Validation Study of Energy Requirements in Critically Ill, Obese Cancer Patients

How do guideline recommended energy targets compare with measured energy expenditure in critically ill adults with obesity: A systematic literature review

BACKGROUND

Critically ill patients with obesity have unique and complex nutritional needs, with clinical practice guidelines conflicting regarding recommended energy targets. The aim of this systematic review was to 1) describe measured resting energy expenditure (mREE) reported in the literature and; 2) compare mREE to predicted energy targets using the European (ESPEN) and American (ASPEN) guideline recommendations when indirect calorimetry is not available in critically ill patients with obesity.

METHODS

The protocol was registered apriori and literature was searched until 17th March, 2022. Original studies were included if they reported mREE using indirect calorimetry in critically ill patients with obesity (BMI≥30 kg/m²). Group-level mREE data was reported as per the primary publication using mean ± standard deviation or median [interquartile range]. Where individual patient data was available, Bland-Altman analysis was used to assess mean bias (95% limits of agreement) between guideline recommendations and mREE targets (i.e. ASPEN for BMI 30-50, 11-14 kcal/kg actual weight compared to 70% mREE and ESPEN 20-25 kcal/kg adjusted weight compared to 100% mREE). Accuracy was assessed by the percentage (%) of estimates within ±10% of mREE targets.

RESULTS

After searching 8019 articles, 24 studies were included. mREE ranged from 1607 ± 385 to 2919 [2318-3362]kcal and 12-32kcal/actual body weight. For the ASPEN recommendations of 11-14 kcal/kg, a mean bias of -18% (-50% to +13%) and 4% (-36% to +44%) was observed, respectively (n = 104). For the ESPEN recommendations 20-25 kcal/kg, a bias of -22% (-51% to +7%) and -4% (-43% to +34%), was observed, respectively (n = 114). The guideline recommendations were able to accurately predict mREE targets on 30%-39% occasions (11-14 kcal/kg actual) and 15%-45% occasions (20-25 kcal/kg adjusted), for ASPEN and ESPEN recommendations, respectively.

CONCLUSIONS

Measured energy expenditure in critically ill patients with obesity is variable. Energy targets generated using predictive equations recommended in both the ASPEN and ESPEN clinical guidelines have poor agreement with mREE and are frequently not able to accurately predict within ±10% of mREE, most commonly underestimating energy needs.

Metabolic monitoring via on-line analysis of 13C-enriched carbon dioxide in exhaled mouse breath using substrate-integrated hollow waveguide infrared spectroscopy and luminescence sensing combined with Bayesian sampling

In studies that target specific functions or organs, the response is often overlaid by indirect effects of the intervention on global metabolism. The metabolic side of these interactions can be assessed based on total energy expenditure (TEE) and the contributions of the principal energy sources, carbohydrates, proteins and fat to whole body CO₂ production. These parameters can be identified from indirect calorimetry using respiratory oxygen intake and CO₂ dioxide production data that are combined with the response of the ¹³CO₂ release in the expired air and the glucose tracer enrichment in plasma following a ¹³C glucose stable isotope infusion. This concept is applied to a mouse protocol involving anesthesia, mechanical respiration, a disease model, like hemorrhage and therapeutic intervention. It faces challenges caused by a small sample size for both breath and plasma as well as changes in metabolic parameters caused by disease and intervention. Key parameters are derived from multiple measurements, all afflicted with errors that may accumulate leading to unrealistic values. To cope with these challenges, a sensitive on-line breath analysis system based on substrate-integrated hollow waveguide infrared spectroscopy and luminescence (iHWG-IR-LS) was used to monitor gas exchange values. A Bayesian statistical model is developed that uses established equations for indirect calorimetry to predict values for respiratory gas exchange and tracer data that are consistent with the corresponding measurements and also provides statistical error bands for these parameters. With this new methodology, it was possible to estimate important metabolic parameters (respiratory quotient (RQ), relative contribution of carbohydrate, protein and fat oxidation f_carb, f_fat and f_prot , total energy expenditure TEE) in a resolution never available before for a minimal invasive protocol of mice under anesthesia.

Systematic review of factors associated with energy expenditure in the critically ill

BACKGROUND AND AIMS

Indirect calorimetry is the reference standard for energy expenditure measurement. Predictive formulae that replace it are inaccurate. Our aim was to review the patient and clinical factors associated with energy expenditure in critically ill patients.

METHODS

We conducted a systematic review of the literature. Eligible studies were those reporting an evaluation of factors and energy expenditure. Energy expenditure and factor associations with p-values were extracted from each study, and each factor was classified as either significantly, indeterminantly, or not associated with energy expenditure. Regression coefficients were summarized as measures of central tendency and spread. Metanalysis was performed on correlations.

RESULTS

The search strategy yielded 8521 unique articles, 307 underwent full text review, and 103 articles were included. Most studies were in adults. There were 95 factors with 352 evaluations. Minute volume, weight, age, % body surface area burn, sedation, post burn day, and caloric intake were significantly associated with energy expenditure. Heart rate, fraction of inspired oxygen, respiratory rate, respiratory disease diagnosis, positive end expiratory pressure, intensive care unit days, C- reactive protein, and size were not associated with energy expenditure. Multiple factors (n = 37) were identified with an unclear relationship with energy expenditure and require further evaluation.

CONCLUSIONS

An important interval step in the development of accurate formulae for energy expenditure estimation is a better understanding of relationships between patient and clinical factors and energy expenditure. The review highlights the limitations of currently available data, and identifies important factors that are not included in current prediction formulae of the critically ill.

Comparison of Resting Energy Expenditure Assessment in Pediatric Oncology Patients

BACKGROUND

Evaluation of energy requirements is an important part of the nutrition assessment of pediatric oncology patients. Adequate provision of energy in this population is of extreme importance because of the prevalence of malnutrition and its effect on growth, development, quality of life, morbidity, and mortality. Numerous methods are used in clinical practice for estimating the resting energy expenditures (REE), specifically indirect calorimetry and predictive equations. A relatively new instrument used to assess REE is the hand-held indirect calorimeter. The purpose of this quality improvement project was to compare the accuracy of REE measurements taken by a hand-held indirect calorimeter and predictive equations to that of a standard indirect calorimeter metabolic cart.

METHODS

Patients receiving therapy for pediatric cancer, aged 7-18 years, and having a weight ≥15 kg and scheduled for a REE nutrition assessment were eligible. Sequentially, the patient's REE was assessed with the cart and the hand-held indirect calorimeter along with the predictive equation calculation.

RESULTS

Post hoc pairwise comparisons revealed that all 3 methods were significantly different from one another (P < .0001). When compared with the cart, the portable hand-held calorimeter was found to underestimate REE by 11.9%, whereas predictive equations overestimated REE by 12.4%.

CONCLUSION

Our quality improvement project suggests that the hand-held indirect calorimeter underestimated REE, and predictive equations overestimated REE in pediatric oncology nutrition assessment. Therefore, we recommend that these limitations in assessment be considered when assessing REE using a hand-held indirect calorimeter or predictive equations.