Current Evidence for Estimating Energy Requirements

1. Current Evidence for Estimating Energy Requirements Clare Soulsby, Research Dietitian

2. Main components of energy expenditure: • basal metabolic rate (BMR) • alteration in BMR due to disease process (stress factors) • activity • diet induced thermogenesis (DIT)

3. Estimating BMR: controversies • basal metabolic rate (BMR) vs. resting energy expenditure (REE) • prediction equations vs. measured energy expenditure (MEE)

4. 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

5. Estimating BMR: controversies • basal metabolic rate (BMR) vs. resting energy expenditure (REE) • prediction equations vs. measured energy expenditure (MEE)

6. 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)

8. Estimating BMR:which equation? • Harris-Benedict • Schofield Equations • disease specific eg Ireton Jones • Kcal/kg

9. 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)

10. 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)

11. 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

12. 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

13. 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

14. Estimating BMR • what about: • the elderly? • the obese?

15. 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

16. Estimating BMR: Obesity • equations (such as Schofield) are linear • weight increases linearly with estimated BMR • may overestimate in obese

17. Estimating BMR: obesity

18. 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

19. 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.

20. 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

21. 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)

22. 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

23. 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

24. Estimating energy requirements • The main components of energy expenditure are estimated: • BMR • Alteration in BMR due to disease process (stress factors) • Activity • DIT

25. 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)

26. 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

27. 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

28. 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).

29. Estimating energy requirements • The main components of energy expenditure are estimated: • BMR • Alteration in BMR due to disease process • Activity • DIT

30. Total energy expenditure Activity + DIT Activity + DIT BMR BMR Health Disease

31. 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

32. Activity factor for activity: institutionalised patients combined with DIT

33. 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

34. 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

35. Physical activity level (PAL) of adults

36. Estimating energy requirements • The main components of energy expenditure are estimated: • BMR • Alteration in BMR due to disease process • Activity • DIT

37. 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

38. 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

39. 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

41. 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