n=40)

SMILEkids Version 2

[6-9] [10-13] [14-18]

Tested using One-way ANOVA with variances of the distributions equal *,** statistical difference at p < .05

%W/A parameters of CC pediatric patients with different nutritional status (n=89)

*

*

*

*

*

**

**

Tested using One-way ANOVA with variances of the distributions equal *,** statistical difference at p < .05

%H/A parameters of CC pediatric patients with different nutritional status (n=89)

* ** ** **

*

*

* **

Length of stay of CC pediatric patients with different nutritional status (n=89)

Tested using Welch statistic due to unequal variances of the distributions *,** statistical difference at p < .05

*

*

* * **

**

Length of stay in ICU of CC pediatric patients with different nutritional status (n=89)

Tested using Welch statistic due to unequal variances of the distributions no statistical difference at p < .05

Days of ventilator use of CC pediatric patients with different nutritional status (n=89)

Tested using Welch statistic due to unequal variances of the distributions no statistical difference at p < .05

Discussion

•There is an improved trend of difference between growth, LOS, LOS in ICU, days of ventilator using SMILEkids V2 comparing with SMILEkids original though not statistically significant.

•LOS in critical care pediatric patients not at risk of malnutrition seems to be significantly less than patients with risk or with malnutrition.

Tested using One-way ANOVA with variances of the distributions equal * statistical difference at p < .05

Growth parameters of CC pediatric patients with different nutritional status (n=89)

*

*

Clinical outcomes of CC pediatric patients with different nutritional status (n=89)

Tested using Welch statistic due to unequal variances of the distributions * statistical difference at p < .05

*

*

What to do next? Lessons learned and challenges ongoing

Lessons learned

•SMILEkids tends to do better in detecting malnutrition in non-critically-ill hospitalized children when comparing with usual SGA but fails in detecting malnutrition in critically ill children.

•SMILEkids V2 were then developed, resulted in better detection of malnourishment than SMILEkids. Although not statistically significant between 3 classes of severity, but at least it could significantly separate those with and without risk of malnutrition.

Challenges

Collect the data from routine practice to evaluate the efficacy of detecting malnutrition in the real situation and to revise indicators in the future (if need) for better detecting power. Compare with published nutrition assessment tools in pediatric to evaluate the efficacy as a further research. Based on the risk assessed using SMILEkids, New pediatric nutrition protocol based on malnutrition risk need to be developed to appropriately implement the nutrition intervention according to each severity. Nutrition screening tool specific to BI pediatric populations can also be made if the indicators used in assessment are revised.

Quality Improvement Team

• T h a n i t V i n i t c h a g o o n

• C h a t c h a n o k P a t t a n a b o o n y a k o r n • S u l e e p o r n T h o n g p h i e w • W a e w t a R a t n i t i s k u l • R u k s a y a U n g k a g a s a m a r a t • P e e r a p a n P o t h o n g • S o m s r i T a c h a v a r a k u l , C D T • K i t i y a S u v a n n a s a n k h a , C D T • O r a p o r n D u m r o n g w o n g s i r i , M D

Certified Dietitians (CDT)

Consultants

References

• White JV, Guenter P, Jensen G, Malone A, Schofield M. Consensus statement: Academy of Nutrition and Dietetics and American Society for Parenteral and Enteral Nutrition: characteristics recommended for the identification and documentation of adult malnutrition (undernutrition). J Parenter Enteral Nutr. 2012; 36(3): 275-83.

• Corkins MR, Griggs KC, Groh-Wargo S, Han-Markey TL, Helms RA, Muir LV, et al. Standards for nutrition support: pediatric hospitalized patients. Nutr Clin Pract. 2013; 28(2): 263-76.

• Aurangzeb B, Whitten KE, Harrison B, Mitchell M, Kepreotes H, Sidler M, et al. Prevalence of malnutrition and risk of under-nutrition in hospitalized children. Clin Nutr. 2012; 31(1): 35-40.

• Joosten KF, Hulst JM. Prevalence of malnutrition in pediatric hospital patients. Curr Opin Pediatr. 2008; 20(5): 590-6.

• Mahdavi AM, Safaiyan A, Ostadrahimi A. Subjective vs objective nutritional assessment study in children: a cross-sectional study in the northwest of Iran. Nutr Res. 2009; 29(4): 269-74.

• Gerasimidis K, Macleod I, Maclean A, Buchanan E, McGrogan P, Swinbank I, et al. Performance of the novel Paediatric Yorkhill Malnutrition Score (PYMS) in hospital practice. Clin Nutr. 2011; 30(4): 430-5.

• Wong S, Graham A, Hirani SP, Grimble G, Forbes A. Validation of the Screening Tool for the Assessment of Malnutrition in Paediatrics (STAMP) in patients with spinal cord injuries (SCIs). Spinal Cord. 2013; 51(5): 424-9.

• Hulst JM, Zwart H, Hop WC, Joosten KF. Dutch national survey to test the STRONGkids nutritional risk screening tool in hospitalized children. Clin Nutr. 2010; 29(1): 106-11.

• Feferbaum R, Delgado AF, Zamberlan P, Leone C. Challenges of nutritional assessment in pediatric ICU. Curr Opin Clin Nutr Metab Care. 2009; 12(3): 245-50.

• Detsky AS, McLaughlin JR, Baker JP, et al. What is subjective global assessment of nutritional status?. J Parenter Enteral Nutr. 1987; 11(1): 8-13.

• Secker DJ, Jeejeebhoy KN. How to perform subjective global nutritional assessment in children. J Acad Nutr Diet. 2012; 112: 424-31.

Thank you :>