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PREDICTING
ENERGY
REQUIREMENTS
Frances Phillips APD
Chief Dietitian
Royal Perth Hospital Nov 2010
“…..Estimated energy requirementsare only a starting point, and that allclinicians should regularly reviewtheir patients to ensure they aremeeting their nutritional goals and toevaluate the effectiveness ofnutritional support”
Weekes Proc Nutr Soc 2007
Survey
Survey:
Use Energy Equations Non Critical
Care Adult Patients Stand Up
Harris Benedict EquationHands at Side
Schofield …. Assume eg = SF 10% and AF 15%
(a) If Multiply BMR x [1.0 + (SF + AF)] (SF +AF = 25%)
(Non Cumulative approach)
Hands on Shoulders(b) If Multiply BMR x (SF or AF), and then multiply other factor (Cumulative approach)
Hands on Head Neither HB or Schofield
Wave Hands
Basil Metabolic Rate (BMR)
• Largest part of an individual’s total energy expenditure(TEE)
• Energy for internal mechanical activities and maintenanceof body temperature
• Measure:
Post fast At physical and mental rest 22-290C environment No artificial stimulants (eg. tea, coffee, nicotine) No physical activity previous day
• Very difficult to control in clinical practice
Weekes Proc Nutr Soc 2007, Garrel NCP 1996, Levine Pub Health Nutr 2005
BMR
Women:
Highest between ages 23-35Increases more slowly > 65 kgMore rapid drop > 50 years than for Men
Men:
Highest between ages 18-20Increases more slowly > 75 kg
Horgan Eur J Clin Nutr 2003
Resting Metabolic Rate (RMR)
65-70% of TEE
Little day to day variations
Declines with age Loss Fat Free Mass & Gain of Fat Mass
Subject to less strict conditions than BMR• Client Specific (Fasting, Exercise, Resting, Comfort)
• Machine Specific (Calibration, Steady State,
Adequate Test Time)
Rest of TEE
Thermogenic response to food (10%)
Expenditure physical activity (15 - 30%)Garrel NCP 1996, Pochlman Med Sci Sports Exerc 1989 , Roberts Pub Health Nutr 2005
Energy Expenditure Equations
Approx 138 Formulae (or more)....
Published by 40 Authors• Many involve critical care patients
Most common Australian Energy Expenditure Equations (Non Critical Care Pts)
• Harris Benedict Equation
• Schofield Equations
Others:• Energy Estimates (per kg)
• Others………..
Reeves Eur J Clin Nutr 2003, Reeves Nutr Rev 2003
Energy Equations - Limitations
All equations open to criticism for multiple reasons
Equations Based on Predictions for “Groups” Poor predictive value for “Individuals”
Equations do not explain 20% of variations between individuals
(eg. tissue variations, disease, genetics
trauma including time since trauma)
Require some clinical judgement
Reeves Eur J Clin Nutr 2003, Schoeller JADA 2007, Weekes Proc Nutr Soc 2007
Energy Equations – Limitations
(cont) Many based on measurements taken over 50
years with some data closer to 100 years• Early data was manually “ cleaned up”
• Some population groups “over” or “under” represented
• Lack of validation studies
• Gender % variations (eg. more males)
Current population More elderly
Increased height and weight
More overweight and obese
Early puberty
Equations focused on “healthy” – not designed tobe applied for disease or injury Reeves Nutr Rev 2003
Harris – Benedict Equation
Background Developed between 1909 and 1917 and based on indirect
calorimetry. Published in 1919 (J. Arthur Harris & Francis G.Benedict)
Predominantly “normal” weight healthy white subjects(Boston) (N =239)
136 men aged 16-63 years (13 underweight)
103 women aged 15-74 years (21 underweight)
Younger (Mean Age F 27 +/- 9 Years , M 31 +/- 14 Years)
Leaner (Mean BMI F 21 +/-3 Years , M 22 +/- 4 Years)
More active than current population
Resting Metabolic Rate Frankenfield JADA 2005, Reeves Nutr Rev 2003
Harris – Benedict Equation
Males:
RMR = 278 + (57.5 x W) + (20.9 x H) – (28.3 x A)
Females:
RMR = 2741 + (40 x W) + (7.7 x H) – (19.6 x A)
Key:
RMR Resting Metabolic Rate (kJ/day)
W Weight (kg)
H Height (cm)
A Age (years)
Reeves Nutr Rev 2003
Adjustments to the HB Equation
HB Equation x AF X IF (Long et al 1979)
• Activity factor• Resting 1.1
• Confined to bed 1.2
• Out of bed 1.3
• Injury factor (N = 20-39 pts)• Minor Op 1.2
• Skeletal Trauma 1.35
• Cancer Cachexia 1.3-1.5
• Major Sepsis 1.6
• Severe Thermal Injury 2.1
• Febrile 1 +0.09 per 0.5 0 c > NReeves Nutr Rev 2003, Long JPEN 1979
? HB = BEE or REE
Early works report HB = BEE
Later considered conditions = REE
Seale 1995:
Factor of 1 to 1.5 added to the BEE of HB Equation = RMR
Thomas 2007, Seale Am J Clin Nutr 1995
Harris – Benedict Equation
Validation
1950’s Measured RMR Within 5%
More recent studies (Indirect Calorimetry)
HB overestimates RMR by 6-15%
More predictive for Men
Large variation between persons
Reeves Nutr Rev 2003, Garrel NCP 1996
Harris – Benedict Equation
Interperson Variation Study
Published 1996 Garrel NCP 1996
RMR (HB Equation vs Indirect Calorimetry)
Subjects (39 Men and 28 Women)
• Healthy, Normal Weight, Sedentary
Results:
• Mean overestimate 11.7%
• RMR Quartile Variations
• Predictive values women
less accurate than men
20.6% 11.5% 9.5% 4%
Lowest Highest
Systematic Review HB 2005
25 Studies …..Focused on individuals not
groups
HB as a Prediction of RMR
• Healthy Non Obese Individual Adults
45 - 80% Individuals
Overestimates > Underestimates
• Healthy Obese Individual Adults
38 - 64% Individuals Overestimates > Underestimates
Adjusted weight ↓ Risk of overestimating RMR
↑ ↑ Maximum underestimation error
• Older Adults
Men: Maximum underest 19%, overest 9% RMR
Women: Maximum underest 27%, overest 12% RMRFrankenfield JADA 2005
Schofield Equations First published in 1985
commonly used in Europe and Australia
Developed from the statistical screening of data inthe literature from 1914 to 1980
Assumed linear relationship between BMR andWeight
Formed the basis for FAO/WHO/UNU Report 1985.
Recommended for use in Australians (1990 , 2005)Schofield Hum Nutr Clin Nutr 1985, Ramirez-Zea Pub Health Nutr 2005, Reeves Nutr Rev 2003, FAO/WHO/UNU 1985,
Hayter Eur J Clin Nutr 1994, Truswell 1990, Aus Govt 2005
Schofield Equations Meta-analysis 114 studies (7173 data points BMR)
inc HB
4809 Men and 2364 Women
Infants to Adults (0-100 Yrs)
↑ Europeans and North American &
↓ Developing countries
Italians (esp military)
47% of the Schofield database >18 Years
Higher proportion of Males, esp Italians 57%
Very active, ↑ BMI's than other caucasians
Few elderly (Only 1-2% > 60 years)
Studies used “Closed-Circuit” Methods – ↑BMR’s
? Stress/anxiety breathing from closed chamberRamirez-Zea Pub Health Nutr 2005, Ferro-Luzzi Pub Health Nutr 2005
Schofield Equations
Italians • Young, physically active
(inc labourers, miners)
• Higher BMR/kg than others
• Most divergent subject group
Indians and Chinese • 10% Lower BMR than Europeans +
Americans
Age, Sex & Weight matchedHayter Eur J Clin Nutr 1994
Schofield Equations
Age (yrs) RMR (Males) RMR (Females)
0-3 (0.249 x W) – 0.127 (0.244 x W) – 0.130
3-10 (0.095 x W) + 2.110 (0.085 x W) + 2.033
10-18 (0.074 x W) + 2.754 (0.056 x W) + 2.898
18-30 (0.063 x W) + 2.896 (0.062 x W) + 2.036
30-60 (0.048 x W) + 3.653 (0.034 x W) + 3.538
>60 (0.049 x W) + 2.459 (0.038 x W) + 2.755
Key:
RMR = Resting Metabolic Rate (MJ/day)
W = Weight (kg)
TEE = BMR x (1.0 + %IF + %AF) or BMR x IF (Elia) x AF
Schofield Hum Nutr Clin Nutr 1985, Reeves Nutr Rev 2003
FAO/WHO/UNU (1985)
Age (yrs) RMR (Males) RMR (Females)
0-3 (0.255 x W) – 0.226 (0.255 x W) – 0.214
3-10 (0.0949 x W) + 2.07 (0.0941 x W) + 2.09
10-18 (0.0732 x W) + 2.72 (0.051 x W) + 3.12
18-30 (0.064 x W) + 2.84 (0.0615 x W) + 2.08
30-60 (0.0485 x W) + 3.67 (0.0364 x W) + 3.47
>60 (0.0565 x W) + 2.04 (0.0439 x W) + 2.49
Key:
RMR = Resting Metabolic Rate (MJ/day)
W = Weight (kg)
Curtin University 2006
British Use Of Schofield Equations
British Dietetic Association 2007
Calculate BMR eg. using Schofield Equation Adjust for Stress Factors (SF)
Elia Nomogram (1990) or
Todorovic and Micklewright (2004) Add a combined factor for activity and dietary induced
thermogenesis (AF)
Bedbound, immobile + 10%
Bedbound, mobile or sitting + 15-20%
Mobile, on ward + 25%
BDA Note: Adding SF and AF is not cumulative Eg If 10% SF and 15% AF = BMR ↑ 25% total
(not ↑ BMR by 10%, then 15%)
Thomas 2007, Elia Med Int 1990
Elia Nomogram
Estimate energy requirements in disease
Used in conjunction with Schofield equations (British Dietetic Assoc)
? Expert opinion
No reference to research or studies or how it is derived
Reeves Nutr Rev 2003
Elia NomogramElia Pub Health Nutr 2005
Stress Factors (% BMR)Todorovic & Micklewright 2004
Brain Injury 0-50%
Cerebral Haem 30%
CVA 5%
COPD 15-20%
Infection 25-45%
IBD 0-10%
ICU 0-60%
Leukaemia 25-34%
Lymphoma 0-25%
Pancreatitis 3-10%
Sepsis / Abcess 20%
Solid Tumours 0-20%
Transplantation 20%
Surgery 5-40%
AF : Schofield vs HB Equations
Bed Rest 1.2
Very Sedentary 1.3
Sedentary 1.4
Light 1.5
Moderate 1.8-1.7
Heavy 2.1-1.8
Very Heavy 2.3-2
Resting 1.1
Confined to Bed 1.2
Light 1.3
Moderate 1.4-1.5
Heavy 1.75
Griffith University 2007
Schofield Equations
Height not required
Other equations including height weredeveloped, however including height did notimprove accuracy.
Mean BMI (>16 years) is not known.
14.6% BMI > 25
4.5% BMI > 30
Lower % Overweight / Obese than today
Reeves Nutr Rev 2003, Horgan Eur J Clin Nutr 2003, Schofield Hum Nutr Clin Nutr 1985
Schofield Equations
General: Predictive value 36-53%
Men: Overestimate BMR in men 3.7%
Especially 18 - 29.9 Years
Greater overestimate BMR when overweight and obese included
Women: Equations not accurate in women
Reeves Nutr Rev 2003, Ramirez-Zea Pub Health Nutr 2005
Schofield Equations
Poor predictive value for adults ( > 18years )
Overestimates BMR current/most populations
7-10%
especially if overweight or obese included
Not accurate for the Elderly
Not accurate for young Australian Men/Women
(18-30 years)
Hayter Eur J Clin Nutr 1994, Piers Eur J Clin Nutr 1997, Reeves 2003, Henry Pub Health Nutr 2005
General Estimates of Energy
Sedentary Adults 25 to 30 kcal (=105to126 kJ) /kg body weight (BW)
25 = Unstressed pts ranging to 35 = Pyrexia or Extreme Stress
19 to 21 = Obesity
Slightly Hypermetabolic Patients
For Weight Gain 30 to 35 kcal (=126to146 kJ) / kg BW
Anabolic Patients
Hypermetabolic patients
Severely Stressed patients 35 kcal (=146 kJ) / kg BW
Patients with Malabsorption
Reeves Nutr Rev 2003, Weekes Proc Nutr Soc 2007
General Estimates of Energy
Does not take into account Age, Gender or Energy Expenditure
Use Actual or Ideal Weight?
? Developed for ventilated critically ill patients
Underestimates true requirements
In reality, amount of Energy per kg progressively decreases with increase in body size
Does not take into account body composition
What determines which value in the range should be used?
Reeves Nutr Rev 2003
Case Studies – Equation Comparisons
Green and Smith and Whelan Eur J Clin Nutr 2008
How estimate BMR?Which stress factor for 37 clinical conditions?Given Standard Scale of Stress Factors (Elia 1990)
40 year old maleNo PMHBMI 23Referred for Oral Nutrition Support
Reeves and Capra Nutr Rev 2003 ****TEE: 50 year old multiple skeletal traumas (Wt 68kg, Ht 175 cms)
Reeves and Capra Eur J Clin Nutr 2003Energy Requirement:50 year old post laryngectomy (exc Larynx Ca).Wt 54kg, Ht 170 cms, RIB, Medically stableWeight loss 7 kg over 6 months secondary to DysphagiaWeight stable & intake adequate (2 wks prior to surgery)
“New” Equations
(eg. Oxford)
Why better predictive value?
• Excluded all Italian subjects
• Included larger number from Tropics
• Sample Size
11,000 measurements
Garrel NCP 1996, Henry Pub Health Nutr 2005
A Few Closing Quotes
“…Clinical judgment must be used todetermine what level of nutrition careshould be based on these estimates”.
Frankenfield JADA 2005
“ …For many patient groups, however ,there is a general lack of studies fromwhich to make any usefulrecommendations”
Weekes Proc Nutr Soc 2007
The Last Words …….
“Nutrition is not an exact science.The most valuable tool in caring forthese patients is a clinical team thatuses energy expenditure calculationsas a starting point but relies onclinical experience andindividualization of patientmanagement to enhance outcome”
Ireton-Jones JPEN 2004
www.health.qld.gov.au/nutrition/resources/est_rqts.pdf
Queensland Government
NEMO Nutrition Support Group
“Estimating Energy & Protein Requirements for Adult Clinical
Conditions”
March 2009 (4 Pages)
REFERENCES
Australian Government & NHMRC (2005) Nutrient Reference Valuesfor Australia and New Zealand.
Curtin University of Technology (2006) Dietitians’ Pocketbook. Elia, M. (1990) Artificial nutritional support. Med. Int 82, 3392-6. Elia, M. (2005) Insights into energy requirements in disease. Pub
Health Nutr. 8(7A) 1037-52. FAO/WHO/UNU(1985) Energy and protein requirements. Report of
a joint FAO/WHO/UNU expert consultation. Technical Report SeriesNo. 724.
Ferro-Luzzi, A. (2005) The conceptual framework for estimatingfood energy requirement. Pub Health Nutr. 8(7A) 940-952.
Frankenfield, D. et al (2005) Comparison of predictive equations forresting metabolic rate in healthy non-obese and obese adults: Asystematic review. JADA. 105: 775-89.
Garrel, D et al (1996) Should we still use the Harris and BenedictEquations? NCP 11: 99-103.
Green, AJ et al (2008) Estimating resting energy expenditure inpatients requiring nutritional support: a survey of dietetic practice.Eur J Clin Nutr. 62: 150-3.
Griffith University (2007) Handbook of Nutrition and Dietetics.
REFERENCES (continued) Hayter, JE. et al (1994) A re-examination of basal metabolic rate
predictive equations: the importance of geographic origin ofsubjects in sample selection. Eur J Clin Nutr. 48: 702-7.
Henry, CJK. (2005) Basal metabolic rate studies in humans:measurement and development of new equations. Public HealthNutrition. 8(7A) 1133-52.
Horgan, GW et al (2003) Predicting basal metabolic rate in theobese is difficult. 57: 335-40.
Ireton-Jones, C (2004) Clinical dilemma: Which energy expenditureequation to use? JPEN. 28(4) 282-3.
Levine, JA (2005) Measurement of Energy Expenditure. PublicHealth Nutrition. 8(7) 1123-32.
Long, CL et al (1979) Metabolic response to injury and illness:Estimation of Energy and Protein needs from Indirect Calorimetryand nitrogen Balance. JPEN 3(6) 452-6.
Piers, LS et al (1997) The validity of predicting the basal metabolicrate of young Australian men and women. Eur. J Clin. Nutr. 51:333-7.
Pochlman, ET (1989) A review: exercise and its influence on restingenergy metabolism in man. Med Sci Sports Exerc. 21: 515-25.
Ramirez-Zea, M. (2005) Validation of three predictive equations forbasal metabolic rate in adults. Public Health Nutrition. 8(7) 1213-28.
REFERENCES (continued) Reeves, MM et al (2003) Predicting energy requirements in the clinical
setting: Are current methods evidenced based? Nutr Rev 61(4) 143-51.
Reeves, MM et al (2003) Variations in the application of methods usedfor predicting energy requirements in acutely ill adult patients: asurvey of practice. Eur J Clin Nutr 57, 1530-5.
Roberts, SB et al (1985) Energy requirements and aging. Pub HealthNutr 8(7) 1028-36.
Schofield, WN et al (1985) Predicting basal metabolic rate, newstandards ad review of previous work. Hum Nutr Clin Nutr 39C,Suppl1, 5-41.
Schoeller, DA. (2007) Making indirect calorimetry a gold standard forpredicting energy requirements for institutionalized patients. JADA.March. 390-2.
Seale, JL (1995) Energy expenditure measurements in relation toenergy requirements. Am J Clin Nutr. 62 (Suppl) 1042S-1046S.
Thomas, B. et al (2007) Manual of Dietetic Practice (BDA) 4th Edition Todorovic, VE et al (2004) A pocket guide to clinical nutrition. BDA Walker, PM (1990) Chapter 24. Predicting food energy requirements
from estimates of energy expenditure. In Recommended NutrientIntakes – Australian Papers. Ed Truswell, A.S.
Weekes, C. (2007) Controversies in the determination of energyrequirements. Proc Nutr Soc 66: 367-77.
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