Precision Nutrition Podcast Series

Hi, I am Dr. Robert Bilkovski. Welcome to this podcast series on Indirect Calorimetry. In this first episode, we will go over the principles of energy metabolism.

The goals of this podcast are to:

• Provide you with an understanding of the difference in calories between fat, carbohydrates and proteins,
• Discuss the components of energy expenditure, which includes Resting Energy Expenditure, the Diet-induced Thermogenesis and Activity Energy Expenditure, and
• How measurement of energy expenditure can inform feeding status, whether that being over-fed or under fed. This will be guided by the RQ or respiratory quotient.

Let´s begin. First, to put nutrition and calorie mix into perspective, we must understand how the body acts at rest. During periods of rest, like we are now listening to this podcast, the body preferentially metabolizes fat rather than carbohydrates.

In contrast, during periods of activity, the body switches to preferential utilization of carbohydrates. For those athletes amongst us, who have heard of the importance of muscle glycogen and the need to consume carbohydrates during periods of endurance activities?

The RQ, or Respiratory Quotient is utilized to inform not only calorie mix but adequacy of feeding. First the goal for calorie mix is an RQ between 0.8 and 1.0 which implies a balanced mix of calories¹.

While a respiratory quotient that is less than 0.7 suggests predominantly fat utilization and that the patient is at risk of being overfed.

In contrast, a respiratory quotient that is greater than 1.3 suggests predominant carbohydrate consumption and the risk of over-feeding may be present.

In summary, an RQ or respiratory quotient close to 0.8 is ideal and most closely reflects the metabolism of the body at rest.

Measurement tools used to determine energy expenditure includes indirect calorimetry, while other equations and algorithms can be used for this measure; and these estimates will be discussed in more detail in future podcasts.

Indirect calorimetry is a measurement of respiratory gas exchange at the alveolar level in order to inform cellular metabolic activity. What indirect calorimetry can measure includes total energy expenditure and respiratory quotient (as we discussed earlier).

RQ is derived from the oxygen consumption, which is also noted as VO2, and the production or elimination of carbon dioxide which is noted as VCO2.

• VO2 is a reflection of the balance between oxygen delivered to the tissue and the metabolic needs of that tissue, these metabolic needs can change between sleeping, exercise, or the extreme cases of burns and sepsis.
• VCO2, in contrast, is a measurement that reflects oxidation of nutrients at the cellular level, where VCO2 measurements are dependent on the adequacy of ventilation at the alveoli.
• In summary, the RQ is derived from the following equation, of VCO2 divided by VO2.

More importantly, the respiratory quotient at steady state is equal to the resting energy metabolism.

As stated earlier, RQ can be helpful to inform nutrient mix as well as caloric needs. The goal is to provide adequate calories through fats, proteins and carbohydrates while at the same time avoiding over and under-feeding to strike that balance, one must understand factors that may increase or decrease energy expenditure of a patient.

A few notable examples that increase energy expenditure includes fever and sepsis, pain and large open wounds such as burns.

In contrast, a reduction in energy expenditure can result from advanced age, obesity and the use of narcotic analgesics and neuromuscular blocking agents².

Both over and under-feeding have the potential for unwanted patient complications, namely, prolongation of mechanical ventilation. Over-feeding also increases the risk of infection, hypercapnia as well as liver and bile duct flow issues. In contrast, under-feeding can decrease lean muscle mass and result in diminished muscle function^3,4.

In closing, the measurement of energy expenditure provides the clinician with a tool that allows them to tailor nutritional support, in order to prevent over-feeding and under-feeding.

The next podcast will go into more details on ways energy expenditure can be determined at the bedside.

Thank you for listening to this podcast on the Principles of Energy Metabolism. In the next podcast we will compare indirect calorimetry and predictive equations.

References

1. McClave, S. A., et al. (2003). "Clinical use of the respiratory quotient obtained from indirect calorimetry." JPEN J Parenter Enteral Nutr 27(1): 21-26
2. Weissman, C., et al. (1986). "The energy expenditure of the mechanically ventilated critically ill patient. An analysis." Chest 89(2): 254-259
3. Singer, P., et al. (2011). "The tight calorie control study (TICACOS): a prospective, randomized, controlled pilot study of nutritional support in critically ill patients." Intensive Care Med 37(4): 601-609.Petros, S., et al. (2016). "Hypocaloric vs Normocaloric Nutrition in Critically Ill Patients: A Prospective Randomized Pilot Trial." JPEN J Parenter Enteral Nutr 40(2): 242-249.
4. Casaer, M. P., et al. (2011). "Early versus late parenteral nutrition in critically ill adults." N Engl J Med 365(6): 506-517

Hi, I am Dr. Robert Bilkovski. Welcome to this podcast series on Indirect Calorimetry. In this episode, we will go over a comparison between indirect calorimetry and predictive equations.

The goals of this podcast are to share:

• The principles of indirect calorimetry and a high-level understanding of the Weir Formula
• The basis of predictive equations and their limitations
• Limitations associated with indirect calorimetry

In this next segment, we will go into more detail on how the various means energy expenditure can be assessed at the bedside. Moreover, the strengths and/or limitations of each method will be discussed.

To start, Resting Energy Expenditure can be determined through the use of predictive equations that use a combination of body weight, age, or height.

One of the most widely recognized and used predictive equations is the Harris Benedict equation, which uses a combination of body weight, height, and age to predict energy expenditure. This predictive equation was first introduced in the early 20^th century.

Other equations include The World Health Organization, the WHO-2 and the Penn State, just to name a few.

Of note, some predictive equations have been modified for use with pediatric patients¹.

Predictive equations do not incorporate patient-specific information beyond those mentioned earlier. In contrast indirect calorimetry utilizes the respiratory gas measurements, namely oxygen and carbon dioxide to determine Resting Energy Expenditure.

Indirect calorimetry incorporates these gas measurements into a formula called the Weir Formula in order to inform on energy expenditure.

The specifics of the Weir formula are too complex to describe in this podcast, but simply include VCO2, VO2 and the 24-hour urinary nitrogen measurement. However, urinary nitrogen represents roughly 4% of the total energy expenditure and is often excluded.

A final method to measure expenditure is via use of the Fick method.

This measurement requires the placement of a pulmonary artery catheter, which is invasive and limits widespread use.

The Fick method relies on the measurement of cardiac output using a principle called thermodilution combined with measurement of both arterial and mixed venous oxygen content.

The Fick method is beyond further discussion in this podcast, most notably, given that it requires a pulmonary artery catheter and has a sizeable inherent error. In fact, that error can be up to 15% due to the variation in cardiac output which occurs during a respiratory cycle².

Therefore, let us focus on predictive equations and indirect calorimetry. The convenience of predictive equations is readily apparent for energy expenditure determinations could be obtained without additional technology and interpretive skills, however, use in critical care environments has shown them to be sub-optimal when compared to indirect calorimetry³.

Similarly, studies conducted by Karlsson and Jotterand demonstrated that the predictive equations were inaccurate compared to indirect calorimetry in the elderly and pediatric populations, respectively^4,5.

In a largest study conducted comparing predictive equations and indirect calorimetry, with over 1400 patients assessed, Zusman showed that all equations tested had relatively poor correlation and agreement with indirect calorimetry.

The correlation ranged between 0.36 to 0.54, while agreement was between 0.3 and 0.5.

In total, eight predictive equations were assessed, and the percentage of error was at or greater that 20% amongst all equations studied.

The author concluded that: “both underfeeding and overfeeding are harmful and that optimizing nutrition to patient-specific needs is an urgent task” and “the optimal way to define caloric goals is ideally preferred using indirect calorimetry.”

The author flagged one important limitation of predictive equations when compared to the use of indirect calorimetry - the predictive equations failed to capture the dynamic metabolic changes that occurred during a patient's critical illness, where repeated measures of indirect calorimetry can better capture these changes over time.

These authors helped to validate that indirect calorimetry provides both timely and better assessment of nutritional needs, compared to predictive equations. It is important to note that indirect calorimetry has some limitations that may impact accuracy⁶.

The limitation to indirect calorimetry is categorized into four main areas that are - leaks, high FiO2, hemodynamic shifts and humidity in the breathing circuit⁷.

Leaks can occur in an array of places within the breathing circuit, including the endotracheal tube, the breathing circuit, and the ventilator itself.

There are inherent inaccuracies as a result of the Haldane transformation, which is used to calculate inhaled gas volumes from exhaled gas measurements, when the inspiratory fraction of oxygen is high for the difference between inspired and exhaled oxygen concentrations become very small. Thus, the indirect calorimetry measurements become more error-prone at increasing FiO2, notably above 70%;

The presence of moisture and humidity within the breathing circuit can have a negative impact on assessing volume measurements.

Lastly, hemodynamic shifts such as fluid challenges or hemodialysis can impact cardiac output, and in turn, energy expenditure determinations.

In closing, indirect calorimetry is the preferred means to evaluate the caloric needs of critically ill patients for it has been shown to be more accurate compared to predictive equations and can be used throughout the course of illness as metabolic needs of a patient will change.

In future podcasts, the clinical use cases and ASPEN guidelines will be discussed in addition to the principles of steady state determination as applied to indirect calorimetry measurement.

Thank you for listening to this podcast on a comparison between Indirect Calorimetry and predictive equations. In the next podcast we will go over clinical use cases and review ASPEN guidelines.

References

1. Jotterand Chaparro, C., et al. (2017). "Performance of Predictive Equations Specifically Developed to Estimate Resting Energy Expenditure in Ventilated Critically Ill Children." J Pediatr 184: 220-226.e225
2. Oshima, T., et al. (2017). "Indirect calorimetry in nutritional therapy. A position paper by the ICALIC study group." Clin Nutr 36(3): 651-662.
3. [Source: Zusman, O., et al. (2016). "Resting energy expenditure, calorie and protein consumption in critically ill patients: a retrospective cohort study." Crit Care 20(1): 367.
4. Jotter and Chaparro, C., et al. (2018). "Estimation of Resting Energy Expenditure Using Predictive Equations in Critically Ill Children: Results of a Systematic Review." JPEN J Parenter Enteral Nutr 42(6): 976-986.
5. Karlsson, M., et al. (2017). "Ability to predict resting energy expenditure with six equations compared to indirect calorimetry in octogenarian men." Exp Gerontol 92: 52-55.
6. Zusman, O., et al. (2016). "Resting energy expenditure, calorie and protein consumption in critically ill patients: a retrospective cohort study." Crit Care 20(1): 367.
7. [Source: Oshima, T., et al. (2017). "Indirect calorimetry in nutritional therapy. A position paper by the ICALIC study group." Clin Nutr 36(3): 651-662.]

In this podcast, we will turn our focus from the theoretical to the practical as we discuss energy metabolism and the use of indirect calorimetry.

In this podcast, the objectives will focus on the following:

First, you will gain an understanding of several of the more widely used clinical use cases which includes burn, pediatric and management of the obese patient.

Second, you will obtain a high-level understanding of the ASPEN guidelines and some of their key takeaways.

Let us start off on the clinical use cases where indirect calorimetry has been studied and demonstrated favorable impacts to patient care. The use cases covered include burn patients, the elderly, the obese, pediatrics and closing out with surgical care.

Burn patients, especially those that have sizable body surface areas affected present a challenge to the clinical team for the patient's open wounds cause large fluid shifts, are open sources for infection and result in large metabolic changes. From a nutritional support perspective, the goal is to accurately determine the caloric requirements during this hypermetabolic state and minimize the risk of overfeeding.¹

The magnitude and duration of these hypermetabolic changes have been shown to be much greater when compared to trauma or septic patients.

Both Clark and Moreira in their publications state that indirect calorimetry is the gold standard for energy expenditure determination in burn patient care. In addition, indirect calorimetry should be measured daily in order to capture the day-to-day metabolic changes in this patient population that may extend into the recovery phase.^1,2

Turning our attention to the elderly, there have been several noteworthy publications on this topic. First, in those aged greater than 65 years with sepsis, a resting energy metabolism was found to be lower than otherwise predicted.³

While six predictive equations were evaluated for accuracy compared to indirect calorimetry, on a whole, these equations had modest performance and were dependent on knowing the fat-free mass of the patient.⁴

Similarly, in critically ill elderly patients, predictive equations showed limited accuracy when compared to calorimetry.⁵

Amongst obese patients, authors from the ICALIC Study Group concluded that,

1. obese patients constitute a growing population of the ICU patient population;⁶
2. the energy requirements are poorly addressed by predictive equations;⁶ and
3. indirect calorimetry is the only way to determine metabolic requirements with accuracy.⁶

With a special focus on the pediatric obese patient, Martinez in a 2017 study showed that energy delivery based on predictive equations was 34.6% of estimated, and this patient population, when compared to the non-obese, had increased ICU lengths of stay and mortality.

This implies the importance of greater accuracy in measuring energy requirements and where indirect calorimetry can support and improve patient outcomes.⁷

Similar to what has been observed in adult patients, the use of predictive equations in critically ill children was also found to be unacceptable in measuring resting energy expenditure across a wide range of caloric needs that spanned 200 to 1000 kilocalories per day.⁸

In total, 15 predictive equations were tested and the authors concluded that indirect calorimetry is preferred in this population.⁸

Lastly, Larsen conducted a retrospective analysis of the pediatric ICU patients to determine the incidence of over and under feeding.

In total, only 12.4% of patient measurements of energy expenditure showed that they received appropriate feeding.⁹

While just over 34% and 53% were either underfed or overfed, respectively.⁹

The final use case discussed care of the surgical patient.

Indirect calorimetry has been shown to predict adverse outcomes in cardiac surgery patients, where non-survivors had a statistically significantly greater respiratory quotient than survivors. This finding may suggest that a shift to anaerobic metabolism is occurring and portends a poor prognosis. ¹⁰

Furthermore, the Caloric Control in Cardiac Surgery Trial showed that malnutrition is common in cardiac surgery patients and the incidence ranges between 10 to 25%.

The malnourished patients had greater in-hospital mortality, increased hospital length of stay, longer duration of vasopressor use, and more frequent positive blood cultures. ¹¹

Amongst polytrauma patients, indirect calorimetry used to measure respiratory quotient showed that the RQ negatively correlated with both ventilator duration and ICU length of stay. This suggests that indirect calorimetry may be useful in optimizing the care of this patient population.¹²

Let's close out this podcast on the topic of ASPEN guidelines. ASPEN stands for the American Society of Parental and Enteral Nutrition and they have published a series of guidelines on topics related to nutritional support.

Of note, in 2021, the ASPEN guidelines on nutritional support of the adult critically ill patient was most recently updated. While for pediatric critically ill patients, the last edition was published in 2017. ^13,14

Regarding guideline recommendations for adults, some of the highlights include:

1 - There were no differences in harms or outcomes between the use of enteral or parenteral feeding in the first week of care;¹⁵

2 - There is no clinical outcome improvement in providing supplemental parenteral nutrition during the first week of illness.¹⁵

And 3 - One addition, a carryover from 2016 guidelines which is important to restate is that  “indirect calorimetry be used to determine energy requirements when available.” ¹⁵

In closing, the ASPEN guidelines serve as a valuable resource to guide clinicians on how one can optimize nutritional support practices by leveraging the latest  evidence-based medicine.

Our next segment of this podcast series will turn our attention to the more practical aspects of indirect calorimetry, specifically the importance of steady state determination.

References:

1. Clark, A., et al. (2017). "Nutrition and metabolism in burn patients." Burns Trauma 5: 11
2. Moreira, E., et al. (2018). "Update on metabolism and nutrition therapy in critically ill burn patients." Med Intensiva 42(5): 306-316
3. Ebihara, T., et al. (2018). "Low energy expenditure among elderly patients in acute, sepsis."  Clinical Nutrition 37: S174.
4. Karlsson, M., et al. (2017). "Ability to predict resting energy expenditure with six equations compared to indirect calorimetry in octogenarian men."  92: 52-55.
5. Segadilha, N., et al. (2017). "Energy Expenditure in Critically Ill Elderly Patients: Indirect Calorimetry vs Predictive Equations." JPEN J Parenter Enteral Nutr 41(5): 776-784.
6. Oshima, T., et al. (2017). "Indirect calorimetry in nutritional therapy. A position paper by the ICALIC study group." Clin Nutr 36(3): 651-662.
7. Martinez, E. E., et al. (2017). "Energy and Protein Delivery in Overweight and Obese Children in the Pediatric Intensive Care Unit." Nutr Clin Pract 32(3): 414-419.
8. Jotterand Chaparro, C., et al. (2017). "Performance of Predictive Equations Specifically Developed to Estimate Resting Energy Expenditure in Ventilated Critically Ill Children." J Pediatr 184: 220-226.e225.
9. Larsen, B. M. K., et al. (2018). "Can energy intake alter clinical and hospital outcomes in PICU?" Clin Nutr ESPEN 24: 41-46.
10. Piot, J., et al. (2018). "An elevated respiratory quotient predicts complications after cardiac surgery under extracorporeal circulation: an observational pilot study." J Clin Monit Comput.
11. De Waele, E., et al. (2018). "Does the use of indirect calorimetry change outcome in the ICU? Yes it does." Curr Opin Clin Nutr Metab Care 21(2): 126-129.
12. Patkova, A., et al. (2018). "Prognostic value of respiratory quotients in severe polytrauma patients with nutritional support."  49: 90-95.
13. Compher, C, Bingham, AL, McCall, M, et al. Guidelines for the provision of nutrition support therapy in the adult critically ill patient: The American Society for Parenteral and Enteral Nutrition. J Parenter Enteral Nutr. 2022; 46: 12– 41
14. Mehta, N.M., Skillman, H.E., Irving, S.Y., Coss-Bu, J.A., Vermilyea, S., Farrington, E.A., McKeever, L., Hall, A.M., Goday, P.S. and Braunschweig, C. (2017), Guidelines for the Provision and Assessment of Nutrition Support Therapy in the Pediatric Critically Ill Patient: Society of Critical Care Medicine and American Society for Parenteral and Enteral Nutrition. Journal of Parenteral and Enteral Nutrition, 41: 706-742.
15. Taylor, B. E., et al. (2016). "Guidelines for the Provision and Assessment of Nutrition Support Therapy in the Adult Critically Ill Patient: Society of Critical Care Medicine (SCCM) and American Society for Parenteral and Enteral Nutrition (A.S.P.E.N.)." Crit Care Med 44(2): 390-438

In this final installment on the series focusing on nutritional support and the role of indirect calorimetry, our focus will be on the assessment of steady state and the pitfalls that may impact reliable energy expenditure measurements utilizing indirect calorimetry.

As we have covered in earlier podcasts from this series, indirect calorimetry can be an accurate tool to measure energy expenditure but it is important to stress that it simply takes a snapshot view of the patient in order to understand the energy consumption needs throughout a day.

That being said, obtaining an indirect calorimetry measurement while a patient is being perturbed may not represent the actual or true energy requirements of the patient.

Examples of these perturbations include the following.

1. No recent ventilator setting changes and especially not during a weaning trial.
2. No nursing treatments that may agitate the patient, for instance endotracheal tube suctioning or wound dressing changes that may be painful.
3. Being hemodynamically stable, for instance no recent fluid challenges or use of hemodialysis. More importantly, in the first 1-2 days of sepsis, the hemodynamic demands are high.
4. No recent administration of therapeutics that can alter the patient's acid-base balance; and lastly,
5. Stable temperature, ideally when the patient is afebrile.

Before pressing onto the topic of steady state in more detail, let us first remind ourselves of the several limitations inherent to indirect calorimetry and should serve as a checklist prior to obtaining a measurement:¹

1. Make sure there are no leaks within the circuit.
2. Avoid or view the outputs with great caution when the fraction inspired oxygen concentration is greater than 60%.
3. Avoid use during hemodialysis or peritoneal dialysis.
4. Monitor and minimize humidity in the breathing circuit.

The foundation of steady state measurements were laid by McClave in a 2003 study wherein a five-minute interval was codified to establish the minimum steady state observation window to inform 24-hour energy expenditure.

In addition, the coefficient of variability of VCO2 and VO2 were equally established with a threshold of acceptance being less than or equal to 10%.²

Interestingly, this criteria is not readily attained in an ICU setting.

In fact, the steady state criteria of five minutes and CV less than or equal to 10% was not achieved in 25 patients studied by McClave.

Similarly, Reeves conducted a study evaluating shorter observation windows but first recognized that steady state criteria defined by McClave was only fulfilled in 52% of their patients evaluated.

The authors found that shortening the time period has drawbacks and increases variability to informing caloric requirements.³

Historically, 30 minutes was determined to be the optimal steady state window.

In addition, the greatest error in energy expenditure measurement occurred between 3 and 11 p.m. Therefore, has been recommended to measure indirect calorimetry when steady state conditions are met between the hours of 11 p.m. and 3 p.m.⁴

To summarize, steady state should be considered when no active interventions are ongoing or have recently concluded  and a sampling window of 5 to 10 minutes should be observed where the coefficient of variation is less than or equal to 10% for both VCO2 and VO2.

By following these principles highlighted during this podcast, you can pursue improved accuracy in determining energy expenditure and in turn optimizing nutritional support for your patients.

This concludes the final podcast on the series focusing on nutritional support and use of indirect calorimetry.

Thank you for your interest in listening to this podcast series.

Reference:

1. Oshima, T., et al. (2017). "Indirect calorimetry in nutritional therapy. A position paper by the ICALIC study group." Clin Nutr 36(3): 651-662.
2. McClave, S.A., Spain, D.A., Skolnick, J.L., Lowen, C.C., Kleber, M.J., Wickerham, P.S., Vogt, J.R. and Looney, S.W., 2003. Achievement of steady state optimizes results when performing indirect calorimetry. Journal of Parenteral and Enteral Nutrition, 27(1), pp.16-20.
3. Reeves, M.M., Davies, P.S., Bauer, J. and Battistutta, D., 2004. Reducing the time period of steady state does not affect the accuracy of energy expenditure measurements by indirect calorimetry. Journal of applied physiology, 97(1), pp.130-134.
4. Smyrnios, N.A., Curley, F.J. and Shaker, K.G. (1997), Accuracy of 30-Minute Indirect Calorimetry Studies in Predicting 24-Hour Energy Expenditure in Mechanically Ventilated, Critically Ill Patients. Journal of Parenteral and Enteral Nutrition, 21: 168-174.