Recommendations Summary
CI: Determination of Resting Metabolic Rate (RMR) 2006
Click here to see the explanation of recommendation ratings (Strong, Fair, Weak, Consensus, Insufficient Evidence) and labels (Imperative or Conditional). To see more detail on the evidence from which the following recommendations were drawn, use the hyperlinks in the Supporting Evidence Section below.
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Recommendation(s)
CI: Indirect Calorimetry to determine RMR
Indirect calorimetry is the standard for determination of RMR in critically ill patients since RMR based on measurement is more accurate than estimation using predictive equations.
Rating: Strong
ImperativeCI: RMR Predictive equations for non-obese patients
If predictive equations are needed in non-obese, critically ill patients, consider using one of the following, as they have the best prediction accuracy of equations studied (listed in order of accuracy): Penn State, 2003a (79%), Swinamer (55%) and Ireton-Jones, 1992 (52%). In some individuals, errors between predicted and actual energy needs will result in under- or over-feeding.
Rating: Fair
ConditionalCI: Inappropriate RMR Predictive equations for this population
The Harris-Benedict (with or without activity and stress factors), the Ireton-Jones, 1997 and the Fick equation should not be considered for use in RMR determination in critically ill patients, as these equations do not have adequate prediction accuracy. In addition, the Mifflin-St. Jeor equation should not be considered for use in critically ill patients, as it was developed for healthy people and has not been well researched in the critically ill population.
Rating: Strong
ImperativeCI: RMR Predictive Equations for obese patients
If predictive equations are needed for critically-ill, mechanically-ventilated individuals who are obese, consider using Ireton-Jones, 1992 or Penn State, 1998, as they have the best prediction accuracy of equations studied. In some individuals, errors between predicted and actual energy needs will result in under- or over-feeding.
Rating: Fair
Conditional-
Risks/Harms of Implementing This Recommendation
- Anxiety may be caused by indirect calorimetry procedures employing a face mask or canopy
- In some individuals, estimation of RMR with predictive equations will lead to under- or over-feeding.
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Conditions of Application
Certain predictive equations were designed for application in mechanically-ventilated patients.
The AARC Clinical Practice Guidelines (1994) recommend that measurements may be indicated in patients with the following conditions:
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Neuro trauma
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Paralysis
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COPD
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Acute pancreatitis
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Cancer with residual tumor
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Multiple trauma
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Amputations
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Patients with no accurate height or weight
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Long term acute care (ventilator units)
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Severe sepsis
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Extreme obesity
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Severely hypermetabolic or hypometabolic patients
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Failure to wean.
The AARC Clinical Practice Guidelines (1994) also provide recommendations for hazards and complications, limitations of the procedures and infection control.
Hazards and Complications
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Short-term disconnection of patient from ventilator for connection to an indirect calorimetry machine may result in hypoxemia, bradycardia and patient discomfort
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Inappropriate calibration or system setup may result in erroneous results, causing incorrect patient management
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Isolation valves in calorimeters may increase circuit resistance and cause increased work of breathing or dynamic hyperinflation
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Inspiratory reservoirs may cause reduction in alveolar ventilation, due to increased compressible volume of the breathing circuit
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Manipulation of the vent circuit may cause leaks that may lower alveolar ventilation.
Limitations of the Procedure
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Leaks in ventilator circuit, endotracheal tube cuffs or uncuffed tubes, through chest tubes or bronchopleural fistula
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Peritoneal and hemo-dialysis procedures remove CO2 during the treatment and require a few hours after the treatment for acid-base to stabilize. Patients should not be measured during or for four hours after these dialysis treatments.
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Inaccurate measures may be caused by:
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Unstable O2 delivery, due to vent blender or mixing characteristics
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FIO2 above 60%
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Inability to separate inspired from expired gases, due to bias flow with intermittent mandatory ventilation systems
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Anesthetic gases other than O2, CO2 and nitrogen in the system
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Water vapor presence
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Inappropriate calibration
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Total circuit flow exceeding internal gas flow of calorimeter
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Leaks within the calorimeter
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Inadequate measurement length.
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Measures should be done by personnel trained in and with demonstrated and documented ability to calibrate, operate and maintain the calorimeter, having a general understanding of how mechanical ventilation works and recognizing calorimeter values within the normal physiologic range.
More frequent measures may be needed in patients with rapidly changing clinical course, as recognized by hemodynamic instability, spiking fevers, immediate postoperative status and ventilator weaning.
Infection Control
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Use standard precautions for contamination of blood and bodily fluids
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Appropriate use of barriers and handwashing
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Tubing to connect expired air from ventilator to indirect calorimetry should be disposed of or cleaned between patients
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Connections in the inspiratory limb of the circuit should be wiped clean between patients and equipment distal to the humidifier should be disposed of
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Bacteria filters may be used to protect equipment in inspire and expired lines.
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Potential Costs Associated with Application
- Cost of equipment, supplies and staff needs to be addressed in all indirect calorimetry measurements
- For patients who require mechanical ventilation, the cost of portable indirect calorimeters may be up to $35, 000 and the cost of tubing used to connect with the ventilator for gas collection varies
- The cost of trained staff to run the tests and maintain equipment can be considerable, since each test may require one hour of staff time
- Insurance companies may pay a technician fee for running the test and a professional fee if a licensed medical professional (e.g., MD) interprets the test.
- A calculator is required for equation calculation.
- Cost of equipment, supplies and staff needs to be addressed in all indirect calorimetry measurements
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Recommendation Narrative
Recommendation: Indirect Calorimetry for Determination of RMR
- Indirect calorimetry is the standard for determination of RMR in critically ill patients
- When indirect calorimetry cannot be performed, predictive formulas may be necessary
- 14 positive-quality cross-sectional studies: Casati et al, 1996; Donaldson-Andersen et al, 1998; Ogawa et al, 1998; Brandi et al, 1999; Flancbaum et al, 1999; Epstein et al, 2000; Cheng et al, 2002; Dickerson et al, 2002; Faisy et al, 2003; MacDonald and Hildebrandt, 2003; Marson et al, 2003; Alexander et al, 2004; Frankenfield et al, 2004; O'Leary-Kelley et al, 2005
- Eight neutral-quality cross-sectional studies: Cutts et al, 1997; Ireton-Jones, 1997; Ahmad et al, 1999; Brandi et al, 1999; Glynn et al, 1999; Ireton-Jones and Jones, 2002; Jansen et al, 2002; Campbell et al, 2005
- Two neutral-quality cohort studies: Barak et al, 2002; Compher et al, 2004
- Grade I.
Recommendation: Predictive Equations with Best Prediction Accuracy
- Seven cross-sectional studies comparing RMR and the Ireton-Jones, 1992, equations report similar mean values, however, for an individual, energy predictions may be different by as much as 500 kcal (52% of non-obese subjects predicted within 10% of RMR)
- Five of positive quality: Flancbaum et al, 1999; Cheng et al, 2002; Dickerson et al, 2002; MacDonald and Hildebrandt, 2003; Frankenfield et al, 2004
- Two of neutral quality: Ireton-Jones, 1997; Campbell et al, 2005.
- Further research regarding the accuracy of the Ireton-Jones, 1992 equation is warranted
- Grade III.
- Two positive quality cross-sectional studies comparing RMR and the Penn State equation report adequate precision (79% of non-obese subjects predicted within 10% of RMR): MacDonald and Hildebrandt, 2003; Frankenfield et al, 2004
- Further research in the critically ill population is needed regarding the Penn State equation
- Grade III.
- One positive-quality cross-sectional study comparing RMR and the Swinamer equation, reported that 55% of non-obese subjects were predicted within 10% of RMR: MacDonald and Hildebrandt, 2003
- Further research in the critically ill population is needed regarding the Swinamer equation
- Grade III.
Recommendation: Predictive Equations with Inadequate Prediction Accuracy
- 13 studies comparing RMR and the Harris-Benedict equation (without adjustments) generally report an underestimation of energy needs in the critically ill population by as much as 1, 000kcal or more
- Nine positive-quality cross-sectional studies: Donaldson-Andersen et al, 1998; Brandi, Santini et al, 1999; Flancbaum et al, 1999; Cheng et al, 2002; Dickerson et al, 2002; Faisy et al, 2003; MacDonald and Hildebrandt, 2003; Alexander et al, 2004; Frankenfield et al, 2004
- Three neutral-quality cross-sectional studies: Ireton-Jones, 1997; Ahmad et al, 1999; Campbell et al, 2005
- One neutral-quality cohort study: Compher et al, 2004
- Grade I.
- 13 studies comparing RMR and the Harris-Benedict equation with stress and activity factors ranging from 1.1 to 1.6 may be biased or imprecise by 900kcal or more, depending on the factors used
- Cross-sectional studies include nine of positive quality: Casati et al, 1996; Donaldson-Andersen et al, 1998; Brandi and Santini et al, 1999; Cheng et al, 2002; Dickerson et al, 2002; Faisy et al, 2003; MacDonald and Hildebrandt, 2003; Alexander et al, 2004; O'Leary-Kelley et al, 2005
- Four cross-sectional studies of neutral quality: Cutts et al, 1997; Ireton-Jones and Jones, 2002; Jansen et al, 2002
- One neutral-quality retrospective cohort study: Barak et al, 2002
- Grade I.
- Five cross-sectional studies comparing RMR and the Fick equation generally report little agreement between methods
- Four of positive quality: Ogawa et al, 1998; Flancbaum et al, 1999; Epstein et al, 2000; Marson et al, 2003; one neutral quality: Brandi, Santini et al, 1999
- Grade I.
- Three cross-sectional studies comparing RMR and the updated Ireton-Jones, 1997, equations report similar mean values. However, only 36% of subjects were predicted within 10% of RMR
- Two of positive quality: Alexander et al, 2004; Frankenfield et al, 2004
- One of neutral quality: Ireton-Jones and Jones, 2002
- Further research in the critically ill population is needed regarding the Ireton-Jones, 1997, equations
- Grade II.
- At the current time, the Mifflin-St. Jeor equation has not been adequately researched in the critically ill population, according to one positive-quality cross-sectional study (Frankenfield et al, 2004) for descriptive purposes only
- Further research in the critically ill population is needed regarding the Mifflin-St. Jeor equation
- Grade V.
Recommendation: Predictive Equations for Critically Ill Individuals with Obesity
- Studies including critically ill individuals with obesity studied the application of several predictive equations and the use of actual or adjusted weight in six studies
- One positive-quality cross-sectional study: Frankenfield et al, 2004
- Four neutral-quality cross-sectional studies: Cutts et al, 1997; Ireton-Jones, 1997; Glynn et al, 1999; Ireton-Jones and Jones, 2002
- One positive-quality retrospective cohort study: Barak et al, 2002.
- One study reported that the Harris-Benedict equation, using actual weight multiplied by a factor of 1.2 (60% of subjects predicted within 10% of RMR) or using an adjusted weight multiplied by a factor of 1.3 (67% of subjects predicted within 10% of RMR), resulted in the most accurate predictions: Glynn et al, 1999
- A second study reports that the Penn State, 2003a, equation predicts within 10% of RMR in 61% of subjects and the Ireton-Jones, 1992, equations predict within 10% of RMR in 72% of subjects: Frankenfield et al, 2004
- Further research is needed in critically ill individuals with obesity
- Grade III.
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Recommendation Strength Rationale
- Recommendation: Indirect Calorimetry for Determination of RMR
- A number of studies were identified
- Conclusion statement was Grade I.
- Recommendation: Predictive Equations with Best Prediction Accuracy
- A number of studies were identified, but the research was limited or inconclusive
- Conclusion statement for Ireton-Jones, 1992, was Grade III
- Conclusion statement for Penn State was Grade III
- Conclusion statement for Swinamer was Grade III.
- Recommendation: Predictive Equations with Inadequate Prediction Accuracy
- A number of studies were identified, but the research was limited or inconclusive
- Conclusion statement for Fick was Grade I
- Conclusion statement for Harris-Benedict was Grade I
- Conclusion statement for Ireton-Jones, 1997, was Grade II
- Conclusion statement for Mifflin-St. Jeor was Grade V.
- Recommendation: Predictive Equations with Inadequate Prediction Accuracy
- A number of studies were identified, but the research was limited or inconclusive
- Conclusion statement was Grade III.
Click here to link to the page that lists the Predictive Equations for Determining Resting Metabolic Rate.
- Recommendation: Indirect Calorimetry for Determination of RMR
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Risks/Harms of Implementing This Recommendation
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Supporting Evidence
The recommendations were created from the evidence analysis on the following questions. To see detail of the evidence analysis, click the blue hyperlinks below (recommendations rated consensus will not have supporting evidence linked).
What is the most accurate method for determination of resting metabolic rate (RMR) in critically ill patients?
In critically ill patients, what is the relationship between resting metabolic rate (RMR) and RMR predicted by the Fick equation?
In critically ill patients, what is the relationship between resting metabolic rate (RMR) and RMR predicted by the Harris-Benedict equation (without adjustments)?
In critically ill patients, what is the relationship between resting metabolic rate (RMR) and RMR predicted by the Harris-Benedict equation (with stress and activity factors)?
In critically ill patients, what is the relationship between resting metabolic rate (RMR) and RMR predicted by the Ireton-Jones 1992 equations?
In critically ill patients, what is the relationship between resting metabolic rate (RMR) and RMR predicted by the Ireton-Jones 1997 equations?
In critically ill patients, what is the relationship between resting metabolic rate (RMR) and RMR predicted by the Penn State equation?
In critically ill patients, what is the relationship between resting metabolic rate (RMR) and RMR predicted by the Mifflin-St. Jeor equation?
In critically ill patients, what is the relationship between resting metabolic rate (RMR) and RMR predicted by the Swinamer equation?
What is the most accurate predictive equation for estimating energy needs in critically ill patients with obesity?-
References
Brandi LS, Bertolini R, Santini L. Calculated and measured oxygen consumption in mechanically ventilated surgical patients in the early post-operative period. Eur J Anaesthesiol 1999;16(1):53-61.
Epstein CD, Peerless JR, Martin JE, Malangoni MA. Comparison of methods of measurements of oxygen consumption in mechanically ventilated patients with multiple trauma: The Fick method vs. indirect calorimetry. Crit Care Med. 2000; 28(5): 1,363-1,369.
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(3):461-6.
Marson F, Martins MA, Coletto FA, Campos AD, Basile-Filho A. Correlation between oxygen consumption calculated using Fick's method and measured with indirect calorimetry in critically ill patients. Arq Bras Cardiol 2003;81:77-81.
Ogawa AM, Shikora SA, Burke LM, Heetderks-Cox JE, Bergren CT, Muskat PC. The thermodilution technique for measuring resting energy expenditure does not agree with indirect calorimetry for the critically ill patient. JPEN 1998; 22: 347-351.
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): 1,659-1,667.
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 2002; 26(4):231-8.
Brandi LS, Santini L, Bertolini R, Malacarne P, Casagli S, Baraglia AM. Energy expenditure and severity of injury and illness indices in multiple trauma patients. Crit Care Med 1999;27(12):2684-9.
Casati A, Colombo S, Leggieri C, Muttini S, Capocasa T, Gallioli G. Measured versus calculated energy expenditure in pressure support ventilated ICU patients. Minerva Anestesiol. 1996; 62 (5): 165-170.
Cheng CH, Chen CH, Wong Y, Lee BJ, Kan MN, Huang YC. Measured versus estimated energy expenditure in mechanically ventilated critically ill patients. Clin Nutr. 2002; 21 (2): 165-172.
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;66:1250-6.
Dickerson RN, Gervasio JM, Riley ML, Murrell JE, Hickerson WL, Kudsk KA, Brown RO. Accuracy of predictive methods to estimate resting energy expenditure of thermally-injured patients. JPEN. 2002; 26 (1): 17-29.
Donaldson-Andersen J, Fitzsimmons L. Metabolic requirements of the critically ill, mechanically ventilated trauma patient: measured versus predicted energy expenditure. Nutr Clin Pract 1998;13(1):25-31.
Faisy C, Guerot E, Diehl JL, Labrousse J, Fagon JY. Assessment of resting energy expenditure in mechanically ventilated patients. Am J Clin Nutr. 2003; 78: 241-249.
Glynn CC, Greene GW, Winkler MF, Albina JE. Predictive versus measured energy expenditure using limits-of agreement analysis in hospitalized, obese patients. JPEN 1999;23:147-154.
Ireton-Jones C, Jones JD. Improved equations for predicting energy expenditure in patients: the Ireton-Jones equations. Nutr Clin Pract 2002;17(1):29-31.
Jansen MMPM, Heymer F, Leusink JA, de Boer A. The quality of nutrition at an intensive care unit. Nutrition Research 2002;22(4):411-422.
MacDonald A, Hildebrandt L. Comparison of formulaic equations to determine energy expenditure in the critically ill patient. Nutrition 2003;19(3):233-9.
O'Leary-Kelley CM, Puntillo KA, Barr J, Stotts N, Douglas MK. Nutritional adequacy in patients receiving mechanical ventilation who are fed enterally. Am J Crit Care 2005; 14(3):222-31.
Ahmad A, Duerksen DR, Munroe S, Bistrian BR. An evaluation of resting energy expenditure in hospitalized, severely underweight patients. Nutrition 1999;15(5):384-8.
Campbell CG, Zander E, Thorland W. Predicted vs measured energy expenditure in critically ill, underweight patients. Nutr Clin Pract 2005;20(2):276-80.
Compher C, Cato R, Bader J, Kinosian B. Harris-Benedict equations do not adequately predict energy requirements in elderly hospitalized African Americans. J National Med Assoc 2004;96(2):209-214.
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 C. Comparison of the metabolic response to burn injury in obese and nonobese patients. J Burn Care Rehabil 1997;18(1 Pt 1):82-5. -
References not graded in Academy of Nutrition and Dietetics Evidence Analysis Process
American Association for Respiratory Care (AARC). Metabolic measurement using indirect calorimetry during mechanical ventilation. Clinical practice guidelines. Respir Care. 1994; 39 (12): 1, 170-1, 175.
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References