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Current Evidence for Estimating Energy Requirements. Clare Soulsby, Research Dietitian. Main components of energy expenditure:. basal metabolic rate (BMR) alteration in BMR due to disease process (stress factors) activity diet induced thermogenesis (DIT). Estimating BMR: controversies.

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main components of energy expenditure
Main components of energy expenditure:
  • basal metabolic rate (BMR)
  • alteration in BMR due to disease process (stress factors)
  • activity
  • diet induced thermogenesis (DIT)
estimating bmr controversies
Estimating BMR: controversies
  • basal metabolic rate (BMR) vs. resting energy expenditure (REE)
  • prediction equations vs. measured energy expenditure (MEE)
conditions essential for measuring bmr
Conditions essential for measuring BMR
  • post-absorptive (12 hour fast)
  • lying still at physical and mental rest
  • thermo-neutral environment (27 – 29oC)
  • no tea/coffee/nicotine in previous 12 hours
  • no heavy physical activity previous day
  • gases must be calibrated
  • establish steady-state (~ 30 minutes)

* if any of the above conditions are not met = REE

estimating bmr controversies1
Estimating BMR: controversies
  • basal metabolic rate (BMR) vs. resting energy expenditure (REE)
  • prediction equations vs. measured energy expenditure (MEE)
estimating bmr prediction equations
Estimating BMR: prediction equations
  • may over or under-estimate (compared with MEE)
  • inadequately validated
  • poor predictive value for individuals
  • open to misinterpretation

(Cortes & Nelson, 1989; Malone, 2002; Reeves & Capra, 2003)

estimating bmr which equation
Estimating BMR:which equation?
  • Harris-Benedict
  • Schofield Equations
  • disease specific eg Ireton Jones
  • Kcal/kg
estimating bmr harris benedict equations
Estimating BMR: Harris Benedict Equations
  • Developed in 1919
  • From data collected between 1909 and 1917 (Harris Benedict 1919)
  • Study population:
    • 136 men; mean age 27 ± 9 yrs, mean BMI 21.4 ± 2.8
    • 103 women; mean age 31 ± 14 yrs, mean BMI 21.5 ± 4.1
  • Tends to overestimate in healthy individuals (Daly 1985, Owen 1986, Owen 1987)
estimating bmr schofield equations
Estimating BMR: Schofield Equations
  • developed in 1985 (Schofield 1985)
  • meta analysis of 100 studies of 3500men and 1200 women
  • studies conducted between 1914 and 1980 (including Harris Benedict data)
  • 2200 (46%) subjects were military Italian adults
  • 88 (1.2%) subjects were >60 years
  • SE 153-164kcal/d (women) 108 -119kcal/d (men) (Schofield 1985)
estimating bmr disease specific equations
Estimating BMR: disease specific equations
  • developed for specific patient groups (Ireton Jones 1992, Ireton Jones 2002)
  • advantage over Schofield/ HB equations:
    • Schofield /HB estimate BMR of a healthy individual then necessary to adjust for disease using a stress factors
    • disease specific equations include patients in their database so aim to more accurately reflect BMR of hospitalised patients
estimating bmr ireton jones energy equations
Estimating BMR: Ireton-Jones energy equations
  • ventilated and breathing ICU patients
  • 3 x 1 minute measurements 200 patients
  • unclear whether measurements took place during feed infusion/ after treatment etc
  • 52% burns, 31% trauma
  • validation studies, IJEE had a better agreement with MEE:
    • HBx1.2, HBx1.3, 21kcal/kg
estimating bmr
Estimating BMR
  • Schofield equation derived using meta analysis:
    • greater power than small/ local studies
  • compiled from unstructured data set obtained for diverse reasons:
    • problems with sampling assumptions
  • accuracy approx ±15%
  • disease specific equations useful in some circumstances
estimating bmr1
Estimating BMR
  • what about:
    • the elderly?
    • the obese?
estimating bmr the elderly
Estimating BMR: the elderly
  • Original Schofield equations:
    • only 88 (1.2%) of subjects >60 years
    • particularly unsuitable for >75yr
    • included data on subjects from the tropics
  • Revised equations for the elderly:
    • published in the 1991 COMA (DH 1991)
    • include additional data from 2 studies; 101 Glaswegian men (60-70yr) 170 Italian men and 180 Italian women
    • excluded data collected in the tropics
estimating bmr obesity
Estimating BMR: Obesity
  • equations (such as Schofield) are linear
  • weight increases linearly with estimated BMR
  • may overestimate in obese
estimating bmr obesity2
Estimating BMR: Obesity
  • obese data primarily obtained from 2 groups:
    • Burmese hill dwellers
    • retired Italian military
  • there were significant differences in weight/ BMR association between groups, Italian group showed greatest difference
  • obese subjects in Schofield data may not be a statistically representative sample of the population is general
estimating bmr obesity3
Estimating BMR: Obesity
  • recent (Horgan 2003) reassessed validity of the Schofield data to predict BMR in obese
  • conclusions:
    • BMR increases more slowly at heavier weights
    • to ignore this is to over predict energy requirements
    • any general equation for predicting BMR may be biased for some groups or populations.
estimating bmr adjusted body weight adj
Estimating BMR: adjusted body weight (ADJ)
  • estimate of how much of the extra body weight is lean and thus metabolically active
  • 2 methods:
  • 25% adjusted weight

= (actual body weight x 0.25) + ideal body weight

  • adjusted average weight

= (actual body weight + ideal body weight) x 0.5

estimating bmr adjusted body weight adj1
Estimating BMR: adjusted body weight (ADJ)
  • first reported in newsletter Q&A format
  • not validated
  • studies suggest adjusted average weight has better predictive value than 25% adjusted weight (Glynn 1998, Barak 2002)
  • no longer included in ASPEN guidelines (2002)
estimating bmr obesity4
Estimating BMR: Obesity
  • predicting BMR is very difficult (without measuring lean body mass)
  • adequacy of specific equations? (Ireton-Jones et al., 1992; Glynn et al., 1998)
  • actual body weight + stress + activity = overestimate
  • access to indirect calorimetry is limited
determining energy requirements in obesity
Determining energy requirements in obesity
  • non stressed patients:
    • calculate as normal and - 400-1000kcal for decrease in energy stores
  • mild to moderately stress:
    • calculate as normal
    • omission of stress and activity avoids the adverse effects of overfeeding
  • severe stress
    • might be necessary to add a stress factor to BMR
  • *monitoring essential eg blood glucose
estimating energy requirements
Estimating energy requirements
  • The main components of energy expenditure are estimated:
    • BMR
    • Alteration in BMR due to disease process (stress factors)
    • Activity
    • DIT
levels of evidence
Levels of evidence

1. a) Meta-analyses

b) Systematic reviews of randomised controlled trials (RCTs)

c) RCTs

2. a) Systematic reviews of case-control or cohort studies

b) Case-control or cohort studies

3. Non-analytic studies e.g. case studies

4. Expert opinion

(adapted from: Draft NICE Guidelines for Nutrition Support in Adults, 2005)

stress factors
Stress factors
  • timing of measurements
  • over (hyperalimentation) vs. under-feeding
  • changes in therapeutic interventions

e.g. improved wound care, anti-pyretics, sedation, control of ambient room temperature

 err towards lower end of the range and monitor

stress factors1
Stress factors
  • unable to include a stress factor for every disease or condition
  • many measured in far from ideal circumstances
  • limited by data available
  • may choose to underfeed in certain circumstances
  • necessary to refer back to the literature
  • included a checklist of factors to look for when reviewing papers
adverse effect of over feeding
Adverse effect of over-feeding
  • excess carbohydrate:
    • difficulties controlling blood glucose
    • increased CO2 production
    • respiratory problems in vulnerable patients (eg COPD/ ventilated)
  • swings in blood glucose increase mortality in critically ill
  • aim not to exceed the glucose oxidation rate (4-7 mg glucose/ kg/ min)
  • long term excess carbohydrate can lead to steatohepatosis or fatty liver (Elwyn DH, 1987).
estimating energy requirements1
Estimating energy requirements
  • The main components of energy expenditure are estimated:
    • BMR
    • Alteration in BMR due to disease process
    • Activity
    • DIT
total energy expenditure
Total energy expenditure

Activity

+ DIT

Activity

+ DIT

BMR

BMR

Health

Disease

activity factor
Activity factor
  • energy expended during active movement of skeletal muscle
  • approximately 20-40% of energy expenditure in free living individuals
  • depends on duration and intensity of the exercise
  • activity is less than 20% of the energy expenditure in hospitalised or institutionalised
  • NB assumes normal muscle function
activity factor abnormal muscle function
Activity factor:abnormal muscle function
  • hospital patients likely to have higher activity levels:
    • abnormal neuro-muscular function e.g. brain injury, Parkinson’s, cerebral palsy, motor neurone disease, and Huntington’s chorea
    • prolonged active physiotherapy
    • effort involved in moving injured or painful limbs
community patients
Community patients
  • free living individuals have higher energy expenditure due to physical activity
  • nursing home and house bound patients ? similar activity levels to hospital patients
  • for active patients in the community a PAL should be added
estimating energy requirements2
Estimating energy requirements
  • The main components of energy expenditure are estimated:
    • BMR
    • Alteration in BMR due to disease process
    • Activity
    • DIT
diet induced thermogenesis
Diet-induced thermogenesis
  • Continuous infusion of enteral feed and parenteral nutrition do not significantly increase REE
  • Bolus feeding increases REE by ~ 5%
  • Mixed meal increases REE ~ 10 %
  • PALs include DIT (COMA, 1991)

 guidelines include combined factor for activity and DIT

estimating requirements sources of error
Estimating requirements: sources of error
  • prediction equation for BMR
  • stress factor:
    • degree of stress inaccurately assessed
    • poor evidence to support stress factor used
  • activity level inaccurately assessed or poorly understood
  • DIT varies by 10% depending on feeding method
sources of error inaccurate weight
Sources of error: inaccurate weight
  • Inaccurately measured weight
    • estimated weight
    • inaccurate scales
    • patient had their feet on the floor (chair scales)
    • patient was fluid overloaded ( 20% of hospital patients)
    • amputees
conclusions
Conclusions
  • Estimated requirements are only a starting point

- set realistic goals of treatment for each patient

- monitor and amend as patient’s condition changes

  • Review and criticise the literature regularly

- be aware of gaps in the evidence

- understand the limitations of guidelines

- check applicability to your patients

  • Contribute to research and audit projects
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