Mechanically Ventilated, Cardiothoracic Surgical Patients Have Significantly Different Energy Requirements Comparing Indirect Calorimetry and the Penn State Equations

The accuracy of estimating equations for total resting energy expenditure in hospitalized patients

BACKGROUND & AIMS

Methods for estimation of nutritional expenditures for hospitalized patients may not be sufficiently specific. This study aimed to investigate the accuracy of predictive equations compared to indirect calorimetry (IC) and the effect of certain patient characteristics which might correlate with total daily energy expenditure on a heterogeneous population of hospitalized medical patients.

METHODS

A cross sectional study including demographic information, measures of bioelectric impedance analysis (BIA) including height and bodyweight (BW), IC, heart rate and from patient records, information was collected regarding nutritional risk by Nutrition Risk Screening 2002, biomarkers of C-reactive protein (CRP), albumin and leukocytes. The Harris-Benedict (HB), Mifflin St. Jeor (MSJ), and Schofield equations were calculated. Data were analyzed using T-test, linear and logistic regression analysis.

RESULTS

Overall, 197 patients, mean age 63.6 ± 16.0 years were measured with IC and had equations performed. BIA was performed in 187 and 46 withdrew, as they were too ill to measure, has oxygen or forgot fasting. All estimation methods underestimate energy expenditures for patients at nutritional risk (p < 0.001), and HB and MSJ underestimate for those with body mass index (BMI) < 18.5 (p = 0.029 and p < 0.001), while for BMI≥30 all overestimate but only HB significantly (p = 0.025). Elevated CRP and leukocytes, lower heart rate, lower and higher BMI, older patients and patients at nutritional risk can affect estimated total daily energy expenditure by equations compared measured by IC (p < 0.05).

CONCLUSION

HB, MSJ, and Schofield equations all underestimate energy expenditures with higher variations in patients at nutritional risk. In patients with BMI≥30, energy expenditures are overestimated. Considerations are to measure energy expenditures for patients at nutritional risk with continued weight loss and need for artificial nutrition, and for those with BMI≥30.

Measured versus estimated energy requirement in hospitalized patients

BACKGROUND & AIM

Failure to identify a patient's energy requirement has a variety of consequences both physiological and economical. Previous studies have shown that predictive formulas, including the Harris Benedict equation (HB), both over- and underestimates energy requirement in severely ill patients and healthy younger adults, compared to the golden standard, indirect calorimetry (IC). The comparison between measured and estimated energy requirements in hospitalized patients in regular wards is underreported. The aim of this study was to assess the agreement between measured energy requirements and requirements estimated by HB in the individual hospitalized patients, and to investigate whether those findings were associated with other specific patient characteristics.

METHODS

IC (n = 86) was used to measure resting energy expenditure (REE) and bioimpedance analysis (BIA) (n = 67) was used for body composition in patients admitted to Aalborg University Hospital. Furthermore, height, weight, body mass index, calf circumference, while information regarding hospital ward, vital values, dieticians estimated energy requirements and blood samples were collected in the patients' electronic medical records. Bland-Altman plots, multiple linear regression analysis, and Chi2 tests were performed.

RESULTS

On average a difference between IC compared with the HB (6.2%), dietitians' estimation (7.8%) and BIA (4.50%) was observed (p < 0.05). Association between REE and skeletal muscle mass (SMM) (R2 = 0.58, β = 149.0 kJ), body fat mass (BFM) (R2 = 0.51, β = 59.1 kJ), and weight (R2 = 0.62, β = 45.6 kJ) were found (p < 0.05). A positive association between measured REE and HB were found in the following variables (p < 0.05): CRP, age, surgical patients, and respiratory rate.

CONCLUSION

This study found a general underestimation of estimated energy expenditure compared to measured REE. A positive correlation between measured REE and SMM, BRM and weight was found. Lastly, the study found a greater association between CRP, age, surgical patients, and respiratory rate and a general greater than ±10% difference between measured and estimation of energy requirements.

Comparison of predictive equations and indirect calorimetry in critical care: Does the accuracy differ by body mass index classification?

BACKGROUND

Nutrition support professionals are tasked with estimating energy requirements for critically ill patients. Estimating energy leads to suboptimal feeding practices and adverse outcomes. Indirect calorimetry (IC) is the gold standard for determining energy expenditure. However, access is limited, so clinicians must rely on predictive equations.

METHODS

A retrospective chart review of critically ill patients who underwent IC in 2019 was conducted. The Mifflin-St Jeor equation (MSJ), Penn State University equation (PSU), and weight-based nomograms were calculated using admission weights. Demographic, anthropometric, and IC data were extracted from the medical record. Data were stratified by body mass index (BMI) classifications, and relationships between estimated energy requirements and IC were compared.

RESULTS

Participants (N = 326) were included. Median age was 59.2 years, and BMI was 30.1. The MSJ and PSU were positively correlated with IC in all BMI classes (all P < 0.001). Median measured energy expenditure was 2004 kcal/day, which was 1.1-fold greater than PSU, 1.2-fold greater than MSJ, and 1.3-fold greater than weight-based nomograms (all P < 0.001).

CONCLUSION

Despite the significant relationships between measured and estimated energy requirements, the significant fold-differences suggest that using predictive equations leads to significant underfeeding, which may result in poor clinical outcomes. Clinicians should rely on IC when available, and increased training in the interpretation of IC is warranted. In the absence of IC, the use of admission weight in weight-based nomograms could serve as a surrogate, as these calculations provided the closest estimate to IC in participants with normal weight and overweight, but not obesity.

A Single-Center Prospective Observational Study Comparing Resting Energy Expenditure in Different Phases of Critical Illness: Indirect Calorimetry Versus Predictive Equations

OBJECTIVES

Several predictive equations have been developed for estimation of resting energy expenditure, but no study has been done to compare predictive equations against indirect calorimetry among critically ill patients at different phases of critical illness. This study aimed to determine the degree of agreement and accuracy of predictive equations among ICU patients during acute phase (≤ 5 d), late phase (6-10 d), and chronic phase (≥ 11 d).

DESIGN

This was a single-center prospective observational study that compared resting energy expenditure estimated by 15 commonly used predictive equations against resting energy expenditure measured by indirect calorimetry at different phases. Degree of agreement between resting energy expenditure calculated by predictive equations and resting energy expenditure measured by indirect calorimetry was analyzed using intraclass correlation coefficient and Bland-Altman analyses. Resting energy expenditure values calculated from predictive equations differing by ± 10% from resting energy expenditure measured by indirect calorimetry was used to assess accuracy. A score ranking method was developed to determine the best predictive equations.

SETTING

General Intensive Care Unit, University of Malaya Medical Centre.

PATIENTS

Mechanically ventilated critically ill patients.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Indirect calorimetry was measured thrice during acute, late, and chronic phases among 305, 180, and 91 ICU patients, respectively. There were significant differences (F= 3.447; p = 0.034) in mean resting energy expenditure measured by indirect calorimetry among the three phases. Pairwise comparison showed mean resting energy expenditure measured by indirect calorimetry in late phase (1,878 ± 517 kcal) was significantly higher than during acute phase (1,765 ± 456 kcal) (p = 0.037). The predictive equations with the best agreement and accuracy for acute phase was Swinamer (1990), for late phase was Brandi (1999) and Swinamer (1990), and for chronic phase was Swinamer (1990). None of the resting energy expenditure calculated from predictive equations showed very good agreement or accuracy.

CONCLUSIONS

Predictive equations tend to either over- or underestimate resting energy expenditure at different phases. Predictive equations with "dynamic" variables and respiratory data had better agreement with resting energy expenditure measured by indirect calorimetry compared with predictive equations developed for healthy adults or predictive equations based on "static" variables. Although none of the resting energy expenditure calculated from predictive equations had very good agreement, Swinamer (1990) appears to provide relatively good agreement across three phases and could be used to predict resting energy expenditure when indirect calorimetry is not available.

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.

Enteral Nutrition in the Mechanically Ventilated Patient

Mechanically ventilated patients are unable to take food orally and therefore are dependent on enteral nutrition for provision of both energy and protein requirements. Enteral nutrition is supportive therapy and may impact patient outcomes in the intensive care unit. Early enteral nutrition has been shown to decrease complications and hospital length of stay and improve the prognosis at discharge. Nutrition support is unique for patients on mechanical ventilation and, as recently published literature shows, should be tailored to the individuals' underlying pathology. This review will discuss the most current literature and recommendations for enteral nutrition in patients receiving mechanical ventilation.

Optimization of Nutrition Therapy with the Use of Calorimetry to Determine and Control Energy Needs in Mechanically Ventilated Critically Ill Patients: The ONCA Study, a Randomized, Prospective Pilot Study

BACKGROUND

Adequate nutrition therapy in critically ill patients poses a challenge because of the variable energy and substrate needs. The objective was to investigate whether nutrition therapy involving indirect calorimetry (IC), instead of equations for assessment of energy needs, could improve the nutrition status of critically ill patients.

METHODS

Forty mechanically ventilated patients were randomized into a group in which energy needs were controlled by calorimetry (IC group) and a group treated with a formula-based approach reflecting standard care (SC group). The primary outcome was change in the phase angle (PhA), a bioelectrical impedance parameter related to nutrition status and prognosis.

RESULTS

The mean IC-based energy requirement was lower than the formula-based estimate (21.1 ± 6.4 versus [vs] 25 kcal/kg/d, P < .01). The IC group reached 98% ± 8% of the energy goal, whereas the SC group reached only 79% ± 29% (P < 0.05), although mean intake was similar in both groups. The protein intake goal was better met in the IC group (91% ± 24%) than the SC group (73% ± 33%). The PhA of the IC group did not change during treatment, whereas that of the SC group tended to decrease by 0.36° ± 0.86° (P = .077). A shorter length of stay in intensive care was observed in the IC than in the SC group (13 ± 8 vs 24 ± 20 days, P < .05).

CONCLUSION

Intensified individual nutrition therapy involving IC appears to be useful for improving nutrition status in critically ill patients.

Predictive equations versus measured energy expenditure by indirect calorimetry: A retrospective validation

BACKGROUND & AIMS

Measuring resting energy expenditure (REE) via indirect calorimetry (IC) in intensive care unit (ICU) patient is the gold standard recommended by guidelines. However technical difficulties hinder its use and predictive equations are largely used instead. We sought to validate commonly used equations using a large cohort of patients.

METHODS

Patients hospitalized from 2003 to 2015 in a 16-bed ICU at a university-affiliated, tertiary care hospital who had IC measurement to assess caloric targets were included. Data was drawn from a computerized system and included REE and other variables required by equations. Measurements were restricted to 5 REE per patient to avoid bias. Equation performance was assessed by comparing means, standard deviations, correlation, concordance and agreement, which was defined as a measurement within 85-115% of measured REE. A total of 8 equations were examined.

RESULTS

A total of 3573 REE measurements in 1440 patients were included. Mean patient age was 58 years and 65% were male. A total of 562 (39%) patients had >2 REE measurements. Standard deviation of REE ranged from 430 to 570 kcal. The Faisy equation had the least mean difference (90 Kcal); Harris-Benedict had the highest correlation (52%) and agreement (50%) and Jolliet the highest concordance (62%). Agreement within 10% of caloric needs was met only in a third of patients.

CONCLUSIONS

Predictive equations have low performance when compared to REE in ICU patients. We therefore suggest that predictive equations cannot wholly replace indirect calorimetry for the accurate estimation of REE in this population.

Development and Validation of a New Cardio-Specific Resting Energy Expenditure Equation for Adults

BACKGROUND

This study tested the accuracy of resting energy expenditure (REE) equations among patients who underwent cardiopulmonary bypass and developed/validated a more accurate cardio-specific equation (CSE).

MATERIALS AND METHODS

Prospective observational cohort of 240 adults (derivation data set, 170 patients; validation data set, 70 patients). REEs were calculated with 6 equations-Penn State 2003a, Penn State 2003b, Ireton-Jones, Swinamer, Faisy, and American College of Chest Physicians-and results were compared with indirect calorimetry (IC). Multivariable linear regression analysis was used to develop the CSE. Agreement between measured and calculated REEs was assessed with Lin's concordance correlation coefficient (LCCC), Bland-Altman plot, and regression analysis.

RESULTS

LCCCs present poor agreement between measured and calculated REEs: 0.24 (95% CI, 0.19-0.29), for the Faisy equation; 0.15 (95% CI, 0.1-0.19), Ireton-Jones; 0.31 (95% CI, 0.25-0.37), Swinamer; 0.17 (95% CI, 0.13-0.21), Penn State 2003a; 0.19 (95% CI, 0.14-0.23), Penn State 2003b; and 0.11 (95% CI, 0.07-0.15), American College of Chest Physicians. Based on the derivation data set, REEs are explained by the following equation: CSE = 616 - 8 × age in years + 13 × weight in kilograms + 450 if on ventilator + 159 × MV in liters + 145 if on inotropes. Based on the validation study results, the LCCC between IC and the CSE was 0.82 (95% CI, 0.73-0.88).

CONCLUSION

The CSE has adequate precision and could be used for REE estimation for patients undergoing cardiopulmonary bypass if IC is unavailable.