To provide optimal nutritional support, an accurate determination of patients’ energy needs is vital. However, these caloric needs can be challenging to predict in patients suffering from both acute and chronic conditions, due to the effect these issues have on their resting energy expenditure (REE)1.
If REE is overestimated, overfeeding can result, leading to azotemia, hypertonic dehydration, metabolic acidosis, hyperglycemia, hypertriglyceridemia, and hepatic steatosis and more2. On the other hand, an underestimation of energy needs and a resultant underfeeding may make malnutrition inevitable, increasing morbidity and mortality risk, as well as hospital-related cost3. These dangers may be especially prevalent in obese patients due to the effect their condition can have on their REE1.
While predictive equations, such as Milner, Zawacki and Harris-Benedict have seen traditional use in the critical care setting for the estimation of REE, use of these methods fails to adequately account for this effect, leaving patients at risk2.
In this article, we’ll discuss the particular challenges obesity presents in determining REE. We’ll also take a look at why predictive equations are unreliable in this patient population and how the use of indirect calorimetry (IC) can ensure precision nutrition and improve outcomes, particularly when continuous monitoring is utilized.
The Challenges of Obesity in Energy Estimation
REE is the amount of energy the body expends during a 24-hour inactive period, energy that’s used to maintain involuntary functions such as body temperature regulation, respiration and cardiac output2. REE makes up approximately two-thirds of the total energy expenditure (TEE) in healthy, sedentary adults2.
However, assuming that this level remains the same in sick patients, under diseased-related stress, leads to inaccuracies. This includes patients suffering from chronic conditions, which make energy requirements difficult to predict. In fact, chronic pathologies, such as obesity, have been shown to exhibit both hyper- and hypo-metabolism due to the presence of inflammation and alterations in organ function, lean body mass and metabolism2.
Research by Horie et al found that obese women had higher total energy expenditure (TEE), compared with normal weight4. However, in “Energy Expenditure Measured by Indirect Calorimetry in Obesity,” Rosado et al point out that this increase is likely due in large part to increased basal metabolic rate (BMR) due to higher fat-free mass (FFM) and energy demand during physical activity4. This was confirmed in a study by Melo et al, who found that the difference disappeared after adjusting for fat-free mass and therefore, the EE per kilogram of body weight is lower in obese individuals2,4.
The Failure of Predictive Equations
While research by Shetty proved a reasonable level of precision in estimation of energy expenditure in normal weight adults when utilizing predictive equations, that same precision is not seen when these equations are utilized in obese individuals4.
The inaccuracies these equations generate are due to the difficulty encountered in choosing the weight to be applied to the equation. Application of the patient’s current weight may lead to an overestimation of REE, while application of ideal or adjusted body weight can lead to an underestimation of energy requirements4.
Kross et al set out to evaluate the accuracy of predictive equations in 927 critically ill patients, 401 of whom were classified as obese, finding poor agreement between the REE predicted by Harris-Benedict, Owen, Mifflin, Ireton-Jones and American College of Chest Physicians equations and actual measured REE4. All equations, except Ireton-Jones, underestimated REE.
Another study by Ullah et al compared REE measured through indirect calorimetry to that predicted by the Harris-Benedict and Schofield equations in a 31 morbidly obese patients. Their results showed that the equations overestimated REE by 10 and 7 percent respectively4.
Improving Accuracy of Measurement to Improve Nutrition
Because of the failure of these predictive equations to accurately estimate the energy needs of obese patients, IC is becoming the preferred method to ensure proper nutrition, while reducing the risk of both over- and under-feeding.
Unlike predictive equations, IC provides an objective, accurate measurement of energy metabolism through the monitoring of the respiratory exchange ratio (RER) at the mouth5. This is the ratio of carbon dioxide produced to oxygen consumed and represents fuel oxidation by IC. Measurements are also made at the cellular level to determine the amount of carbon dioxide produced compared to oxygen consumed, which is known as the respiratory quotient (RQ), which serves to validate the IC determination and is integral in precision measurement of energy expenditure.
The RER can also serve as an indicator of which type of fuel is being metabolized by the patient’s body5. For example, if less carbon dioxide is produced for the amount of oxygen consumed, it can be assumed that fat metabolism is occurring. On the other hand, during metabolism of carbohydrates, an equal amount of carbon dioxide is produced for the oxygen consumed.
Additionally, it’s important to note that numerous studies, including the TICACOS study, have reported a significant day-to-day variation in REE measured by IC6. This is unsurprising since patient caloric needs may vary significantly across their care journey due to6:
- The phase of their illness
- Neuromuscular blockade
- Early rehabilitation
- Other unknown factors
IC-guided nutrition therapy with continuous monitoring allows for the capture of these daily variations. This helps ensure that as a patient’s needs change, their nutrition can remain precisely balanced for optimal recovery from illness and chronic health management, thus avoiding the known adverse effects of over- and underfeeding due to a low precision equation-based REE strategy.
The Value of IC for Critically Ill Obese Patients
The value of utilizing IC for obese patients in the ICU and beyond is clear.
In “Energy delivery guided by indirect calorimetry in critically ill patients: a systematic review and meta-analysis,” Duan et al found that6:
- Guiding energy delivery through the use of IC significantly reduced short-term mortality in critically ill adults compared with predictive equations.
- IC-guided nutrition therapy led to higher mean energy and protein intake per day and percent delivered energy over measured REE.
- Using IC led to a better patient prognosis.
Overall, the authors concluded that their findings provide support for the use of IC as the gold standard in the determination of REE in the ICU, rather than predictive equations.
GE Healthcare: Leading the Way in Precision Nutrition
Part of the GE Healthcare Monitoring Solutions and Respiratory portfolio, IC combines respiratory gas exchange measurements with quantification of cellular metabolism to support clinicians in defining the nutritional requirements of critically ill, obese patients.
The solution consists of three components:
- CARESCAPE™ Bx50 Monitor or the CARESCAPE™ R860 Ventilator
- E-sCOVX Metabolics and Gas Exchange Module
- The adult D-lite++ or Pedi-lite+ Spirometry Set
Sensors are placed, allowing for simultaneous measurement of airway gases, lung mechanics and metabolism. All parameters are measured through a single, lightweight flow sensor and EE and RQ parameters are displayed on the CARESCAPE monitor or ventilator.
This enables clinicians to act when patients experience significant changes in nutritional requirements over the course of their hospitalization in response to medical and nursing care or complications.
- Providing precisely balanced nutrition is vital to improving patient safety, recovery and outcomes.
- Due to a lower REE in obese patients, there is a major risk of over- or undershooting their caloric needs when predictive equations are utilized.
- The use of IC has been shown to reduce mortality in critically ill patients and should be considered the gold standard for care.
- Continuous monitoring of REE through IC allows daily variation in energy requirements to be met through patient-specific nutrition.
1: Delsoglio, Marta et al. “Indirect Calorimetry in Clinical Practice.” Journal of clinical medicine vol. 8, 9 1387. 5 Sep. 2019, doi:10.3390/jcm8091387.
2: Klein, Catherine et al. “Overfeeding Macronutrients to Critically Ill Adults: Metabolic Complications.” Journal of the American Dietetic Association, Volume 98, Issue 7, 1998, Pages 795-806, ISSN 0002-8223, https://doi.org/10.1016/S0002-8223(98)00179-5.
3: Osooli, Fatemeh et al. “Identifying Critically Ill Patients at Risk of Malnutrition and Underfeeding: A Prospective Study at an Academic Hospital.” Advanced pharmaceutical bulletin vol. 9,2 (2019): 314-320. doi:10.15171/apb.2019.037.
4: Rosado, Eliane, Kaippert, Vanessa, de Brito, Roberta. "Energy Expenditure Measured by Indirect Calorimetry in Obesity". Applications of Calorimetry in a Wide Context - Differential Scanning Calorimetry, Isothermal Titration Calorimetry and Microcalorimetry, edited by Amal Elkordy, IntechOpen, 2013. 10.5772/55605.
5: Gupta, Riddhi Das et al. “Indirect Calorimetry: From Bench to Bedside.” Indian journal of endocrinology and metabolism vol. 21,4 (2017): 594-599. doi:10.4103/ijem.IJEM_484_16.
6: Duan, JY., Zheng, WH., Zhou, H. et al. Energy delivery guided by indirect calorimetry in critically ill patients: a systematic review and meta-analysis. Crit Care 25, 88 (2021). https://doi.org/10.1186/s13054-021-03508-6.