detecting pediatric malnutrition webinar 1 · 2019-12-02 · •physical exam of fluid status...
TRANSCRIPT
12/2/19
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DETECTING PEDIATRIC MALNUTRITION: TOOLS FOR SCREENING & CRITERIA FOR DIAGNOSING
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MAREE FERGUSONPATRICIA BECKER
MELINDA WHITE
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DISCLOSURES
• The content of this program has met the continuing education criteria of being evidence-based, fair and balanced, and non-promotional
• This educational event is supported by Abbott Nutrition Health Institute, Abbott Nutrition and Dietitian Connection
• Maree Ferguson is the Founder of Dietitian Connection
• Patricia Becker is a self-study program reviewer for ANHI
• Melinda White receives research support from Children’s Hospital Foundation and Nestle Australia
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THE ABBOTT NUTRITION HEALTH INSTITUTE
MISSIONConnect and empower people through science-based nutrition resources to optimize health worldwide.
VISIONImprove lives through the power of nutrition.
ANHI provides nutrition continuing education and resources for you and your patients. Visit anhi.org
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DIETITIANCONNECTION.COM
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OBJECTIVES
• Review the goals, purpose and definition of malnutrition indicators as defined by ASPEN and AND
• Utilize the malnutrition indicators and identify their implications on the diagnosis of pediatric malnutrition
• Define and understand how to implement pediatric malnutrition screening and rescreening tools
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PATRICIA BECKERMS, RDN, CSP, CNSC, FAND
PEDIATRIC NUTRITION SPECIALIST
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ACADEMY/A.S.P.E.N. CONSENSUS STATEMENT: PURPOSE
• ID characteristics/indicators that can be used to diagnose and document pediatric undernutrition in children 1 month – 18 years old– Macronutrient focus– Preterm infants and neonates not addressed
• Use in all settings
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DEFINITION OF UNDERNUTRITION
• An imbalance between nutrient requirement and intake, resulting in cumulative deficits of energy, protein or micronutrients that negatively affects growth, development and other relevant outcomes
• Causes related to:– Diseases/conditions; acute or chronic– Adverse environmental/social/behavioral factors
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ATTRIBUTES OF RECOMMENDED INDICATORS
• Basic parameters– Few in number
– Easily obtained in multiple settings
• Support a diagnosis of undernutrition
• Characterize severity
• Reflect changes in nutritional status– Rather than degree of inflammation/disease acuity
• Evidence informed/consensus derived
• May change over time as evidence of validity accrues
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INTENDED POPULATION
1. Term infants one month of age or greater
2. Infants with a corrected gestational age to term (37 weeks or greater) day of life 30 or greater
3. Children up to and including 18 years of age
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RECOMMENDED INDICATORS• Height-for-age z-score when indicated
• Weight gain velocity
• Adequacy of food/nutrient intake & utilization
• Weight-for-length or BMI-for-age z-score
• Weight loss percentage of usual body
• Mid-Upper Arm Circumference (MUAC)
• Considered indictors:
– Handgrip Strength (ages 6+)
– Tanner Stage
• Should be tracked in all pre-teens/adolescents
• Utility as a nutritional marker limited by the significant variability in genetic determinants for onset of puberty
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PRIMARY INDICATORS: SINGLE DATA POINT AVAILABLE
Mild malnutrition Moderate malnutrition Severe malnutrition
Weight-for-height z-score -1 to -1.9 z-score -2 to -2.9 z-score -3 or greater z-score
BMI-for-age z-score -1 to – 1.9 z-score -2 to -2.9 z-score -3 or greater z-score
Length / height z-score No data No data -3 z-score
Mid-upper arm circumference
Less than – 1 z-score for age / 12.5-13.4cm
Less than -2 z-score for age / < 12.5 cm
Less than – 3 z-score for age / <11.5 cm
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PRIMARY INDICATORS: TWO OR MORE DATA POINTS AVAILABLE
Mild malnutrition Moderate malnutrition Severe malnutrition
Weight gain velocity (< 2 years of age)
Less than 75% *of the norm+ for expected weight gain
Less than 50%* of the norm+ for expected weight gain*
Less than 25%* of the norm+ for expected weight gain*
Weight loss(2-20 years of age) 5% usual body weight 7.5% usual body weight 10% usual body weight
Deceleration in weight for length / height Z-score
Decline of 1 z-score Decline of 2 z-score Decline of 3 z-score
Inadequate nutrient intake
51-75% estimated energy / protein need
26-50% estimated energy / protein need
≤ 25% estimated energy / protein need
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CHILDREN 0-24 MONTHS
• Weight for length z-score
• Weight gain velocity percentage of expected
• Adequacy of energy – protein intake
• Length for age z-score
• Decline in weigh for length z-score
• MUAC z-score
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WEIGHT GAIN VELOCITY ASSESSMENT
Age / boys Grams per day (median)
Grams per month (median)
Mild malnutrition <7 5 % n o rm /m e d ia n
g p e r d a y g p e r m o n th ly
in te rv a l
Moderate malnutrition <5 0 % n o rm /m e d ia n
g p e r d a y g p e r m o n th ly
interval
Severe malnutrition <2 5 % n o rm /m e d ia n
g p e r d a y g p e r m o n th ly
interval
0-30 days 35 1025 26 770 18 513 9 256 30-60 days 40 1200 30 900 20 600 10 300 2-4 months 25 815 19 611 12.5 407 6 204 4-6 months 16 475 15 360 8 238 4 1206-9 months 11 330 8 250 6 125 3 83
9-12 months 9 254 7 191 4.5 127 2 6412-18 months 7 200 5 150 3.5 100 2 5018-24 months 7 195 5 147 3.5 98 2 49
Age / girls Grams per day (median)
Grams per month (median)
Mild malnutrition <7 5 % n o rm /m e d ia n
g p e r d a y g p e r m o n th ly
in te rv a l
Moderate malnutrition <5 0 % n o rm /m e d ia n
g p e r d a y g p e r m o n th ly
interval
Severe malnutrition <2 5 % n o rm /m e d ia n
g p e r d a y g p e r m o n th ly
interval
0-30 days 30 880 22.5 660 15 440 7.5 22030-60 days 34 1012 26 760 17 506 8.5 2532-4 months 24 720 18 540 12 360 6 1804-6 months 15 445 11 334 7.5 223 4 1116-9 months 10 310 8 250 6 173 3 869-12 months 8 240 6 180 4 120 2 6012-18 months 7 200 5 150 3.5 100 2 5018-24 months 7 195 5 147 3.5 98 2 49
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USING Z-SCORES: WHY Z-SCORES ARE BETTER
• Allows for description of change for growth below the 3rd%ile
• Allows for calculation of changes in standard deviation
• Only calculation of z-scores allows for a discrete number that could identify improvement in the shorter term…
• For scores that are above +2 or below the -2 z-score line / above the 95th%ile and below the 5th%ile
• Percentiles are intended to be stated as intervals; such as “between the 5th and the 10th” if a child falls between lines, not as a discrete number (7th%ile)
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COMPARISON OF PERCENTILES AND Z-SCORES
• A z-score of +3 and -3 is more likely to be abnormal
• Percentile graphs have a narrower range
• 97th%ile corresponds to +2 z-score
• 3rd%ile corresponds to -2 z-score
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CHILDREN 2 TO 18 YEARS
• BMI-for-age z-score
• Decline in BMI-for-age z-score
• Weight loss percentage of usual body weight
• Adequacy of energy – protein intake percentage of estimated need
• MUAC z-score
• Height-for-age z-score
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WEIGHT LOSS PERCENTAGE OF USUAL BODY WEIGHT
• Nutritionally significant weight loss for adults is time dependent
• Nutritionally significant weight loss for children is not time dependent
• Usual body weight for growing children is a challenge
• Most recent stable weight vs. highest weight
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MAKING THE DIAGNOSIS
• Assess all available data
• There may be more than one level of severity / acuity of malnutrition resulting from the data. It is appropriate to select / assign the highest or most severe level of malnutrition based on the criteria, as this will impact the intervention, care and treatment plan for the child
• Example:– If the adequacy of intake suggests moderate malnutrition, but the weight
gain velocity percentage of the norm or the weight-for-length z-score indicate severe a diagnosis of severe malnutrition should be documented
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MAKING A DIAGNOSIS
• Nutrition focused physical examination
– Macronutrient assessment vs. micronutrient assessment
• Physical examination of subcutaneous fat loss and muscle wasting
• Physical exam of fluid status
• Assessing functional status
• Physical exam of hair, face, (eyes, nose, mouth) nails, skin
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• Use the International Dietetics and Nutrition Terminology
• Problem Etiology Signs/Symptoms (PES) Statement
– Severe pediatric malnutrition (chronic) related to inadequate nutrient intake as evidenced by weight-for-length z-score less than -3 z-score with reduced muscle mass, visible rib and spine, pale conjunctiva
– Moderate malnutrition (acute) related to nutrient loss from high ostomy output as evidenced by weight loss percentage of usual body weight between 7.5 – 9.9%
– Mild malnutrition (chronic) related to IBD as evidenced by decline in BMI-for- age z-score between -1 to -1.99
DOCUMENTING THE DIAGNOSIS
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THE ROLE OF THE PEDIATRIC DIETITIAN
• Include your nutrition-focused physical findings in your nutrition diagnosis
• Use your nutrition diagnosis to support your nutrition interventions
• Notify your health care team members of your nutrition diagnosis and your nutrition intervention recommendations
• Monitor and evaluate your patient’s response to the intervention and changes to their malnutrition severity / acuity
• Document changes in the medical record
• Notify your care team of changes
• Get to know your medical coders to assist them with needed coding information related to malnutrition billing and coding
• Educate your health care team on the recommended indicators for the identification and documentation of pediatric malnutrition
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MELINDA WHITE PHD, GRAD DIP NUTR&DIET, BSC, APD
DIRECTOR, CLINICAL NUTRITION
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PEDIATRIC NUTRITION SCREENING TOOLS
• STAMP (Screening Tool for the Assessment for Malnutrition in Paediatrics)1
– Diagnosis with nutritional implications
– Change in nutritional intake– Comparison of height and weight to percentiles
• STRONGkids (Screening Tool for Risk on Nutritional Status and Growth)2
– Subjective Clinical Assessment (1 point)– High Risk Disease (2 points) [refer to table]
– Weight loss or poor weight gain? (1 point)– Nutritional intake and losses (1 point)
• Excessive diarrhoea ≥ 5 per day and/or vomiting (>3 times per day) the last few days?
• Reduced food intake during the last few days before admission
• Pre-existing dietetically advised nutritional intervention?
• Inability to consume adequate intake because of pain?
1. McCarthy H, et al. J Hum Nutr Diet. 2012;25(4):311-3182. Hulst JM, et al. Clin Nutr. 2010;29(1):106-111.
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• PYMS (Paediatric Yorkhill Malnutrition Score)1
– BMI compared to cut-off values
– Weight loss– Reduced intake
• PeDiSMART (Pediatric Digital Scaled Malnutrition Risk Screening Tool)2
– Nutritional status (weight z-score)– GI symptoms
– Disease impact
• PNST (Paediatric Nutrition Screening Tool)3
– Weight loss– Poor weight gain
– Reduced nutritional intake– Physical presentation
1. Gerasimidis K, et al. Br J Nutr. 2010;104(5):751-6.2. Karagiozoglou-Lampoudi T, et al. JPEN . 2015;39(4):418-25.3. White M, et al. JPEN . 2016;40(3):392-8.
PEDIATRIC NUTRITION SCREENING TOOLS
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HOW DO YOU CHOOSE?
• Quick: less than 5 minutes, no need to refer to other material
• Simple: no user training needed
• Valid: specific and sensitive• Easy to implement
• Universal• Screen Vs Assessment
• Inexpensive• Non-invasive
• Monitor for nutritional deterioration
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IMPLEMENTING SCREENING TOOLS
• Embedded into the nursing admission process
• Referral process
• Training
• Triage of screening referrals• Time frames to review at risk patients
• Nutrition assessment methodology
• Formation of nutrition care plans and frequency for ongoing review
• Rescreening
• Auditing and monitoring
• Promotion
• Population specific screening tools
• Duty of care
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BARRIERS TO SCREENING
• Time– Timeframe for completion
• Complexity– Simple
• Discomfort with subjective nature of screening– Guide for completion
• Lack of organisational support– Incorporation into hospital accreditation
– Embed into routine practice
– Metrics for communication to executive
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BARRIERS TO SCREENING
• Lack of RCT to demonstrate influence on clinical outcome– Predicts lengths of stay1
– Increases malnutrition awareness2
– Links to clinical coding, US study coding only identified 1.3% malnutrition3
• Concern with an increase in referrals
1. Galeria-Martinez R, et al. JPGN . 64(3):e65-e70.2. Rub G, et al. JPGN. 62(5):771-5. 3. Abdelhadi R, et al. JPEN . 2016;40(5):623-35.
RCT – Randomized Controlled Trial
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NUTRITION SURVEILLANCE DURING HOSPITAL STAY: RESCREENING
• STRONGkids, PYMS, PeDiSMART, STAMP
– No targeted validation for children who stay longer than 7 days
– Contain criteria of diagnosis and anthropometric categorisation against standards which do not measure nutritional deterioration
• The environment is right– Progression of care via digital platforms
creates an opportunity for refinement of the screening process with the introduction of regular ‘rescreening’ as a weekly nursing order or task
– Malnutrition is classed as a Hospital Acquired Complication (HAC)
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AIM: RESCREENING
To design and validate a simple, quick and easy nutrition screening tool that can be repeated to detect recent nutritional deterioration in long stay paediatric patients
White S, et al. Clin Nutr ESPEN . 2019;34:55-60.
Clin Nutr ESPEN, 34, 55-60 Dec 2019A Simple Nutrition Screening Tool to Identify Nutritional Deterioration in Long Stay Paediatric Inpatients: The Paediatric Nutrition Rescreening Tool (PNRT)Melinda S White, Melinda Ziemann, Annabel Doolan, Shang Qian Song, Anne Bernard
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METHODS• Prospective longitudinal study in Queensland Children’s Hospital
– 359 beds
• Change in weight over 7 days vs has the child lost weight or had poor weight gain in the last 7 days?
• Change in energy and protein intake over 7 days vs has the child had reduced nutritional intake in the last 7 days?
White S, et al. Clin Nutr ESPEN . 2019;34:55-60.
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150 patients approached
N=88 consented
N=61 (Ax + at least 1 Ax
and rescreen 7 days later)
Total 224 data points collected over 1-20 weeks
(median 6 weeks)
27 excluded (d/c before 7 day
follow up assessment)
White S, et al. Clin Nutr ESPEN . 2019;34:55-60.
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DESCRIPTIVE STATISTICS
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44%
56%
Children with one week of data (n = 61):• Age = range from 0-16,
average of 4.5 years old
• BMI z-score average was -0.65 (Min = -3.67, Max = 2.27)
• MUAC z-score average was -0.75 (Min = -3.49, Max = 0.1.84)
Primary Diagnosis
Cardiac Cystic Fibr osisGastroenter olg y OncologySu rgical Other
White S, et al. Clin Nutr ESPEN . 2019;34:55-60.
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Variable Sensitivity Specificity AUC
Any reduction in energy intake (Δ in energy intake) 28.71% 83.61% 0.56 (poor)
Any reduction in protein intake (Δ in protein intake) 28.57% 84.68% 0.57 (poor)
Reduction in ≥ 25% of energy intake 61.9% 82.18% 0.72 (good)
Reduction in ≥ 25% of protein intake 55.56 % 82.65 % 0.69 (moderate)
Reduction in ≥ 50% of energy intake (n=8) 85.71% 80.09% 0.83 (excellent)
Reduction in ≥ 50% of protein intake 80% 80.75% 0.8 (excellent)
Q1. Has the child had reduced nutritional intake in the last 7 days?
AUC = Area under the curve
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Q2. Has the child had any weight loss or poor weight gain in the last 7 days?
Variable Sensitivity Specificity AUC Any decrease in weight 71.43% 87.77% 0.8 (excellent)
Weight loss or poor weight gain 42.3% 90.8% 0.66 (moderate)
Any change in BMI z-score 40.91% 89.58 % 0.65 (moderate)
5% reduction in BMI z-score 39.34% 87.92% 0.64 (moderate)
AUC = Area under the curve
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INTRODUCING THE PAEDIATRIC NUTRITION RESCREENING TOOL (PNRT)
• Has the child had reduced nutritional intake in the last 7 days?
• Has the child lost weight in the last 7 days?
• A positive answer to either question indicates the need for a full nutrition assessment
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MAREE FERGUSONMBA, PHD, RDN, FAND
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How do you manage referrals generated from nutrition screening?
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How do you audit nutrition screening compliance?
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How do you maximise the identification of children who are malnourished to ensure they are recognised by the international disease classification system
(coded)?
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Is a single data point adequate to identify and document malnutrition? Could this lead to over diagnosing
malnutrition?
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Should you diagnose a child with malnutrition but who is energetic, has good functional status and whose other nutrition-focused
physical findings do not suggest malnutrition?
For example, a child with a BMI for age z-score of -2.15 no signs of micronutrient deficiency, who is energetic,
with good functional status
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Why is only severe malnutrition associated with height-for-age
z-score?
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Inaccuracy in height/length measurement is a major issue for pediatric dietitians, how does this impact the diagnosis of pediatric
malnutrition?
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THANK YOU!
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