# Back to basics: estimating energy requirements for adult hospital patients.

Abstract

Predictive equations are a quick and non-invasive way to estimate a patient's energy requirements, and can be a useful tool when used appropriately. However, as with any tool, the skill and experience of the user will affect the quality of the result. This paper looks at the origins and limitations of some of the more commonly used equations. Considerations in their use and interpretation, such as the use of injury and activity factors, adjusting weight and non-protein calories, are also discussed.

Key words: basal metabolism, energy metabolism, nutrition assessment, nutrition support.

INTRODUCTION

The estimation of a patient's requirements is an essential component of nutrition support, ensuring that the patient's nutritional needs are met without significant over- or underfeeding. In everyday hospital practice, several different equations are used, often without an adequate understanding of their origins and limitations. (1,2) This can lead to significant variation in energy provision, which could have serious implications for patient care. A previous review of prediction equations concluded that none is sufficiently accurate to be useful in practice. (3) However, the reality is that equations are the most widely used method for assessing nutrition support that patients need in hospital. When used appropriately, a predictive equation can be a useful tool. Although it is not a 'magic formula' to tell us the answer, it enables us to make a good prediction as a starting point for ongoing patient care. Like any tool, the equation is only as good as the person using it: skill and experience will significantly inform the use and interpretation of these prediction equations.

BASIC CONCEPTS

The body's energy expenditure is usually described as consisting of three components: the basal metabolic rate (BMR), the energy expended in physical activity, and the thermic effect of feeding (TEF). The BMR is the minimum amount of energy required to sustain the body's essential metabolic processes. It is the value for metabolic rate that would be obtained when the subject first awakes (remaining relaxed, and motionless) after an overnight fast, in a thermoneutral setting. Resting metabolic rate (RMR) approximates the BMR when it is not possible to meet all of the above conditions. It is usually measured in a subject who has fasted and has been lying quietly for at least 30 minutes before measurement. (4) The TEF is the amount of energy consumed by the body after eating, in digesting and absorbing the nutrients from food and converting them for use or storage. It can be measured using indirect calorimetry, by comparing the BMR with energy expenditure measured (in the same conditions) after a meal. TEF is usually assumed to be about 10-15% of the BMR, but is affected by a wide range of factors, such as the subject's nutritional status and the composition of the diet. Other terms used for the TEF include: 'diet-induced thermogenesis', 'postprandial thermogenesis', and 'thermic effect of a meal'. (5) The total energy expenditure is the total of basal energy expenditure (BMR), the TEF and activity. Increased activity raises the total, as does increased food intake (by increasing TEF) and illness/inflammation (by increasing BMR).

The rate of energy consumption in the body changes from moment to moment, and varies between different body organs and tissues, but an individual's BMR changes very little, even over significant periods of time. (6,7) Metabolic rate measurements produce a 'snapshot' of energy expenditure over a short period, which is then used to estimate the average rate of energy expenditure for the whole day. However, the measurement period may not reflect the true energy expenditure, as there is necessarily some variation and error involved. Similarly, the energy released from food will not provide exactly the calculated amount, because of losses as unabsorbed nutrient, losses due to energy conversion inefficiencies, and losses as heat. Decreased activity and muscle mass with ageing, and smaller muscle mass in women, generally contribute to a lower energy expenditure; however, variation between individuals is significant. A group of people of the same age and sex who have similar body weight and body composition will not have identical BMRs; even when intake and activity are controlled, the variation in BMRs between different members of the group will be as much as 10%. (8)

EQUATIONS FOR ESTIMATING ENERGY REQUIREMENTS

Clearly, it would be best if it were possible to measure actual energy expenditure, rather than just estimating it. For this reason, indirect calorimetry is considered to be the 'gold standard' for assessing energy expenditure in hospitalised patients. This uses respiratory gas exchange to estimate fuel consumption, and can produce accurate results when implemented correctly by trained personnel. It is not infallible, however, as its results are affected by factors such as oxygen therapy, haemodynamic instability, fever, nursing care activities and difficulties obtaining a steady state. (9) It is time-intensive and requires expensive equipment, and at present, most dietitians do not have access to this method for everyday estimation of their patients' needs.

Other measurement methods are generally not useful for hospital patients. Direct calorimetry is not in wide use even for research purposes, as it measures energy expenditure by monitoring the body's heat production and requires a specially designed sealed room with tightly controlled conditions. The doubly labelled water technique is the only research method that allows energy expenditure to be estimated in free-living subjects. This uses orally administered 'heavy' water (containing stable isotopes of hydrogen and oxygen) to measure the body's carbon dioxide production as indicated by the gradual loss of the isotopes from the body over 1-3 weeks. (4) The energy expenditure can then be estimated in a similar way to that in indirect calorimetry. The time frame for doubly labelled water studies is too long for the assessment of most hospital patients, and the heavy water is expensive. As an alternative to these measurement methods, predictive equations provide a cheap, quick and non-invasive method for estimating requirements, based on the main factors that affect energy expenditure: age and sex (which both affect body composition), and body size.

Researchers developing predictive equations have attempted to validate these against measurements of energy expenditure, often using statistical regression or correlation. When assessing the literature, it should be noted that correlation does not necessarily indicate how closely the equation can estimate the patient's expenditure. For example, if an equation always produced a result that was exactly double the true energy expenditure, it would lead to dangerous over-feeding if it was used for predicting patients' requirements. Statistically, though, it is considered a perfect correlation, with a correlation coefficient r = 1. The correlation therefore does not indicate how useful the equation would be in practice. Validation of predictive equations should always consider residuals, limits of agreement, or other indication of fit, rather than just the correlation coefficient. (10)

Various equations have been developed for the estimation of energy requirements, but the most commonly used equations are the Harris-Benedict equation and the Schofield equation. More recently developed equations have attracted attention; these include the Mifflin-St Jeor equation and the Ireton-Jones equation.

Schofield equation (11,12)

The Schofield equations are an extension of the FAO/WHO/UNU work on energy requirements (13) and, as a result, are sometimes referred to, incorrectly, as the WHO equation. (14) Having since been revised by Schofield, the equation now in use is slightly different due to the incorporation of some extra data. It is the most commonly used by Australian dietitians (1) and has been preferred, because it does not require a value for height and, therefore, introduces fewer sources of error if no measured height or weight are available. Schofield did develop an additional equation that included height, but it did not significantly increase the accuracy of the prediction when compared with the simpler equation. The Schofield equation (Table 1) estimates basal requirement. It is based on a very large data set (pooled BMR data from 114 studies, with more than 7000 healthy subjects from 23 different countries). The results may have been affected by significant differences in ambient temperatures for some of the data, and also some of the subjects were significantly underweight and may have been malnourished. The age and weight range of subjects is wide, but the group contains many more men than women, and a significant number (about 1000) of the subjects were young male Italian soldiers and cadets. The average subject in Schofield's data set would therefore be significantly younger, leaner and fitter than an average Australian hospital patient. This may mean that the equations overestimate requirements (15,16) even in young healthy Australian men. (17) Other validating studies have suggested that it may overestimate for those with low requirements and underestimate for those with high requirements, deviating towards the mean for both. (14) In 1991, a British panel of experts, the Panel on Dietary Reference Values, published a modified version of the Schofield equation for use in Britain. (18) They added data from an additional 451 European subjects, particularly from older age groups, and also excluded some of Schofield's original data which were 'collected in the tropics' and were not felt to reflect the requirements of better-nourished British people. The original data set may in fact have been more appropriate for multicultural Australia; however, the modifications affect only the older (over 60 years) age group. In any case, the majority of Australian dietitians are likely to be using the original, unmodified equations, because these are generally the ones to be found in textbooks and other widely used resources, such as the Dietitians' Pocketbook, (19) the previous Recommended Dietary Intakes for Use in Australia (20) and the Nutrient Reference Values for Australia and New Zealand, which replaced it. (21)

Harris-Benedict equation (22)

This equation was developed from a single smaller study, with only 239 subjects, all healthy Americans. The participants may not be reflective of modern Australians because they were relatively young (average age 29 [+ or -] 11 years) and lean (average body mass index 21 [+ or -] 3 kg/[m.sup.2]). Repeated measurements were made in each subject, with careful attention to factors such as subject inactivity; however, the limitations of the testing conditions mean that some subjects may not have been in a true 'basal' state, leading to over-estimation of their energy needs. (8,15) The Harris-Benedict equation (Table 2) is thought to overestimate requirements in healthy people, perhaps by 5% in men or 15% in women. (23-25) A disadvantage of this equation is that it requires both weight and height, which may often not be available. However, as it remains the most commonly used equation in the world, particularly in the USA, it is essential to be familiar with it when assessing the medical literature.

Mifflin-St Jeor equation (26)

This equation used 498 healthy adult subjects with a wide range of ages and weights (about half of the subjects were obese) and measured resting metabolic rate. The equation uses actual weight, and notably it predicts significantly lower requirements when weight is very high, compared with the Schofield and Harris-Benedict equations. An advantage of the Mifflin-St Jeor equation (Table 3) is that it is very simple and easy to remember; however, like the Harris-Benedict equation, it requires values for both weight and height. Because of its wider range of subjects, it is considered to reflect the requirements of the modern US population with less estimation bias than other equations, and has recently been endorsed by the American Dietetic Association. (27) Its use may increase in those populations among whom obesity is becoming more common, but it does not yet appear to be in common use in Australia (1) and has not been subjected to as much critical scrutiny as the older equations, having been developed more recently Further research may help establish whether it will be of lasting use in practice.

Activity and injury factors

All three of the equations above were developed using healthy subjects, and may not accurately reflect the requirements of hospitalised patients. They estimate only basal or resting requirements, and therefore, it is customary to make adjustments to the value obtained, to allow for energy expended in activity and for the increased requirements due to illness. In practice, these adjustment factors are often applied in different ways. Typically, the result from the equation is multiplied by an activity factor (which may be only 1.0 for a sedated patient lying still in bed) and a stress or injury factor pertaining to the individual patient's condition. There is no evidence to support the less-common practice of adding the activity and stress factors before multiplying by the basal energy expenditure. It may be based on different assumptions about how activity and stress would increase requirements.

The activity and stress factors were not developed by the authors of the equations, but have been suggested by researchers investigating total energy expenditure in different states of illness or exercise. Table 4 lists activity factors derived from the FAO/WHO/UNU Expert Consultation Report and from a variety of other studies, mostly in healthy people. (13,28-33) Physical activity levels (PALs) are obtained by measuring total energy expenditure in free-living individuals and dividing by their BMR. This produces a multiple of the BMR that expresses the average energy requirement for that individual's level of daily activity. When the measured energy cost of a particular activity (climbing a ladder, for example) is expressed as a multiple of BMR, this is called the physical activity ratios (PARs) for that activity. Both PALs and PARs are expressed as a multiple of the BMR, and are therefore affected by any errors in measurement of BMR and total energy expenditure. (34) The PAR and PAL values are generated from non-fasting subjects, so they include the TEF: this means that an additional factor for TEF is not necessary when using these as activity factors to predict an individual's energy requirement. Illness may have an effect on TEF: the thermic response to nutrition can be increased in stress situations, while continuous tube feeding may reduce TEF close to the fasting level. (9) It is important to consider that illness and sedation cause a decrease in activity, so the activity component of total energy expenditure is likely to be lower for a hospital patient than for a healthy person performing the same activity. For example, studies of healthy people with sedentary jobs found that their activity factor averaged 1.5-1.7, (29,35) while Elia reports a variety of studies showing activity factors of only 1.15-1.3 in free-living people with chronic illnesses, and 1.0-1.2 in hospitalised people with acute diseases. (28)

Although illness tends to cause a decrease in physical activity, it can also cause an increase in energy requirements, by several different mechanisms. For example, inflammatory or infective illness can increase the BMR. Large wounds, such as in burn injury, cause loss of heat and body tissue. A fever may increase energy expenditure by about 10% of BMR for every centigrade degree above normal body temperature, (36) while inducing hypothermia (such as after stroke or cardiac arrest) decreases energy expenditure. (37) Pain and stress, too, increase energy expenditure, while sedation and pain control can decrease it. Energy expenditure is reduced further with heavier sedation. (38)

Since the early studies of energy expenditure were published, changes in patient care (particularly in the critically ill) such as improved pain management and respiratory support, avoidance of overfeeding, and more effective treatment of infections, have reduced the impact of illness on energy consumption. This means that older recommendations for injury factors are mostly too high. Newer research has also revealed a surprisingly wide variation in metabolic rates of patients with conditions that were previously assumed to be consistently hypermetabolic, such as cancer (39) and sepsis. (40,41) Such patients may have BMRs that are close to normal, or even below normal, and even where hypermetabolism occurs, it may be short-lived, peaking within a few days. (28) Unfortunately, the injury factors that are still in wide use (and which still appear in many textbooks) are those from the original classic paper by Long et al. from 1979, (42) and use of these may significantly overestimate requirements, as indicated by more recent studies. (28,43-45) One of the more comprehensive approaches is the study by Barak et al., (43) who derived injury factors for the critically ill using indirect calorimetry and compared them with existing factors in the literature. The paper by Elia (28) is a compilation of energy expenditure data for a variety of both acute and chronic illnesses. Table 5 displays some injury factors based on these recent studies.

The studies that derived these injury factors have most commonly used the Harris-Benedict equation, and consequently it has been argued that the factors are not valid to use with other equations. However, the injury factors represent the estimated degree of hypermetabolism as a multiple of the BMR, so in theory they should be applicable to any equation that accurately estimates BMR. In reality, none of the equations is free of bias or error, and this error may be increased if the injury factors are treated as fully transferable between equations. Even with equations known to have a similar bias (such as the Schofield and Harris-Benedict equations, which both tend to overestimate BMR) conservative selection of activity and injury factors may be necessary, to avoid overfeeding. Careful evaluation of the individual patient, with attention to biochemical parameters (such as albumin, C-reactive protein) and clinical signs (such as body temperature, minute ventilation, cardiac output, weight changes), can also help identify whether hypermetabolism is likely, justifying the use of a larger injury factor.

In critically ill patients, even when the energy requirement is significantly increased, it may be difficult, and even inappropriate, to meet these needs. Stress metabolism can interfere with utilisation of the extra energy, leading to an increased risk of overfeeding, and undesirable complications. (46) Conservative provision of nutrition support, or even deliberate underfeeding, is increasingly being recommended in these patients (47,48) despite their known increase in requirements.

Ireton-Jones equation (49)

This is one of the few equations available that have been developed and validated for use in hospitalised patients, rather than healthy people, and is notable for its lower estimates for heavier patients when compared with other commonly used equations. (50) The original equation (Table 6) was developed from a single study of 200 hospitalised patients, including patients with trauma and burns. Advantages of the Ireton-Jones equation include the fact that it uses the patient's actual weight, does not require a value for height, predicts total energy expenditure (therefore does not require activity or stress factors), and is subject to ongoing review by its authors and, therefore, may be more reflective of contemporary medical management than other, older equations. It takes into account specific clinical conditions, such as mechanical ventilation or trauma. However, it is important to be aware of the many assumptions made in developing this equation, which affect its use and interpretation. These include:

1 A patient is critically ill only while ventilated.

2 All burns and trauma are of the same severity, and affect energy requirement during the ventilated/critical illness phase only. This means that the equation does not account for an anabolic period of convalescence.

3 All modes of ventilation have the same impact on energy requirements.

4 All obese patients have the same body size and body composition at a given weight.

Several study groups have tested this equation on their own patients, usually by comparing the results of the equation (which estimates total energy expenditure) with indirect calorimetry measures of resting expenditure. This may be appropriate in a sedated critically ill patient, but is not a valid comparison if the patient has significant activity. Unsurprisingly, the Ireton-Jones equation produces a result that is significantly greater than the resting energy expenditure in such situations. (51,52) In a number of studies of sedated mechanically ventilated patients, the Ireton-Jones equations performed better than other equations (including the Harris-Benedict equation), but did show some bias towards underestimation. (50,53-56) Two studies of acutely ill hospital patients found that the Harris-Benedict equation used with an injury factor was more accurate than the Ireton-Jones equation. One of these studies looked at normal-weight ventilated critically ill patients (using a factor of 1.2); (55) the other was in acutely ill obese patients and used an adjusted weight value (with an injury factor of 1.3). (57)

OTHER CONSIDERATIONS

Which value to use for body weight has been a controversial issue for some time. All of the equations discussed above were developed using the actual weight of each subject, and the authors make no recommendations regarding the use of any other value. Use of an arbitrary adjusted weight value introduces an additional source of possible error, increasing the variability of the result. (58) However, there are many situations in which the patient's weight differs significantly from normal, and the use of the actual weight value can lead to unacceptable errors in estimating requirements. (59-61) It may therefore be appropriate sometimes to use a different value in the calculation.

If the patient is underweight, the use of the patient's current weight is likely to be the best way to estimate current energy requirements. However, in some patients, such as the critically ill, this may be an underestimate. (59) If it is appropriate to aim for weight gain, ideal weight can be used to estimate an ideal energy intake. However, this approach may be too aggressive for frail or unstable patients, and it may be necessary to select a more conservative goal weight at first, particularly in very underweight people. Close monitoring is desirable to ensure that the patient does not develop overfeeding-related complications. The risk of overfeeding is increased during illness, as stress metabolism alters fuel utilisation. (46) During this time, any weight gained will be mainly fat and fluid. (62) As a guide, weight change (either gain or loss) is not an appropriate goal during the period that an injury/stress factor applies to the patient.

If the patient is overweight or obese, or severely oedematous, using the actual weight can lead to an overestimation of the patient's requirements. The metabolic activity of adipose tissue is lower compared with other tissue, (63,64) so an obese patient has a lower metabolic rate per kg body weight. It has been suggested that the ratio of lean tissue to fat tissue changes as weight increases. That is, an increase in adipose tissue is supported by an increase in muscle and organ mass but, at the point of obesity, the fat stores are increasing disproportionately. (65,66) Studies using indirect calorimetry have obtained conflicting results on this point. (23-26,43,67,68) Some suggest that the ratio of lean tissue to fat tissue remains the same as weight increases, even in obesity; others indicate that equations using actual weight can grossly overestimate requirements in the obese. Use of an adjusted weight value is clearly problematic, (69) but may still be appropriate in order to avoid overestimating the patient's requirements in cases where overfeeding is particularly undesirable. These may include situations such as:

1 Where an obese or oedematous patient is not mobilising, such as bedbound or critically ill patients. An ambulant obese or oedematous patient has greater energy expenditure in everyday activities as a result of moving the extra body weight around. If the patient is not mobilising, this contribution to energy expenditure is absent.

2 Where overfeeding may be difficult to detect and is very undesirable, such as in mechanical ventilation or other respiratory compromise, or patients receiving parenteral nutrition.

3 Where the patient is sedentary in the long term, and muscle is not being maintained by activity (therefore, any extra weight is more likely to be adipose tissue), such as elderly patients or those who are otherwise mobility disabled.

4 In patient groups with known reduced energy needs, such as head injury patients after the acute period.

Usually the adjustment consists of using the ideal weight plus 25-50% of the excess weight. (43,57,67) For example, a patient weighing 100 kg whose ideal weight is 60 kg would have an adjusted weight value of 70-80 kg for use in predictive equations. Ideally, the choice of weight adjustment should be based on a physical assessment of the patient's tissue stores. A very muscular patient can be 'overweight' yet be very lean, and it would be appropriate to use actual weight in predicting the energy requirement. For most patients requiring an adjusted weight calculation, an average between ideal and actual weights would be appropriate, (43,57) while in extreme adiposity, where lean tissue stores appear depleted, or in oedema, where lean tissue is not contributing to the extra weight, an even lower adjusted weight might better reflect the patient's reduced metabolic activity. (67) Using an adjusted weight value requires caution, as it increases the risk of underfeeding in overweight patients. (27) If the patient is ambulant, it may be more appropriate to use an obesity-validated equation, such as the Mifflin-St Jeor equation (if height information is available) or the Ireton-Jones equation using the obesity factor.

Total energy or 'non-protein calories'?

In the past it has been suggested (most commonly in the context of parenteral nutrition) that a patient's energy requirements should be provided as 'non-protein calories', in order to spare the protein for healing and anabolism. However, predictive equations estimate the consumption rate of all energy, not just non-protein energy. If it is assumed that the estimated requirement refers only to the non-protein energy requirement, overfeeding will result, and beyond a certain point, giving extra energy will not improve protein sparing at all. As long as the protein requirement is met, it is sufficient to provide the estimated energy needs as the total energy input. (70,71)

CONCLUSION

The use of predictive equations for energy requirements has many pitfalls. There is little benefit in blindly applying an equation without paying attention to the individual characteristics of the patient and the situation. This can affect the credibility of the nutrition support dietitian, attracting terms like 'mumbo-jumbo' and 'dietitian's fudge factor'. An understanding of the origins and limitations of the equations is important for any dietitian who uses them.

The complex appearance of the equations unfortunately seems to give them more authority than they deserve. An equation is not a magic formula, and will not transform incorrect or inaccurate data into a useful result. For example, using an equation with both an estimated height and an estimated weight is probably no better than just making a conservative guess about an appropriate feed rate, or approximating requirements with a simple rule of thumb (such as a 'calories-per-kilo' method). Expressing the predicted requirement as a range, rather than a fixed value, may help avoid implying an unrealistic level of accuracy.

Most importantly, it is often forgotten that the equation only provides a suggested starting point for energy provision: the aim is not just to obtain the 'right answer' at the beginning and then walk away. Ongoing monitoring of the patient is essential, and this may involve regular re-estimation of requirements and adjustment of the feeding regimen as the patient's condition changes. An equation cannot replace other forms of assessment, such as physical examination, and is no substitute for quality patient care. However, when used by an informed and experienced practitioner, predictive equations can still be a valuable and time-saving tool, and retain a role in a dietitian's evidence-based clinical practice.

ACKNOWLEDGEMENTS

The authors wish to acknowledge Kathryn Marshall, Nicola Riley and Kellie Draffin for their valuable contributions to this paper.

REFERENCES

1 Reeves MM, Capra S. Variation in the application of methods used for predicting energy requirements in acutely ill patients: a survey of practice. Eur J Clin Nutr 2003; 57: 1530-35.

2 Elia M. Energy expenditure in the whole body. In: Kinney JM, Tucker HN, eds. Energy Metabolism: Tissue Determinants and Cellular Corollaries. New York: Raven Press, 1992; 19-59.

3 Reeves MM, Capra S. Predicting energy requirements in the clinical setting: are current methods evidence-based? Nutr Rev 2003; 61: 143-51.

4 van Raaij J. Energy. In: Mann J, Truswell AS, eds. Essentials of Human Nutrition, 2nd edn. New York: Oxford University Press, 2003; 81-94.

5 James WPT. From SDA to DIT to TEF. In: Kinney JM, Tucker HN, eds. Energy Metabolism: Tissue Determinants and Cellular Corollaries. New York: Raven Press, 1992; 163-86.

6 Black AE, Cole TJ. Within- and between-subject variation in energy expenditure measured by the doubly-labelled water technique: implications for validating reported dietary energy intake. Eur J Clin Nutr 2000; 54: 386-94.

7 Soares MJ, Shetty PS. Intra-individual variations in resting metabolic rates of human subjects. Hum Nutr Clin Nutr 1986; 40C: 365-9.

8 Daly JM, Heymsfield SB, Head CA et al. Human energy requirements: overestimation by widely-used prediction equation. Am J Clin Nutr 1985; 42: 1170-74.

9 McClave SA, Snider HL. Use of indirect calorimetry in clinical nutrition. Nutr Clin Pract 1992; 7: 207-21.

10 Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986; 1: 307-10.

11 Schofield WN, Schofield C, James WPT. Basal metabolic rate--review and prediction. Hum Nutr Clin Nutr 1985; 39C (Suppl. 1): 5-96.

12 Schofield WN. Predicting basal metabolic rate, new standards and review of previous work. Hum Nutr Clin Nutr 1985; 39C: 1-41.

13 World Health Organisation. Energy and protein requirements. Report of a Joint FAO/WHO/UNU Meeting. WHO Technical Report Series; 724. Geneva: World Health Organisation; 1985.

14 Muller MJ, Bosy-Westphal A, Klaus S et al. World Health Organization equations have shortcomings for predicting resting energy expenditure in persons from a modern, affluent population: generation of a new reference standard from a retrospective analysis of a German database of resting energy expenditure. Am J Clin Nutr 2004; 80: 1379-90.

15 Clark HD, Hoffer LJ. Reappraisal of the resting metabolic rate of normal young men. Am J Clin Nutr 1991; 53: 21-6.

16 Hayter JE, Henry CJ. A re-examination of basal metabolic rate predictive equations: the importance of geographic origin of subjects in sample selection. Eur J Clin Nutr 1994; 48: 702-7.

17 Piers LS, Diffey B, Soares MJ et al. The validity of predicting the basal metabolic rate of young Australian men and women. Eur J Clin Nutr 1997; 51: 333-7.

18 UK Department of Health. Report on health and social subjects 41: dietary reference values for food energy and nutrients for the United Kingdom. Report of the Panel on Dietary Reference Values of the Committee on Medical Aspects of Food Policy. London: Her Majesty's Stationery Office; 1991.

19 Snell R, ed. Dietitians' Pocket Book. Perth: Department of Nutrition, Dietetics and Food Science School of Public Health, Curtin University of Technology, 2006.

20 National Health and Medical Research Council (NHMRC). Recommended Dietary Intakes for Use in Australia. Canberra: National Health and Medical Research Council, 1991.

21 National Health and Medical Research Council (NHMRC) and New Zealand Ministry of Health. Nutrient Reference Values for Australia and New Zealand. Canberra: National Health and Medical Research Council, 2006.

22 Harris JA, Benedict FG. A Biometric Study of Basal Metabolism in Man. Carnegie Institute Publication no. 279. Washington: Carnegie Institute, 1919.

23 Owen OE, Kavle E, Owen RS et al. A reappraisal of the caloric requirements of healthy women. Am J Clin Nutr 1986; 44: 1-19.

24 Owen OE, Holup JL, D'Alessio DA et al. A reappraisal of the caloric requirements of healthy men. Am J Clin Nutr 1987; 46: 875-85.

25 Frankenfeld DC, Muth ER, Rowe WA. The Harris-Benedict studies of human metabolism: history and limitations. J Am Diet Assoc 1998; 98: 439-45.

26 Mifflin MD, St Jeor ST, Hill LA et al. A new predictive equation for resting energy expenditure in healthy individuals. Am J Clin Nutr 1990; 51: 241-7.

27 Frankenfield D, Roth-Yousey L, Compher C. Comparison of predictive equations for resting metabolic rate in healthy non-obese and obese adults: a systematic review. J Am Diet Assoc 2005; 105: 775-89.

28 Elia M. Insights into energy requirements in disease. Public Health Nutr 2005; 8: 1037-52.

29 Black AE, Coward WA, Cole TJ, Prentice AM. Human energy expenditure in affluent societies: analysis of 574 doubly-labelled water measurements. Eur J Clin Nutr 1996; 50: 72-92.

30 Goldberg GR, Black AE, Jebb SA et al. Critical evaluation of energy intake data using fundamental principles of energy physiology: 1. Derivation of cut-off limits to identify under-recording. Eur J Clin Nutr 1991; 45: 569-81.

31 Irsigler K, Veitl V, Sigmund A, Tschegg E, Kunz K. Calorimetric results in man: energy output in normal and overweight subjects. Metabolism 1979; 28: 1127-32.

32 James WPT, Ferro-Luzzi A, Waterlow JC. Definition of chronic energy deficiency in adults. Eur J Clin Nutr 1988; 42: 969-81.

33 Vaz M, Karaolis N, Draper A, Shetty P. A compilation of energy costs of physical activities. Public Health Nutr 2005; 8: 1153-83.

34 Shetty P. Energy requirements of adults. Public Health Nutr 2005; 8: 994-1009.

35 Westerterp KR, Plasqui G. Physical activity and human energy expenditure. Curr Opin Clin Nutr Metab Care 2004; 7: 607-13.

36 DuBois E. The basal metabolism in fever. JAMA 1921; 77: 352-7.

37 Bardutzky J, Georgiadis D, Kollmar R, Schwab S. Energy expenditure in ischemic stroke patients treated with moderate hypothermia. Int Care Med 2004; 30: 151-4.

38 Terao Y, Miura K, Saito M, Sekino M, Fukusaki M, Sumikawa K. Quantitative analysis of the relationship between sedation and resting energy expenditure in postoperative patients. Crit Care Med 2003; 31: 830-33.

39 Bauer J, Reeves MM, Capra S. The agreement between measured and predicted resting energy expenditure in patients with pancreatic cancer: a pilot study. JOP 2004; 5: 32-40.

40 McClave SA, Snider HL. Understanding the metabolic response to critical illness: factors that cause patients to deviate from the expected pattern of hypermetabolism. New Horiz 1994; 2: 139-46.

41 Weissman C, Kemper M. Assessing hypermetabolism and hypometabolism in the postoperative critically ill patient. Chest 1992; 102: 1566-71.

42 Long C, Schaffel N, Geiger J, Schiller W, Blakemore W. Metabolic response to injury and illness: estimation of energy and protein needs from indirect calorimetry and nitrogen balance. JPEN J Parenter Enteral Nutr 1979; 3: 452-6.

43 Barak N, Wall-Alonso E, Sitrin MD. Evaluation of stress factors and body weight adjustments currently used to estimate energy expenditure in hospitalized patients. JPEN J Parenter Enteral Nutr 2002; 26: 231-8.

44 Swinamer DL, Grace MG, Hamilton SM, Jones RL, Roberts P, King EG. Variation in the resting metabolic rate of mechanically ventilated critically ill patients. Crit Care Med 1990; 18: 657-61.

45 Uehara M, Plank L, Hill G. Components of energy expenditure in patients with severe sepsis and major trauma: a basis for clinical care. Crit Care Med 1999; 27: 1295-302.

46 Klein CJ, Stanek GS, Wiles CE. Overfeeding macronutrients to critically ill adults: metabolic complications. J Am Diet Assoc 1998; 98: 795-806.

47 Patino J, Echeverri de Pimiento S, Vergara A, Savino P, Rodriguez M, Escallon J. Hypocaloric support in the critically ill. World J Surg 1999; 23: 553-9.

48 Jeejeebhoy K. Permissive underfeeding of the critically ill patient. Nutr Clin Pract 2004; 19: 477-80.

49 Ireton-Jones C, Jones J. Improved equations for predicting energy expenditure in patients. Nutr Clin Pract 2002; 2: 29-40.

50 MacDonald A, Hildebrandt L. Comparison of fomulaic equations to determine energy expenditure in the critically ill. Nutrition 2003; 19: 233-9.

51 Das SK, Saltzman E, McCrory MA et al. Energy expenditure is very high in extremely obese women. J Nutr 2004; 134: 1412-16.

52 Hirano KM, Heiss CJ, Olson KE, Beerman KA, Brahler CJ. A comparison of calculated and measured resting energy expenditure in obese women. Top Clin Nutr 2001; 16 (61-69): 85-8.

53 Amato P, Keating KP, Quercia RA, Karbonic J. Formulaic methods of estimating calorie requirements in mechanically ventilated obese patients: a reappraisal. Nutr Clin Pract 1995; 10: 229-332.

54 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.

55 Alexander E, Susla GM, Burstein AH, Brown DT, Ognibene FP. Retrospective evaluation of commonly used equations to predict energy expenditure in mechanically ventilated, critically ill patients. Pharmacotherapy 2004; 24 (12 I): 1659-67.

56 Frankenfield D, Smith JS, Cooney RN. Validation of two approaches to predicting resting metabolic rate in critically ill patients. JPEN J Parenter Enteral Nutr 2004; 28: 259-64.

57 Glynn CC, Greene GW, Winkler MF. Predictive versus measured energy expenditure using limits-of-agreement analysis in hospitalised obese patients. JPEN J Parenter Enteral Nutr 1999; 23: 147-54.

58 Frankenfield DC, Rowe 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: 1152-9.

59 Campbell CG, Zander E, Thorland W. Predicted vs measured energy expenditure in critically ill, underweight patients. Nutr Clin Pract 2005; 20: 276-80.

60 Horgan GW, Stubbs J. Predicting basal metabolic rate in the obese is difficult. Eur J Clin Nutr 2003; 57: 335-40.

61 Shetty PS. Adaptation to low energy intakes: the responses and limits to low intakes in infants, children and adults. Eur J Clin Nutr 1999; 53 (Suppl.): S14-33.

62 Streat SJ, Beddoe AH, Hill GL. Aggressive nutritional support does not prevent protein loss despite fat gain in septic intensive care patients. J Trauma 1987; 27: 262-6.

63 McClave S, Snider H. Dissecting the energy needs of the body. Curr Opin Clin Nutr Metab Care 2001; 4: 143-7.

64 Elia M. Organ and tissue contribution to metabolic rate. In: Kinney JM, Tucker HN, eds. Energy Metabolism: Tissue Determinants and Cellular Corollaries. New York: Raven Press, 1992; 61-79.

65 Naeye RL, Roode P. The sizes and numbers of cells in visceral organs in human obesity. Am J Clin Path 1970; 54: 251-3.

66 Forbes GB. Lean body mass-body fat interrelationships. Nutr Rev 1987; 45: 225-31.

67 Cutts ME, Dowdy RP, Ellersieck MR, Edes TE. Predicting energy needs in ventilator-dependent critically ill patients: effect of adjusting weight for edema or adiposity. Am J Clin Nutr 1997; 66: 1250-56.

68 Ireton-Jones CS, Turner WW. Actual or ideal body weight: which should be used to predict energy expenditure? J Am Diet Assoc 1991; 91: 193-5.

69 Ireton-Jones C. Clinical controversies: adjusted body weight, con: why adjust body weight in energy expenditure equations? Nutr Clin Pract 2005; 20: 474-9.

70 VanWay C. Total calories vs non-protein calories. Nutr Clin Pract 2001; 16: 271-2.

71 Miles JM, Klein JA. Should protein calories be included in caloric calculations for a TPN prescription? Point-counterpoint. Nutr Clin Pract 1996; 11: 204-6.

Suzie FERRIE (1) and Meagan WARD (2)

(1) Royal Prince Alfred Hospital, Sydney, New South Wales, and (2) Department of Nutrition and Dietetics, Austin Health, Melbourne, Victoria, Australia

S. Ferrie, M.Nutr.Diet, APD, Critical Care Dietitian

M. Ward, M.Nutr.Diet, APD, Senior Dietitian

Correspondence: S. Ferrie, Department of Nutrition and Dietetics, Royal Prince Alfred Hospital, Missenden Road, Camperdown, NSW 2050, Australia. Email: suzie.ferrie@cs.nsw.gov.au
```Table 1 Schofield equation for estimating basal metabolic rate
(kJ/day) (11,12)

Age (years) Men Women

10-17 74 W + 2754 56 W + 2898
18-29 63 W + 2896 62 W + 2036
30-59 48 W + 3653 34 W + 3538
Original equations over 60 49 W + 2459 38 W + 2755
Modified equations 60-74 49.9 W + 2930 38.6 W + 2875
Modified equations over 75 35 W + 3434 41 W + 2610

W = weight (kg).

Table 2 Harris-Benedict equation for estimating resting energy
expenditure (kcal/day) (22)

Men 66.5 + 13.8 W + 5.0 H - 6.8 A
Women 655.1 + 9.6 W + 1.8 H - 4.7 A

Coefficients are rounded to one decimal place.
W = weight (kg); H = height (cm); A = age (year).

Table 3 Mifflin-St Jeor equation for estimating resting energy
expenditure (kcal/day) (26)

Men 10 W + 6.25H - 5 A + 5
Women 10 W + 6.25H - 5 A - 161

W = weight (kg); H = height (cm); A = age (year).

Table 4 Activity factors for adult hospital patients (13,28-33)

resting (lying or sitting)# 1.0-1.4 X
BMR
lying still, sedated or asleep 0.9-1.1
lying still, conscious 1.0-1.1
bedrest (moving self around bed) 1.15-1.2
sitting out of bed long periods 1.1-1.3
mobilising occasionally on ward 1.15-1.4
sedentary/light activity (standing for long periods)# 1.4-1.6
mobilising frequently on ward 1.4-1.5
+regular, intensive physiotherapy 1.5-1.6
moderate activity (continuous movement/slow walking)# 1.6-1.8

Factors in bold were obtained in healthy, free-living people.
BMR = basal metabolic rate.

Note: Factors indicated with # were obtained in healthy, free-living
people.

Table 5 Stress/injury factors for adult hospital patients (28,43-45)

Medical 1.1-1.2
(e.g. inflammatory bowel disease, liver or pancreatic disease)
Surgical 1.1-1.4
(e.g. transplant, fistula)
Cancer 1.1-1.4
(e.g. tumour or leukaemia)
Trauma 1.2-1.4
(e.g. skeletal or head injury or minor burns)
Sepsis 1.3-1.4
or other major infection
Major burns 1.4-1.6
Critical illness and/or major surgery/trauma
* With mechanical ventilation 1.2-1.4
* After the first week, for next 2-3 weeks (note limitations >1.6-1.8
on utilisation and risk of overfeeding)

Table 6 Ireton-Jones equation for estimating total energy expenditure
(kcal/day) (49)

Non-ventilated patients
629 - 11 A + 25 W - 609 O
Ventilated patients
1784 - 11 A + 5 W + 244 S + 239 T + 804 B

A = age (year); W = weight (kg).
O = 1 if obese (body mass index > 27); 0 otherwise.
S = 1 if patient is male; 0 otherwise.
T = 1 if trauma (include major surgery) is present; 0 otherwise.
B = 1 if burns are present; 0 otherwise.
```
COPYRIGHT 2007 Dietitians Association of Australia
No portion of this article can be reproduced without the express written permission from the copyright holder.