Quick Links

Recommendations Summary

CI: Determination of Resting Metabolic Rate 2012

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.

  • Recommendation(s)

    CI: Resting Metabolic Rate Predictive Equations for Non-Obese Critically Ill Adults

    If indirect calorimetry is not available, the Registered Dietitian (RD) should use the Penn State University [PSU(2003b)] equation in non-obese, critically ill mechanically-ventilated adults. Research indicates that this equation has the best prediction accuracy in non-obese patients.

    Rating: Fair

    CI: Resting Metabolic Rate Predictive Equations for Obese Critically Ill Adults

    If indirect calorimetry is not available, the Registered Dietitian (RD) should use the Penn State University [PSU(2003b)] equation in critically ill mechanically-ventilated adults with obesity who are less than 60 years of age. For obese patients 60 years or older, the PSU(2010) equation should be used. Research indicates that these equations have the best prediction accuracy.  

    Rating: Fair

    • 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 resting metabolic rate (RMR) with predictive equations will lead to under- or overfeeding.

    • Conditions of Application

      Predictive Equations: The PSU(2003b) and PSU(2010) predictive equations were designed for mechanically-ventilated patients.

      Indirect Calorimetry:

      The American Association of Respiratory Care (AARC) Clinical Practice Guidelines (2004) recommend that measurements may be indicated in patients with the following conditions:

      • Neuro trauma
      • Paralysis
      • COPD
      • Acute pancreatitis
      • Cancer with residual tumor
      • Multiple trauma
      • Amputations
      • Patients with no accurate height or weight
      • Long term acute care (ventilator units)
      • Severe sepsis
      • Extreme obesity
      • Severely hypermetabolic or hypometabolic patients
      • Failure to wean.

      The AARC Clinical Practice Guidelines (2004) also provide recommendations for hazards and complications, limitations of the procedures and infection control:

      Hazards and Complications

      • Short-term disconnection of patient from ventilator for connection to an indirect calorimetry machine may result in hypoxemia, bradycardia and patient discomfort
      • Inappropriate calibration or system setup may result in erroneous results, causing incorrect patient management
      • Isolation valves in calorimeters may increase circuit resistance and cause increased work of breathing or dynamic hyperinflation
      • Inspiratory reservoirs may cause reduction in alveolar ventilation, due to increased compressible volume of the breathing circuit
      • Manipulation of the vent circuit may cause leaks that may lower alveolar ventilation
        • Closed circuit calorimeters may cause a reduction in alveolar ventilation due to increased compressible volume of the breathing circuit
        • Closed circuit calorimeters may decrease the trigger sensitivity of the ventilator and result in increased patient work of breathing.

      Limitations of the Procedure

      • Accurate assessment of REE and RQ may not be possible because of patient condition or certain bedside procedures or activities
      • Leaks in ventilator circuit, endotracheal tube cuffs or uncuffed tubes, through chest tubes or bronchopleural fistula
      • Peritoneal and hemodialysis 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.
      • Inaccurate measures may be caused by:
        • Instability of delivered oxygen concentration (FIO2) within a breath or breath to breath due to changes in source gas pressure and ventilator blender/mixing characteristics
        • FIO2 above 60%
        • Inability to separate inspired from expired gases, due to bias flow with intermittent mandatory ventilation systems
        • Anesthetic gases other than O2, CO2 and nitrogen in the system
        • Water vapor presence
        • Inappropriate calibration
        • Total circuit flow exceeding internal gas flow of calorimeter
        • Leaks within the calorimeter
        • Inadequate measurement length.
      • Connection of the indirect calorimeter to certain ventilators with adverse effect on triggering mechanism, increased expiratory resistance, pressure measurement, or maintenance of the ventilator.

      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

      • Use standard precautions for contamination of blood and bodily fluids
      • Appropriate use of barriers and handwashing
      • Tubing to connect expired air from ventilator to indirect calorimetry should be disposed of or cleaned between patients
      • Connections in the inspiratory limb of the circuit should be wiped clean between patients and equipment distal to the humidifier should be disposed of
      • Bacteria filters may be used to protect equipment in inspire and expired lines.

    • Potential Costs Associated with Application

      Indirect calorimetry:

      • For patients who require mechanical ventilation, portable indirect calorimeters may cost at least $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.

    • Recommendation Narrative

      A total of 27 studies were included in the evidence analysis for this recommendation:

      • Thirteen positive quality cross-sectional studies (Alexander et al, 2004; Brandi, Santini et al, 1999; Casati et al, 1996; Cheng et al, 2002; Donaldson-Andersen et al, 1998; Epstein et al, 2000; Faisy et al, 2003; Flancbaum et al, 1999; Frankenfield et al, 2004; Marson et al, 2003; Ogawa et al, 1998; O'Leary-Kelley et al, 2005; and MacDonald and Hildebrandt, 2003) 
      • Five positive quality concurrent comparative studies (determine sensitivity and specificity of diagnostic test) (Anderegg et al, 2009; Boullata et al, 2007; Frankenfield (JPEN), 2010; Frankenfield et al, 2009; and Savard et al, 2008)
      • One positive quality comparison study (Stucky et al, 2008)
      • Six neutral quality cross-sectional studies (Brandi, Bertolini et al, 1999; Campbell et al, 2005; Cutts et al, 1997; Glynn et al, 1999; Ireton-Jones and Jones, 2002; and Jansen et al, 2002) 
      • One neutral quality observational cohort study (Compher et al, 2004) 
      • One neutral quality cohort study (Barak et al, 2002).

      Indirect calorimetry is the standard for determination of RMR in critically ill patients. When indirect calorimetry cannot be performed, predictive formulas may be necessary.

      Estimating Resting RMR in Non-obese Critically Ill Adults

      • Twenty-two studies evaluating nine predictive equations provide evidence for the following:
        • Four equations were precise and unbiased in the non-obese patients. These equations and their accuracy rates in patients less than 60 years and those 60 years or older, respectively, were: PSU(2003b) (69%, 77%); Brandi equation (61%, 61%), MSJE x 1.25 (54%, 54%) and Faisy equation (65%, 37%)
      • Evidence is based on the following studies: Alexander et al, 2004; Barak et al, 2002; Boullata et al, 2007; Brandi, Santini et al, 1999; Campbell et al, 2005; Casati et al, 1996; Cheng et al, 2002; Compher et al, 2004; Donaldson-Andersen et al, 1998; Epstein et al, 2000; Faisy et al, 2003; Flancbaum et al, 1999; Frankenfield et al, 2004; Frankenfield et al, 2009; Ireton-Jones and Jones, 2002; Jansen et al, 2002; MacDonald and Hildebrandt, 2003; Marson et al, 2003; Ogawa et al, 1998; O'Leary-Kelley et al, 2005; Savard et al, 2008; and Stucky et al, 2008.   
      • Click here to see all Predictive Equation Formulas.  

      Estimating RMR in Obese Critically Ill Adults

      • Eight studies comparing measured RMR with RMR predicted by several equations in critically ill patients with obesity provide evidence for the following:
        • The Penn State equation [PSU(2003b)] worked best and predicted RMR with 70% accuracy in obese patients. For a subset of obese critically ill patients 60 years old or older, a modified Penn State equation [PSU(2010)] predicted RMR with 74% accuracy. All other predictive equations tested had lower accuracy rates. 
      • Evidence is based on the following studies: Anderegg et al, 2009; Boullata et al, 2007; Cutts et al, 1997; Frankenfield et al, 2004; Frankenfield et al, 2009; Frankenfield (JPEN), 2010; Glynn et al, 1999; and Ireton-Jones and Jones, 2002.  

    • Recommendation Strength Rationale

      • Grade I evidence is available for the conclusion statements regarding the relationship between RMR and RMR predicted by the following equations in critically ill adult patients:
        • Fick equation 
        • Harris-Benedict equation (with stress and activity factors)
        • Harris-Benedict equation (without adjustments)
        • Ireton-Jones, 1997 equation
        • Swinamer equation 
      • Grade II evidence is available for the following conclusion statements regarding:
        • Relationship between RMR and RMR predicted by the following equations in critically ill adult patients:
          • Harris-Benedict Equation with adjustments for weight
          • Ireton-Jones, 1992 equation
          • Penn State equations 
        • Best way to estimate RMR in non-obese and obese adult critically ill patients if indirect calorimetry is unavailable or impractical 
      • Grade III evidence is available for the conclusion statements regarding the relationship between RMR and RMR predicted by the following equations in critically ill adult patients:
        • Faisy equation
        • Mifflin-St. Jeor equation
        • Brandi equation.

    • Minority Opinions