Assessment ncp

Nutrition Care Process: Assessment

Nutrition Assessment

The purpose of nutrition assessment is to collect and interpret relevant patient/client information to identify nutrition-related problems and their causes

Is the first step in the Nutrition Care Process Different from monitoring and evaluation where

similar or same data may be used to determine changes in client behavior or nutrition status and the efficacy of nutrition intervention

ADA IDNT Reference Manual, 2008, p. 8

Nutrition Assessment Involves Critical Thinking Determine appropriate data to collect and

selecting valid and reliable tools Distinguish relevant from irrelevant data Select appropriate norms and standards for

comparing the data Organizing and categorizing the data in a

meaningful way that relates to nutrition problems

Nutrition Assessment Is Ongoing

Is the first step in the Nutrition Care Process but not an isolated event

A dynamic process that evolves throughout the NCP as the pt’s status changes or new information becomes available

Nutrition Assessment

In ADA’s draft Standards of Professional Practice (SOPP), nutrition assessment is a function of the registered dietitian

A dietetic technician, registered, contributes by collecting data, providing some interventions, and evaluating and monitoring patient/client response

Assessment parameters and possible nutrition diagnoses are listed in the IDNT manual and pocket guide

Nutrition Assessment Data

Data collected depends on the practice setting For individuals, data can come directly from

pt/client through interview, observations, and measurement; from health care providers or referring agencies, medical record or laboratory tests

For populations, data from surveys, administrative data sets, and epidemiological and research studies are used


Categories of Nutrition Assessment Data

Food/nutrition history Biochemical data,

medical tests and procedures

Anthropometric measurements

Physical examination findings

Client historyIDNT Reference Manual, ADA, 2008, p. 9

Food/Nutrition Histories: Food Intake

Composition and adequacy of food and nutrient intake, meal and snack patterns, environmental cues to eating, food and nutrient tolerance, and current diets and/or food modifications

ADA IDNT Reference Manual, 2008, p. 11)

Food/Nutrition Histories: Nutrition and Health Awareness Knowledge and beliefs about nutrition

recommendations, self-monitoring/ management practices, and past nutrition counseling and education

ADA IDNT Reference Manual, 2008, p. 11)

Food/Nutrition Histories: Physical Activity and Exercise Functional status, activity patterns, amount

of sedentary time (TV, phone, computer) and exercise intensity, frequency, and duration


Food/Nutrition Histories: Food Availability Food planning, purchasing, preparation

abilities and limitations, food safety practices, food/nutrition program utilization, and food insecurity


Biochemical Data, Medical Tests and Procedures Include laboratory data (e.g. electrolytes,

glucose, lipid panel Gastric emptying time Colonoscopy, CT scan or EKG results

Anthropometric Measurements

Include height, weight, body mass index, growth chart percentile, growth rate, and rate of weight change

Physical Examination Findings

Include oral health, general physical appearance, muscle and subcutaneous fat wasting, and affect

Client History Social history: socioeconomic status, social and

medical support, cultural and religious beliefs, housing situation, and social isolation/connection

Personal history: factors including age, occupation, role in family, and education level

Medical/health history includes chief nutrition complaint, present/past illness, surgical history, chronic disease or complication risk, family medical history, mental/emotional health and cognitive abilities

Medication/supplement history: prescription and over-the-counter drugs, herbal and dietary supplements, and illegal drugs

IDNT Reference Manual, ADA, 2008, p. 11

Food/Nutrition History Information

Food/Nutrition History Information —cont’d

Methods of Obtaining Intake Data Direct observation and nutrient analysis: can be

used only in controlled settings; doesn’t represent usual intake; calorie counts fall into this category

Food record or diary: prospective tool; asks client to record or weigh food intake for a specific time period

Food frequency questionnaire: retrospective; asks client to complete a survey about food intake over a specific time period

24-hour recall: retrospective tool; asks client about food intake during the previous 24 hours

24-Hour Recall

Strengths Weaknesses

Less likely to modify dietary behavior

Memory dependent

Quick and inexpensive Overestimates low intake

Low client burden Underestimates high intake

Literacy independent High-inter-interviewer variability

Food Records


Greater precision than single 24-hour recall

Eating behavior may change

Not memory reliant Literate and numerate dependent; requires knowledge of portion sizes

Considered “actual” intake

High client burden

Food Frequency QuestionnaireStrengths Weaknesses

Low client burden Primarily provides qualitative information

Quick and inexpensive Literate and numerate dependent

Can examine specific nutrients

Memory dependent

Considered “usual” intake Cognitively difficult since food list not meal based

Easily standardized

Accuracy improves when combined with other data

Direct Observation


Low client burden High staff burden

Client unaware of assessment

Intrusive

Not memory or literacy dependent

Difficult to attain and interpret

Does not represent usual intake

Expensive

NCI Food Frequency Questionnaire

Food Diary

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Weight Status and Anthropometry

Anthropometry

Involves obtaining physical measurements of an individual and relating them to standards that reflect the growth and development of the individual

Can be used in nutrition assessment and evaluation and monitoring

Height Measurement

Standing: taken without shoes, feet flat, heels together, legs straight

Arm span: with arms at right angles, distance from tip of the middle finger on the right hand to tip of middle finger on the left; does not change with age

Knee height: can be used to measure stature in those unable to stand; there are equations to convert knee height to stature

Commonly Used Weight Standards NHANES (%ile or IBW or DBW) Hamwi (% IBW or DBW) BMI % ABW/UBW (weight loss)

Hamwi

Population: small group of people with diabetes; desirable weight related to best blood glucose control

Advantage: Portable and easy to use Disadvantage: no evidence this is predictive

of morbidity/mortality in general or in hospitalized population

Hamwi GJ. Changing dietary concepts. In: Donowski TS, ed. Diabetes Mellitus: Diagnosis and Treatment. New York, NY: American Diabetes Association, 1964;73-78.

Metropolitan Height/Weight Tables Population: 4.2 million mostly Caucasian

policy holders; upper socio-economic class. Advantages: desirable wt associated with

lowest morbidity/mortality in this population Disadvantages: may not apply to different

socio-economic classes; requires frame size measurement and reference to a table; data gathering methods poorly controlled; data collected 1954-1972

NHANES DATA Population: U.S. Population over time;

generally percentiles <5 and >95 are considered to be at risk. 50th percentile is median

Advantages: more rigorous data gathering methodology; can be normed to age, sex, race, socio-economic class etc.

Disadvantages: Americans are growing larger; median is not necessarily a healthy weight

NIH BMI Classification Population: NHANES data has been

collected in general population; many RCT use BMI to describe height-weight relationships

Advantage: Strong evidence from RCT and epidemiological studies demonstrating relationship between BMI classification and risk for morbidity and mortality

Disadvantage: Paucity of evidence showing BMI predicts risk in an acute care population

Calculating BMI (Quetelet Index) BMI = Weight (kg) divided by (height [m]2)

OR BMI = (Weight in Pounds x 703

(Ht/in) x (Ht/in)

“Ideal” Body Weight vs “Usual” Body Weight Ideal weight for height (IBW) from

standards like NHANES and Metropolitan ht/wt tables (and Hamwi) is no longer used—Hammond in K&M, p. 400

However, IBW IS often used, whether evidence-based or not

Usual body weight is more useful in those who are ill

What is “Desirable” Weight in Men?Wt in Kg

Ht(in)

Hamwi 1983 MetroHt/Wt

NHANESI and II

BMI 20-25(NIH)

62 54 61 68 50-62

64 59 64 71 53-66

66 65 66 75 56-71

68 70 68 78 60-75

70 75 71 81 63-79

72 81 74 84 67-84

Weight Status as a Predictor of Morbidity and Mortality In young to middle aged adults, morbidity/

mortality is highest in the highest quintile of BMI

In the elderly, morbidity/mortality is highest in the lowest quintile of BMI

In most populations, there is a U-shaped relationship between mortality and BMI

Weight Status as a Predictor of Morbidity and Mortality McClave et al found that “marasmic PCM”

defined as <90% “IBW” was not predictive of poor outcome in acute care pts receiving TPN (JPEN16:337. 1002)

Weight loss and unintentional weight loss is strongly predictive of morbidity/mortality, particularly in the elderly. It is unclear whether this is a nutritional issue

Evaluation of Weight Loss

Significant weight loss: 5% loss in 1 month; 7.5% loss in 3 months; 10% loss in 6 months

Severe weight loss: >5% loss in 1 month; 7.5% loss in 3 months, >10% weight loss in 6 months

Blackburn GL et al. Nutritional and metabolic assessment of the hospitalized patient. J Parent Ent Nutr 1:11, 1977

Evaluation of % Usual Body Wt

85-90% of usual weight: mild malnutrition 75-84% of usual weight: moderate

malnutrition <74% of usual weight: severe malnutrition

Buchman AL: Handbook of nutritional support, Baltimore, 1997, Williams & Wilkins, cited in Hammond in Krause, p. 434

Weight Issues in Clinical Settings Accuracy and reproducibility of weights taken on

different scales and by different personnel Weights of critically ill patients may be unavailable

on admission; sometimes heights are not measured Fluid status Accuracy of weight history data from patients and

family members Confounding factors (wheelchairs, splints, casts,

clothing, amputations) Question of dosing weight: actual, usual, ideal,

adjusted???

Anthropometrics in Pediatrics

Recumbent length measurements used for children younger than 2 or 3 years of age; recorded on birth-to-36 month growth grids

Standing heights of children age 2 or 3 should be recorded on the 2-20 years growth grids

Rate of length or height gain reflects long-term nutritional adequacy

Head circumference: used to evaluate growth in children <3 years of age; usually detects nonnutritional abnormalities

Weight in Pediatrics

A more sensitive measure of nutritional adequacy than height, and reflects recent nutritional intake

Provides crude evaluation of fat and muscle stores

Measurement of the Length of an Infant

Body Composition: Skinfold Thickness (subcutaneous fat) Validity depends on measurement

technique and repetition over time Changes take 3 to 4 weeks Accuracy decreases with increasing

obesity Skinfold sites most reflective of body

fatness are over the triceps and the biceps, below the scapula, suprailiac, and upper thigh

Body Composition: Circumference measurements Waist circumference: smallest area below

the ribcage and above the umbilicus; measurements >40 for men and >35 for women are risk factors for disease

Mid arm circumference (MAC) in combination with TSF can determine arm muscle area (lean body mass)

Fatfold Measurements

Skinfold Calipers Measure Thickness of Subcutaneous Fat in Millimeters

Courtesy Dorice Czajika-Narins, PhD

Other Methods of Body Composition Underwater weighing Total body potassium Neutron activation analysis BIA: fat free mass and fat mass CT: subcutaneous and intraabdominal fat MRI: size of skeleton and internal organs;

abdominal fat DEXA: dual-energy x-ray absorptiometry;

bone mineral density and fat and boneless lean tissue

Visceral Protein Status

Evaluation of Visceral Protein Status Affected by numerous other factors, including

hydration status, chronic illness, acute phase response

May have low sensitivity/specificity However, low serum albumin and acute phase

proteins are associated with increased complications and length of stay in hospitalized patients; probably an index of severity of illness

Preoperative Albumin as a Predictor of Risk in Elective Surgery Patients Retrospective review of 520 patients with

preoperative serum albumin measurements Preoperative albumin correlated inversely

with complications, length of stay, postoperative stay, ICU stay, mortality, and resumption of oral intake

S. albumin levels <3.2 were predictive of risk

– Kudsk et al, JPEN, 2003

Role of Visceral Protein Measurement in Nutrition Screening and Assessment

Low values in critically ill patients a measure of severity of illness

Is a valuable predictor of morbidity/mortality in hospitalized and LTC patients

Can be used to identify elective surgery patients who could benefit from nutrition intervention

Sequential measurements may reflect changes/improvement of nutritional status

Nutrition-Focused Physical Examination Physical signs: using inspection, palpation,

percussion, auscultation Immune function: skin testing and TLC; not

always useful for hospitalized patients Handgrip dynamometry: measures muscle

function; useful for serial measurements Biochemical analysis

Classifying Malnutrition (determination of ICD-9 codes) Body weight Body fat Somatic and visceral protein stores Laboratory values

More Research Needed More data needed to evaluate sensitivity/

specificity of nursing screening systems More research needed to validate nutrition

assessment parameters in clinical settings More data needed to evaluate whether

nutrition intervention is helpful in patients identified to us

The Joint Commission requirements for nutrition screening and assessment are critical in keeping nutrition care front of mind among nurses and administrators

Estimation of Energy and Protein Needs

Harris-Benedict Equation

Monograph in 1919 described results of indirect calorimetry on 239 healthy men and women of varying body sizes up to a BMI of 56 in men and 40 in women

Predicts BMR (RMR) with systematic overestimation of 5-15% (1)

Random error greater in women than in men Stress and activity factors must be applied to

estimate total energy expenditure

1. Daly JM, Helmsfield SB, Head CA, et al. Human energy requirements : overestimation by widely-used predictive equations. Am J. Clin Nutr 1985;42:1170-1174.

Harris Benedict Equation (HBE)

Men = 66.47 + (13.75 x wt in kg) + (5 x ht in cm) – (6.76 x age)

Women = 655.1 + (9.56 x wt in kg) + (1.85 x ht in cm) – (4.68 x age)

Stress Factors for Use with HBE

Elective surgery 1.0 – 1.1 X BEE Multiple bone fx 1.1 – 1.3 X BEE Cancer 1.1 – 1.45 X BEE Fever 1.2 X BEE per 1C >37C Sepsis 1.2 – 1.4 X BEE Severe infection 1.2 – 1.6 X BEE Closed head injury 1.3 X BEE Infection with trauma 1.3 – 1.55 X BEEElwyn DH et al. Surg Clin N Am 1981;61:545-556; Souba WB et al. In Shils ME. Modern Nutrition in Health and Disease, 9th ed. Baltimore, MD: Williams & Wilkins, 1999; Sax HC et al. In The ASPEN Nutrition Support Practice Manual Silver Springs, MD: ASPEN, 1998, 1-5. Cited in ADA Manual of Clinical Dietetics, 6 th edition.

Activity Factors for Use with REE Hospitalized and critical illness 1.05-1.1 Chair or Bed-Bound 1.2 x BEE Seated work with little movement and 1.4-1.5 x BEE little or no leisure activity Seated work with requirement to move 1.6-1.7 x BEE but little strenuous leisure activity Standing work 1.8-1.9 x BEE Strenuous work or highly active 2.0-2.4 x BEE leisure activity 30-60 minutes strenuous leisure AF + .3 activity 4-5 times a week

Shetty PS, Henry CJK, Black AE, et a. Energy requirements in adults: an update of basal metabolic rate (BMR) and physical activity levels (PALs). Eur J. Clin Nutr 1996;50:S11-S23, Frankenfeld et al Crit Car Med 22;1796:1994

Dosing Weight

The weight on which nutritional calculations are based

Must consider– Fluid status– Weight vs standard (IBW? SBW? Adjusted

wt?)

Adjusted Body Weight

In common use for obese patients Rationale is that fat is less metabolically active

than lean tissue; thus using actual body weight in an obese person will overpredict energy needs

However, studies have shown that in very overweight persons, calculating HBE using the adjusted wt tends to make calculations less accurate; underestimating total energy needs

Adjusted Body Weight

[(ABW – IBW) * .25] + IBW

Alternative equation: [(ABW – IBW) * .50] + IBW

– Barak N, et al. Evaluation of stress factors and body weight adjustments currently used to estimate energy expenditure in

hospitalized patients. JPEN 26:231-238, 2001.

What Weight Should We Use to Calculate HBE in Obese Patients? Use Average of IBW/AW. Glynn CC, et al. JPEN

1999;23:147-154. Barak N at al. Evaluation of stress factors and body weight adjustments currently used to estimate energy expenditure in hospitalized patients. JPEN 26:231-238, 2002.

Use actual weight in the HBE equation. Or use obesity-specific equations. Ireton-Jones CS, Turner WW. Actual or ideal body weight: which should be used to predict energy expenditure? JADA 1991;91:193-195.

Use the Mifflin-St. Jeor equation and actual weight for healthy patients (Frankenfeld et al JADA 2003;103:1152-1159)

What Weight Should We Use to Calculate HBE in Obese Patients? ADA Pocket Guide to Nutritional Assessment.

Does not include adjusted body weight. Chicago: The American Dietetic Association, 2004.

ADA Nutrition Care Manual. Does not use adjusted body weight. Online at www.nutritioncaremanual.org

ADA Evidence Library cites maximal underestimation of 42% to overestimation of 25% when using HBE and adjusted body weight

REE Estimates Using Various Equations Healthy Non Obese PtsMifflin-St. Jeor 82% of estimates are accurate;

maximal underestimation 18%; overestimation 15%

Harris-Benedict Actual BW

45-81% of estimates are accurate; errors tend to be overestimates; underestimation 23%; over 42%

Owen 73% of estimates are accurate; errors tend to be underestimates; max underestimation 24%; overestimation 28%

REE Estimates Using Various Equations Healthy Obese PtsMifflin-St. Jeor

70% of estimates are accurate; errors tend to be underestimates; max underestimations 20%; over 15%

Harris-Benedict Actual BW

38-64% of estimates are accurate; errors tend to be overestimates; max underestimation 35%; over 57%

Harris-Benedict Adjusted BW

26% of estimations are accurate; errors tend to be underestimates

Max underestimation 42% to overestimation 25%

ADA EAL NCP accessed 12-07

Recommendations for Predicting RMR in Critically Ill Pts HBE should not be used to predict RMR in

critically ill patients (Grade I) Ireton-Jones 1997 should not be used to

predict RMR in critically ill patients (Grade II)

Ireton-Jones 1992 may be used to predict RMR in critically ill pts but errors will occur. (Grade III)– ADA Evidence Analysis Library, 12-07

Ireton-Jones 1992 Equations

Spontaneously-breathing patients: IJEE (s) = 629 – 11(A) + 25(W) – 609 (O)

Ventilator-dependent patients: IJEE (v) = 1925 – 10(A) + 5(W) + 281 (S)

+ 292 (T) + 851 (B)

Frankenfield D, Smith JS, Cooney RN. Validation of 2 approaches to predicting resting metabolic rate in critically ill patients. JPEN 2004;28(4):259-64.

Ireton-Jones Equations

Where: A = age in years W = weight (kg) O = presence of obesity >30% above IBW (0 =

absent, 1 = present) G = gender (female = 0, male = 1) T = diagnosis of trauma (absent = 0, present = 1) B = diagnosis of burn (absent = 0, present = 1) EEE = estimated energy expenditure

Advantages of Ireton-Jones

Validated in hospitalized patients with varying conditions (pancreatitis, diabetes, trauma, burns)

No need to apply stress factor Data are easily obtainable

Mifflin-St. Jeor

Derived from data on 498 healthy subjects (females=247, males=251) ages 19-78 years

Included normal weight (n=264) and obese (n=234) subjects

REE (males) = 10 x wt (kg) + 6.25 x height (cm) – 5 x age (years) + 5

REE (females) = 10 x weight (kg) + 6.25 x height (cm) – 5 x age (years) - 161

Mifflin-St. Jeor

Best validated of predictive equations in estimating REE in healthy patients

Like HBE, studies were done in healthy patients, so stress factors must be applied if used in medical or surgical patients

Comparison of Prediction Equations with Indirect Calorimetery in Critically Ill Patients Compared REE measurements by indirect

calorimetry with standard predictive equations (Harris-Benedict, Ireton-Jones, Fusco, Frankenfeld)

None of the REEs predicted by the equations correlated well with indirect calorimetry

Mean REEs predicted by Ireton-Jones and Frankenfield were not significantly different, but had low correlation coefficients (r=.26 and r=.39, respectively), meaning they predicted poorly in individual patients

Flancbaum L, Choban PS, Sambucco S, Verducci J, Burge JC. Comparison of indirect calorimetry, the Fick method, and prediction equations in estimating the energy requirements of critically ill patients. Am J Clin Nutr 1999;69:461-6.

Validation of Equations for RMRin Obese and Non-obese People Subjects: 130 non-hospitalized volunteers grouped

by degree of obesity (BMI 18.8 to 96.8) Resting metabolic rate determined using indirect

calorimetry was compared with Harris-Benedict, Harris-Benedict with adjusted body wt in obese persons, Mifflin-St. Jeor, and Owen equations

Main outcome was % of subjects whose calculated metabolic rate differed more than 10% from measured values

Frankenfield DC, Rose WA, Smith JS, Cooney RN. Validation of several established equations for resting metabolic rate in obese and nonobese people. J Am Diet Assoc 2003;103:1152-1159

Results

Calculated RMR was 10% different from measured in 22% of subjects using Mifflin; 33% using Harris-Benedict; 35% using the Owen equation; and 74% in obese subjects using adjusted body wt (vs 36% in obese subjects using actual wt)

Mifflin-St. Jeor was accurate in the largest percentage of non-obese and obese healthy individuals

Frankenfield DC, Rose WA, Smith JS, Cooney RN. Validation of several established equations for resting metabolic rate in obese and nonobese people. J Am Diet Assoc 2003;103:1152-1159

Penn State Equation RMR = RMR (healthy) (0.85) + Ve(33) +

Tmax(175) – 6433 Where RMR = RMR calculated using Harris

Benedict equation and actual body weight, Ve is minute ventilation in L/min, and Tmax is maximum body temperature in the previous 24 hours in degrees Centigrade

ADVANTAGE: does not require the application of stress factors; usually does not require activity factor unless the patient is seizing or unusually active; found to be highly correlated with MEE in validation studies

– Frankenfeld DC et al. Validation of two approaches to predicting resting metabolic rate in critically ill patients. J Parent Ent Nutr 2004;28:259.

In-Class Use of Predictive Equations for EEE and REE Use actual body weight in calculations in

class Use Mifflin-St. Jeor plus activity factors, if

applicable, in ambulatory patients Use Harris-Benedict with actual weight in

hospitalized, stressed patients. Apply stress factors and very small activity factor (1.05 to 1.1)

– ADA Nutrition Care Manual, www.nutritioncaremanual.org, accessed 1-06

In-Class Use of Predictive Equations for EEE and REE Use Ireton-Jones 1992 in patients with

burns and trauma where Penn State data not available

Use Penn State equation in the ICU where minute ventilation and temperature are available

Indirect Calorimetry

Better estimate in critically ill hypermetabolic patient

The “gold standard” in estimating energy needs in critical care

Can be used in both mechanically ventilated and spontaneously breathing patients (ventilated patients most accurate)

Equipment is expensive and not readily available in many facilities

Indirect Calorimetry

Requires appropriate calibration of equipment, attainment of a steady state for measurement, and appropriate timing of measurement

Requires interpretation by trained clinician Inaccurate in patients requiring inspired

oxygen (FiO2>60%), and with air leaks via the entrotracheal tube cuff, chest tubes or bronchopleural fistula

RQ should be within physiological range of .67 to 1.3

Indications for Indirect Calorimetry

Patients with altered body composition (underweight, obese, limb amputation, peripheral edema, ascites)

Difficulty weaning from mechanical ventilation Patients s/p organ transplant Patients with sepsis or hypercatabolic states

(pancreatitis, trauma, burns, ARDS) Failure to respond to standard nutrition support

Malone AM. Methods of assessing energy expenditure in the intensive care unit. Nutr Clin Pract 17:21-28, 2002.

Estimation of Protein Needs Adult maintenance:

0.8-1 g/kg Older adults: 1 g/kg Predialysis: 0.6-0.8

based on GFR Hemodialysis: 1.1-1.4

g/kg PD: 1.2-1.5 g/kg Short bowel

syndrome: 1.5-2 g/kg

Cancer 1-1.2 g/kg Cancer cachexia 1.2-

1.5 g/kg Obesity, stressed: 1.5-

2 g/kg IBW Pregnancy: + 10 g/day Critical illness: 1.5-2

g/kg Major Surgery: 1.0-1.5

g/kg (Cresci, p. 101)

See Chart in ADA Pocket Guide to Nutrition Assessment, p. 159

Estimation of Protein Needs

Use actual weight in estimating protein needs

Use lower end of range in obese patients (BMI>30)

Clinical Standards of Care

Providers in a community or health care organization should develop evidence-based standards for care delivery

This promotes consistency among providers and improved quality of care

Review University of Akron Nutritional Standards of Care

Assessment ncp

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