Prediction of resting metabolic rate in critically ill adult patients: results of a systematic review of the evidence

Methods for estimating resting energy expenditure in intensive care patients: A comparative study of predictive equations with machine learning and deep learning approaches

BACKGROUND

Accurate estimation of resting energy expenditure (REE) is critical for guiding nutritional therapy in critically ill patients. While indirect calorimetry (IC) is the gold standard for REE measurement, it is not routinely feasible in clinical settings due to its complexity and cost. Predictive equations (PEs) offer a simpler alternative but are often inaccurate in critically ill populations. While recent advancements in machine learning (ML) and deep learning (DL) offer potential for improving REE estimation by capturing complex relationships between physiological variables, these approaches have not yet been widely applied or validated in critically ill populations.

METHODOLOGY

This prospective study compared the performance of nine commonly used PEs, including the Harris-Benedict (H-B1919), Penn State, and TAH equations, with ML models (XGBoost, Random Forest Regressor [RFR], Support Vector Regression), and DL models (Convolutional Neural Networks [CNN]) in estimating REE in critically ill patients. A dataset of 300 IC measurements from an intensive care unit (ICU) was used, with REE measured by both IC and PEs. The ML/DL models were trained using a combination of static (i.e., age, height, body weight) and dynamic (i.e., minute ventilation, body temperature) variables. A five-fold cross validation was performed to assess the model prediction performance using the root mean square error (RMSE) metric.

RESULTS

Of the PEs analysed, H-B1919 yielded the lowest RMSE at 362 calories. However, the XGBoost and RFR models significantly outperformed all PEs, achieving RMSE values of 199 and 200 calories, respectively. The CNN model demonstrated the poorest performance among ML models, with an RMSE of 250 calories. The inclusion of additional categorical variables such as body mass index (BMI) and body temperature classes slightly reduced RMSE across ML and DL models. Despite data augmentation and imputation techniques, no significant improvements in model performance were observed.

CONCLUSION

ML models, particularly XGBoost and RFR, provide more accurate REE estimations than traditional PEs, highlighting their potential to better capture the complex, non-linear relationships between physiological variables and REE. These models offer a promising alternative for guiding nutritional therapy in clinical settings, though further validation on independent datasets and across diverse patient populations is warranted.

Nutrition management of a patient following emergent pneumonectomy due to chest wall trauma

Emergent total pneumonectomy is a rare surgical intervention for patients with severe chest trauma. Patients who survive the immediate postoperative period experience prolonged, complex hospitalizations. The purpose of this case study is to review the nutrition care provided to a patient who survived total pneumonectomy and the supporting evidence. John Doe (JD) is a man aged 28 years who presented to a level I trauma center with penetrating chest trauma. He required multiple operative interventions, resulting in a partial right and total left pneumonectomy. JD's hospitalization was complicated by prolonged use of extracorporeal membrane oxygenation (ECMO) and continuous renal replacement therapy (CRRT). His surgical course and gastric feeding intolerance hampered enteral nutrition adequacy, and parenteral nutrition support was initiated on hospital day 17. Tolerance to enteral nutrition improved after jejunal access was obtained, and the patient transitioned to total enteral nutrition support. As a result of inflammatory metabolic changes and nutrition delivery challenges for the first 2 weeks of hospitalization, JD developed malnutrition. His nutrition care was further complicated by copper and carnitine deficiencies, which have been described in patients requiring ECMO and CRRT. Patients who require emergent total pneumonectomy following traumatic chest injuries will likely require complex hospital care, including extracorporeal organ support. These patients present unique nutrition challenges; however, given the relative infrequency of the intervention, there is limited research to guide clinical practice. Additional research on nutrition interventions in this population is warranted.

Nutrition and outcomes in venovenous extracorporeal membrane oxygenation: An observational cohort study

BACKGROUND

Overfeeding and underfeeding are associated with negative outcomes during critical illness. The purpose of this retrospective study was to assess the association between nutrition intake and outcomes for patients receiving venovenous (VV) extracorporeal membrane oxygenation (ECMO).

METHODS

Adults who received VV ECMO August 2017 to June 2020 were screened. Patients with <3 ECMO nutrition support days were excluded. Age, sex, height, weight, ideal body weight (IBW), body mass index, sequential organ failure assessment score, respiratory ECMO survival prediction score, energy, and protein goals were collected. All nutrition intake was collected for the first 14 days of ECMO or until death, decannulation, or oral diet initiation. Outcomes analyzed included mortality and VV ECMO duration. The relationship between nutrition delivery and outcomes was tested with multivariate analysis. Univariate analyses were conducted on obese and nonobese subgroups.

RESULTS

A total of 2044 nutrition days in 178 patients were analyzed. The median estimated needs were 24 (interquartile range: 22.3-28.3) kcal/kg/day and 2.25 (interquartile range: 2.25-2.77) g/kg/day of protein using IBW in patients with obesity and actual weight in patients without obesity. Patients received 83% of energy and 63.3% of protein targets. Patients with obesity who received ≥2 g/kg IBW of protein had a significantly shorter ECMO duration (P = 0.037). Increased protein intake was independently associated with a reduced risk of death (odds ratio: 0.06; 95% confidence interval: 0.01-0.43).

CONCLUSION

Higher protein intake was associated with reduced mortality. Optimal energy targets for patients receiving ECMO are currently unknown and warrant further study.

Malnutrition in adult patients treated with venoarterial extracorporeal membrane oxygenation: A descriptive cohort study

Objective

To evaluate malnutrition and its association with outcomes in adult patients requiring venoarterial (VA) extracorporeal membrane oxygenation (ECMO).

Methods

Patients cannulated for VA ECMO between January 1, 2020, and January 1, 2023, were screened. Patients on ECMO for <48 hours or without a nutritional evaluation were excluded. Demographic and anthropometric data were collected retrospectively. Malnutrition assessments were conducted using the Global Leadership Initiative on Malnutrition framework. Outcomes analyzed were duration of ECMO and in-hospital mortality. Patients were stratified by admission and discharge nutritional status for analysis. Baseline characteristics were controlled for with propensity score matching.

Results

Data from 197 patients was analyzed. The cohort was 68% male. The median duration of ECMO was 139.5 hours (interquartile range [IQR], 94.8-257 hours), and mortality was 35%. Thirty-three patients presented with malnutrition, and 61 developed hospital-acquired malnutrition, for an incidence of 47.7%. Malnutrition at any point was associated with longer duration of ECMO (median, 180 hours [IQR, 107.8-335.8 hours] vs 120 hours [IQR, 90-185.8 hours]; P < .001). Patients with hospital-acquired malnutrition required a 50% longer duration of ECMO (median, 182.5 hours [IQR, 101.5-367 hours] vs 123 hours [IQR, 90.8-211.5 hours]; P < .001). Preexisting malnutrition was associated with a nonsignificant increase in mortality (48.2% vs 32.9%; P = .13), which was similar after 3:1 propensity score matching (43.3% vs 35.4%; P = .44).

Conclusions

In adult patients, malnutrition appears to be associated with prolonged duration of VA ECMO. Adequately powered studies are needed to further investigate the relationship between malnutrition and mortality.

Dietary Modifications in Critically-Ill Patients: A Comparison of Persian Medicine and Conventional Medicine Perspectives

In Persian Medicine (PM) literature, a crisis is the culmination of the body's response to illness, which necessitates fundamental dietary modification to improve prognosis. In this narrative review, authentic PM textbooks as well as articles on diets for critically-ill patients (CIPs) obtained from PubMed and Google Scholar databases, were reviewed, and after gathering data, they were classified, coded, analyzed, and compared. In the acute phase, both PM and conventional medicine agree on relative food restriction, but PM lays a special focus on the use of meat in cases of weakness. There are both similarities and differences between PM and conventional medicine regarding nutritional recommendations in critical illness. For example, recommendations for food restriction and protein intake are similar in both schools, but recommendations for carbohydrate intake are different. The variables addressed and emphasized in PM require further evaluation in clinical trials.

Resting Energy Expenditure Early after Cardiac Surgery and Validity of Predictive Equations: A Prospective Observational Study

OBJECTIVE

Several predictive equations have been used to estimate patients' energy expenditure. The study aimed to describe the characteristics of resting energy expenditure (REE) in patients undergoing mechanical ventilation during early postoperative stage after cardiac surgery and evaluate the validity of 9 REE predictive equations.

METHODS

This was a prospective observational study. Patients aged 18-80 years old, undergone open-heart surgery, were enrolled between January 2017 and 2018. The measured REE (mREE) was evaluated via indirect calorimetry (IC). The predictive resting energy expenditure (pREE) was suggested by 9 predictive equations, including Harris-Benedict (HB), HB coefficient method, Ireton-Jones, Owen, Mifflin, Liu, 25 × body weight (BW), 30 × BW, and 35 × BW. The association between mREE and pREE was assessed by Pearson's correlation, paired t test, Bland-Altman method, and the limits of agreement (LOA).

RESULTS

mREE was related to gender, BMI, age, and body temperature. mREE was significantly correlated with pREE, as calculated by 9 equations (all p < 0.05). There was no significant difference between pREE and mREE, as calculated by 30 × BW kcal/kg/day (t = 0.782, p = 0.435), while significant differences were noted between mREE and pREE calculated by other equations (all p < 0.05). Taking the 30 × BW equation as a suitable candidate, most of the data points were within LOA, and the percentage was 95.6% (129/135). Considering the rationality of clinical use, accurate predictions (%) were calculated, and only 40.74% was acceptable.

CONCLUSIONS

The 30 × BW equation is relatively acceptable for estimating REE in 9 predictive equations in the early stage after heart surgery. However, the IC method should be the first choice if it is feasible.

Methods for Estimating Energy Expenditure in Critically Ill Adults

Energy expenditure (EE) is the sum of metabolic activity within the body at a given time and comprises basal EE, diet-induced thermogenesis, and physical activity. In the intensive care unit, EE is most often assessed to determine a patient's caloric requirements. Energy expenditure also may be useful to understand disease states and the metabolic impact of interventions. Several methods for estimating EE are relevant for clinical use, including indirect calorimetry, predictive equations, exhaled carbon dioxide volume, and the Fick method. Indirect calorimetry is the preferred method for evaluating EE and is considered the gold standard for estimating EE in hospitalized patients. However, use of indirect calorimetry is not always practical or possible. Therefore, other methods of estimating EE must be considered. In this review, methods of evaluating EE in critically ill adults are examined and the benefits and limitations of each method are discussed, with practical considerations for use.

Comparison of resting metabolic rate prediction equations in college-aged adults

Prediction equations have been considered an accurate method for estimating resting metabolic rate (RMR) across multiple populations, but their accuracy for college-aged individuals not on an athletics team remains to be determined. Sixty-two college-aged (18-30 yrs) males (n = 31) and females (n = 31) had their RMR measured (RMRm), using indirect calorimetry, and body composition assessed via air-displacement plethysmography. The World Health Organization (WHO), Mifflin-St Jeor (Mifflin), Harris-Benedict (HB), Cunningham, and Nelson equations were used to estimate RMR. No difference was observed between the Cunningham and RMRm regardless of sex (p ≥ 0.05). All other prediction equations estimated a significantly lower RMR for males (p < 0.05). The Mifflin and Nelson equations predicted an RMR that was significantly lower than RMRm for females (p < 0.05). When compared with RMRm, no difference was detected for females using the WHO, HB, or Cunningham (p ≥ 0.05). Only the Nelson equation predicted an RMR that was outside of the clinically acceptable range (±10% of RMRm) regardless of sex. The Cunningham, WHO, and HB equations can accurately predict RMR for college-aged males and females. RMR prediction equations used in this study are less accurate for those with greater RMRs. Novelty: For adults 18-30 years old that are not on an athletics team, the Cunningham equation can accurately predict RMR. The Nelson equation should not be used to predict RMR for this population. There is a systematic bias for RMR prediction equations to underestimate higher measured RMR values.

Measured vs Predicted Energy Expenditure in Mechanically Ventilated Adults With Acute, Traumatic Spinal Cord Injuries

BACKGROUND

Research regarding the impact of acute spinal cord injury (aSCI) on energy expenditure is limited. Patients with aSCI are prone to complications of both over- and under-feeding, making appropriate nutrition support pivotal to patient care. The purpose of this study was to describe energy expenditure and assess the performance of predictive equations in mechanically ventilated adults with aSCI.

METHODS

Adult patients admitted to a single trauma center from March 2017 through June 2018 with aSCI and a documented indirect calorimetry (IC) within 6 weeks of injury were included for analysis. Predictive equations evaluated included Penn State 2003b (PS 2003b), the derived Weir equation, 25 kcal/kg and 30 kcal/kg. Sub-set analysis was performed for patients with and without obesity, isolated aSCI, and concomitant traumatic injuries.

RESULTS

On hundres fifteen IC studies in 51 patients were included for analysis. Median energy expenditure was 1747 kcal/day (interquartile range [IQR], 1492-2099 kcal/day), or 22.7 kcal/kg (IQR, 19.3-25.9 kcal/kg). When stratified by hospital day, energy expenditure ranged from 20 to 25 kcal/kg. PS 2003b and the derived Weir equation had similar correlation coefficients (r = 0.81 and 0.82, respectively). The 25 and 30 kcal/kg performed unacceptably (r = 0.61). PS 2003b predicted within 10% of measured energy expenditure most frequently. All equations were biased towards overfeeding, except for PS 2003b in the obese subset.

CONCLUSION

In the absence of IC, PS 2003b or the derived Weir equation may be acceptable predictive equations in this population. However, bedside clinicians should monitor carefully for signs and symptoms of overfeeding.

Proposal of a new equation for estimating resting energy expenditure of acute kidney injury patients on dialysis: a machine learning approach

Background

The objective of this study was to develop a new predictive equation of resting energy expenditure (REE) for acute kidney injury patients (AKI) on dialysis.

Materials and methods

A cross-sectional descriptive study was carried out of 114 AKI patients, consecutively selected, on dialysis and mechanical ventilation, aged between 19 and 95 years. For construction of the predictive model, 80% of cases were randomly separated to training and 20% of unused cases to validation. Several machine learning models were tested in the training data: linear regression with stepwise, rpart, support vector machine with radial kernel, generalised boosting machine and random forest. The models were selected by ten-fold cross-validation and the performances evaluated by the root mean square error.

Results

There were 364 indirect calorimetry measurements in 114 patients, mean age of 60.65 ± 16.9 years and 68.4% were males. The average REE was 2081 ± 645 kcal. REE was positively correlated with C-reactive protein, minute volume (MV), expiratory positive airway pressure, serum urea, body mass index and inversely with age. The principal variables included in the selected model were age, body mass index, use of vasopressors, expiratory positive airway pressure, MV, C-reactive protein, temperature and serum urea. The final r-value in the validation set was 0.69.

Conclusion

We propose a new predictive equation for estimating the REE of AKI patients on dialysis that uses a non-linear approach with better performance than actual models.

Throwing darts in ICU: how close are we in estimating energy requirements?

Background

Indirect calorimetry (IC) is the gold standard for determining energy requirement. Due to lack of availability in many institutions, predictive equations are used to estimate energy requirements. The purpose of this study is to determine the accuracy of predictive equations (ie, Harris-Benedict equation (HBE), Mifflin-St Jeor equation (MSJ), and Penn State University equation (PSU)) used to determine energy needs for critically ill, ventilated patients compared with measured resting energy expenditure (mREE).

Methods

The researchers examined data routinely collected as part of clinical care for patients within intensive care units (ICUs). The final sample consisted of 68 patients. All studies were recorded during a single inpatient stay within an ICU.

Results

Patients, on average, had an mREE of 33.9 kcal/kg using IC. The estimated energy requirement when using predictive equations was 24.8 kcal/kg (HBE×1.25), 24.0 kcal/kg (MSJ×1.25), and 26.8 kcal/kg (PSU).

Discussion

This study identified significant differences between mREE and commonly used predictive equations in the ICU.

Level of evidence

III.

A Comparison of the Indirect Calorimetry and Different Energy Equations for the Determination of Resting Energy Expenditure of Patients With Renal Transplantation

OBJECTIVE

We aimed to evaluate the agreement between the resting energy expenditure (REE) obtained by indirect calorimetry and eight prediction equations in adult patients with renal transplantation and a newly developed REE prediction equation for use in patients with renal transplantation in the clinic.

METHODS

A total of 51 patients (30 males and 21 females) were involved in the study. The REE was measured by indirect calorimetry and compared with the previous prediction equations. The agreement was assessed by the interclass correlation coefficient and by Bland-Altman plot analysis.

RESULTS

No significant difference was found in terms of age and body mass index between the genders. Differences between the predicted and measured REEs were maximum in the Bernstein equation (-478 kcal) and minimum in the Cunningham equation (-69 kcal). It was found that underprediction values varied from 27.5% (chronic kidney disease equation) to 98.0% (Bernstein equation). The highest overprediction value was found in the Schofield equation (17.7%). The Cunningham equation and the new equation had the lowest root mean square error (265 kcal/day). In this study, fat-free mass (FFM) was found to be the most significant variable in multiple regression analysis (r²: 0.55). The new specific equation based on FFM was generated as 424.2 + 24.7∗FFM (kg). Besides that, it was found that the new equation and Cunningham equation were distributed randomly according to Bland-Altman analysis. A supplementary new equation based on available anthropometric measurements was developed as -1996.8 + 19.1∗height (cm) + 7.2∗body weight (kg).

CONCLUSION

This study showed that most of the predictive equations significantly underestimated REE. In patients with renal transplantation, if the REE is not measurable by indirect calorimetry, the use of the proposed equations will be more accurate.

A novel prediction equation of resting energy expenditure for Japanese septic patients

Estimating nutrient consumption and administering appropriate nutritional therapy is essential for improving clinical outcomes in critically ill patients. Various equations, such as the Harris-Benedict equation, have been developed to estimate the required calories. Previous equations, however, targeted Westerners, whose physical characteristics are likely different from those of Asians. Hence, it is unclear whether these equations can be used for Asian patients. This study focused specifically on sepsis patients admitted to a single Japanese ICU, and aimed to develop novel equations to estimate their total energy expenditure. A total of 95 sepsis patients were included in this study. We measured resting energy expenditure (REE) by using indirect calorimetry, and created equations to calculate basal metabolic rate (BMR) using height, weight and age as variables. REE was predicted by multiplying BMR by the novel equation with the stress factor of 1.4. The prediction error of our novel equations were smaller than those of other conventional equations. We further confirmed the accuracy of our equations and that they were unaffected by patient age and disease severity by using data obtained from another patient group. The current study suggested that these equations might allow accurate estimation of the total energy expenditure and proper management of nutritional therapy in Asian sepsis patients.

Factors Related to the Assessment of Resting Metabolic Rate in Critically Ill Patients

BACKGROUND

Predicting resting metabolic rate (RMR) in mechanically ventilated, critically ill patients is an important part of the nutrition care in such patients.

METHODS

RMR and associated clinical data from various studies of mechanically ventilated, critically ill patients were combined, and the impact of body size, age, reason for admission, and sedation level were analyzed along with prediction methods of RMR (the American Society for Parenteral and Enteral Nutrition [ASPEN] standards and the Penn State equation).

RESULTS

Among 826 measurements, trauma patients had a higher RMR than surgical and medical patients (2077 ± 290 vs 1987 ± 282 kcal/d; P < .0001). RMR was not different in sedated vs unsedated patients. Wide ranges of weight (27-374 kg) and age (18-95 years) were captured. The relationships between weight and RMR and RMR and age were curvilinear. For weight-based ratio methods of RMR prediction, <50% of predictions were within the range in which they were designed to work. The accuracy of the Penn State equation was better in some weight categories than others. New equations based on a wider range of body weights and ages are presented.

CONCLUSIONS

Curvilinear functions exist for weight and age in relation to RMR, but extraordinary levels of each are required for the curve to become apparent. The ASPEN energy standards (kcal/kg body weight) fail to predict RMR because the relationship is more complex than a simple ratio. The Penn State equations are better able to model these relationships. The new versions of the equation presented here await validation.

Indirect calorimetry in critically ill mechanically ventilated patients: Comparison of E-sCOVX with the deltatrac

BACKGROUND & AIMS

Indirect calorimetry is recommended to measure energy expenditure (EE) in critically ill, mechanically ventilated patients. The most validated system, the Deltatrac® (Datex-Ohmeda, Helsinki, Finland) is no longer in production. We tested the agreement of a new breath-by-breath metabolic monitor E-sCOVX® (GE healthcare, Helsinki, Finland), with the Deltatrac. We also compared the performance of the E-sCOVX to commonly used predictive equations.

METHODS

We included mechanically ventilated patients eligible to undergo indirect calorimetry. After a stabilization period, EE was measured simultaneously with the Deltatrac and the E-sCOVX for 2 h. Agreement and precision of the E-sCOVX was tested by determining bias, limits of agreement and agreement rates compared to the Deltatrac. Performance of the E-sCOVX was also compared to four predictive equations: the 25 kcal/kg, Penn State University 2003b, Faisy, and Harris-Benedict equation.

RESULTS

We performed 29 measurements in 16 patients. Mean EE-Deltatrac was 1942 ± 274 kcal/day, and mean EE-E-sCOVX was 2177 ± 319 kcal/day (p < 0.001). E-sCOVX overestimated EE with a bias of 235 ± 149 kcal/day, being 12.1% of EE-Deltatrac. Limits of agreement were -63 to +532 kcal/day. The 10% and 15% agreement rates of EE-E-sCOVX compared to the Deltatrac were 34% and 72% respectively. The bias of E-sCOVX was lower than the bias of the 25 kcal/kg-equation, but higher than bias of the other equations. Agreement rates for E-sCOVX were similar to the equations. The Faisy-equation had the highest 15% agreement rate.

CONCLUSION

The E-sCOVX metabolic monitor is not accurate in estimating EE in critically ill mechanically ventilated patients when compared to the Deltatrac, the present reference method. The E-sCOVX overestimates EE with a bias and precision that are clinically unacceptable.

Estimation of Resting Energy Expenditure Using Predictive Equations in Critically Ill Children: Results of a Systematic Review

Provision of adequate energy intake to critically ill children is associated with improved prognosis, but resting energy expenditure (REE) is rarely determined by indirect calorimetry (IC) due to practical constraints. Some studies have tested the validity of various predictive equations that are routinely used for this purpose, but no systematic evaluation has been made. Therefore, we performed a systematic review of the literature to assess predictive equations of REE in critically ill children. We systematically searched the literature for eligible studies, and then we extracted data and assigned a quality grade to each article according to guidelines of the Academy of Nutrition and Dietetics. Accuracy was defined as the percentage of predicted REE values to fall within ±10% or ±15% of the measured energy expenditure (MEE) values, computed based on individual participant data. Of the 993 identified studies, 22 studies testing 21 equations using 2326 IC measurements in 1102 children were included in this review. Only 6 equations were evaluated by at least 3 studies in critically ill children. No equation predicted REE within ±10% of MEE in >50% of observations. The Harris-Benedict equation overestimated REE in two-thirds of patients, whereas the Schofield equations and Talbot tables predicted REE within ±15% of MEE in approximately 50% of observations. In summary, the Schofield equations and Talbot tables were the least inaccurate of the predictive equations. We conclude that a new validated indirect calorimeter is urgently needed in the critically ill pediatric population.).

Modeling a Predictive Energy Equation Specific for Maintenance Hemodialysis

BACKGROUND

Hypermetabolism is theorized in patients diagnosed with chronic kidney disease who are receiving maintenance hemodialysis (MHD). We aimed to distinguish key disease-specific determinants of resting energy expenditure to create a predictive energy equation that more precisely establishes energy needs with the intent of preventing protein-energy wasting.

MATERIALS AND METHODS

For this 3-year multisite cross-sectional study (N = 116), eligible participants were diagnosed with chronic kidney disease and were receiving MHD for at least 3 months. Predictors for the model included weight, sex, age, C-reactive protein (CRP), glycosylated hemoglobin, and serum creatinine. The outcome variable was measured resting energy expenditure (mREE). Regression modeling was used to generate predictive formulas and Bland-Altman analyses to evaluate accuracy.

RESULTS

The majority were male (60.3%), black (81.0%), and non-Hispanic (76.7%), and 23% were ≥65 years old. After screening for multicollinearity, the best predictive model of mREE (R2 = 0.67) included weight, age, sex, and CRP. Two alternative models with acceptable predictability (R2 = 0.66) were derived with glycosylated hemoglobin or serum creatinine. Based on Bland-Altman analyses, the maintenance hemodialysis equation that included CRP had the best precision, with the highest proportion of participants' predicted energy expenditure classified as accurate (61.2%) and with the lowest number of individuals with underestimation or overestimation.

CONCLUSIONS

This study confirms disease-specific factors as key determinants of mREE in patients on MHD and provides a preliminary predictive energy equation. Further prospective research is necessary to test the reliability and validity of this equation across diverse populations of patients who are receiving MHD.

Adequacy of nutrition support during extracorporeal membrane oxygenation

BACKGROUND

The use of veno-venous extracorporeal membrane oxygenation (vv-ECMO) is increasing in adults with severe respiratory failure. Observational data suggest that there are significant challenges to providing adequate nutrition support for patients on vv-ECMO. We aimed to describe firstly the nutrition support practices in a large single-centre providing vv-ECMO to adults and secondly any association with clinical outcome.

METHODS

We conducted a retrospective review of patients receiving vv-ECMO on the Intensive Care Unit (ICU) of a large London teaching hospital. Adult patients admitted to the ICU with severe respiratory failure between December 2010 and December 2015 were included. Daily energy and protein delivery were compared with estimated targets and reasons for feeding interruptions were collected from electronic medical records. Adequate feeding was defined as 80-110% of estimated targets.

RESULTS

We analysed 203 eligible patients. Median duration of ICU stay was 21.0 (IQR, 15.0-33.0) days and vv-ECMO 10.0 (IQR, 7.0-16.0) days. Although median energy (89.8% (IQR, 80.5-96.0%)) and protein (84.7% (IQR, 74.0-96.7%)) delivery was adequate, underfeeding of either energy or protein occurred on nearly one third (28.3%) of nutrition support days. A higher admission severity of illness score was associated with inadequate protein delivery (p = 0.040). Patients with more severe organ dysfunction on the first day of vv-ECMO received inadequate energy (p = 0.026). The most common reasons for interrupted feeding were medical procedures (39.1%) followed by poor gastric motility (22.8%).

CONCLUSION

Adequate energy and protein delivery during vv-ECMO is possible but underfeeding is still common, especially in those who are more severely ill or who have more severe organ dysfunction. Patients with inadequate energy or protein delivery did not differ in ICU and 6-month survival. Prospective studies investigating optimal feeding in this patient cohort are required.

Determination of the energy requirements in mechanically ventilated critically ill elderly patients in different BMI groups using the Harris-Benedict equation

BACKGROUND

Due to studies on calorie requirement in mechanically ventilated critically ill elderly patients are few, and indirect calorimetry (IC) is not available in every intensive care unit (ICU). The aim of this study was to compare IC and Harris-Benedict (HB) predictive equation in different BMI groups.

METHODS

A total of 177 mechanically ventilated critically ill elderly patients (≧65 years old) underwent IC for measured resting energy expenditure (MREE). Estimated calorie requirement was calculated by the HB equation, using actual body weight (ABW) and ideal body weight (IBW) separately. Patients were divided into four BMI groups. One-way ANOVA and Pearson's correlation coefficient were used for statistical analyses.

RESULTS

The mean MREE was 1443.6 ± 318.2 kcal/day, HB(ABW) was 1110.9 ± 177.0 kcal/day and HB(IBW) was 1101.5 ± 113.1 kcal/day. The stress factor (SFA = MREE ÷ HB(ABW)) was 1.43 ± 0.26 for the underweight, 1.30 ± 0.27 for the normal weight, 1.20 ± 0.19 for the overweight, and 1.20 ± 0.31 for the obese. The SFI (SFI = MREE ÷ HB(IBW)) was 1.24 ± 0.24 for the underweight, 1.31 ± 0.26 for the normal weight, 1.36 ± 0.21 for the overweight, and 1.52 ± 0.39 for the obese. MREE had significant correlation both with REE(ABW) = HB(ABW) × SFA (r = 0.46; P < 0.0001) and REE(IBW) = HB(IBW) × SFI (r = 0.43; P < 0.0001).

CONCLUSION

IC is the best accurate method for assessing calorie requirement of mechanically ventilated critically ill elderly patients. When IC is not available, using the predictive HB equation is an alternative choice. Calorie requirement can be predicted by HB(ABW) × 1.20-1.43 for critically ill elderly patients according to different BMI groups, or using HB(IBW) × 1.24-1.52 for patients with edema, ascites or no available body weight data.

Gut microbiota and body composition in anorexia nervosa inpatients in comparison to athletes, overweight, obese, and normal weight controls

OBJECTIVES

Anorexia nervosa (AN) is a heterogeneous eating disorder associated with alterations of body structure and the gut microbiome. We aimed to investigate the gut microbiota composition of a large female cohort including different BMI groups and activity levels along with body composition parameters.

METHOD

106 female participants were included in this cross-sectional study: AN patients (n = 18), athletes (n = 20), normal weight (n = 26), overweight (n = 22), and obese women (n = 20). DNA was extracted from stool samples and subjected to 16S rRNA gene analysis. The software Quantitative Insights Into Microbial Ecology (QIIME) was used to analyze data. Additionally, we performed anthropometric assessments, ultrasound measurements of subcutaneous adipose tissue thickness, bioimpedance analysis, administered depression inventories, and ascertained laboratory parameters and dietary intakes.

RESULTS

Alpha diversity was particularly lower in AN patients and obese participants compared to other groups, while athletes showed highest alpha diversity. Several categories significantly associated with community structure were identified: body fat parameters, serum lipids, CRP, depression scales and smoking. Comparative analysis revealed Coriobacteriaceae as the only enriched phylotype in AN compared to other entities (LDA score >3.5).

DISCUSSION

This study provides further evidence of intestinal dysbiosis in AN and sheds light on characteristics of the gut microbiome in different BMI and physical activity groups. These insights point to new modulation possibilities of the gut microbiota which could improve the standard therapy of AN.

Energy and Protein in Critically Ill Patients with AKI: A Prospective, Multicenter Observational Study Using Indirect Calorimetry and Protein Catabolic Rate

The optimal nutritional support in Acute Kidney Injury (AKI) still remains an open issue. The present study was aimed at evaluating the validity of conventional predictive formulas for the calculation of both energy expenditure and protein needs in critically ill patients with AKI. A prospective, multicenter, observational study was conducted on adult patients hospitalized with AKI in three different intensive care units (ICU). Nutrient needs were estimated by different methods: the Guidelines of the European Society of Parenteral and Enteral Nutrition (ESPEN) for both calories and proteins, the Harris-Benedict equation, the Penn-State and Faisy-Fagon equations for energy. Actual energy and protein needs were repeatedly measured by indirect calorimetry (IC) and protein catabolic rate (PCR) until oral nutrition start, hospital discharge or renal function recovery. Forty-two patients with AKI were enrolled, with 130 IC and 123 PCR measurements obtained over 654 days of artificial nutrition. No predictive formula was precise enough, and Bland-Altman plots wide limits of agreement for all equations highlight the potential to under- or overfeed individual patients. Conventional predictive formulas may frequently lead to incorrect energy and protein need estimation. In critically ill patients with AKI an increased risk for under- or overfeeding is likely when nutrient needs are estimated instead of measured.

Energy, Protein, Carbohydrate, and Lipid Intakes and Their Effects on Morbidity and Mortality in Critically Ill Adult Patients: A Systematic Review

The guidelines for nutritional support in critically ill adult patients differ in various aspects. The optimal amount of energy and nutritional substrates supplied is important for reducing morbidity and mortality, but unfortunately this is not well known, because the topic is complex and every patient is individual. The aim of this review was to gather recent pertinent information concerning the nutritional support of critically ill patients in the intensive care unit (ICU) with respect to the energy, protein, carbohydrate, and lipid intakes and the effect of their specific utilization on morbidity and mortality. Enteral nutrition (EN) is generally recommended over parenteral nutrition (PN) and is beneficial when administered within 24-48 h after ICU admission. In contrast, early PN does not provide substantial advantages in terms of morbidity and mortality, and the time when it is safe and beneficial remains unclear. The most advantageous recommendation seems to be administration of a hypocaloric (<20 kcal · kg^-1 · d^-1), high-protein diet (amino acids at doses of ≥2 g · kg^-1 · d^-1), at least during the first week of critical illness. Another important factor for reducing morbidity is the maintenance of blood glucose concentrations at 120-150 mg/dL, which is accomplished with the use of insulin and lower doses of glucose of 1-2 g · kg^-1 · d^-1, because this prevents the risk of hypoglycemia and is associated with a better prognosis according to recent studies. A fat emulsion is used as a source of required calories because of insulin resistance in the majority of patients. In addition, lipid oxidation in these patients is ∼25% higher than in healthy subjects.

Gender- and Age-Specific REE and REE/FFM Distributions in Healthy Chinese Adults

Basic data on the resting energy expenditure (REE) of healthy populations are currently rare, especially for developing countries. The aims of the present study were to describe gender- and age-specific REE distributions and to evaluate the relationships among glycolipid metabolism, eating behaviors, and REE in healthy Chinese adults. This cross-sectional survey included 540 subjects (343 women and 197 men, 20–79 years old). REE was measured by indirect calorimetry and expressed as kcal/day/kg total body weight. The data were presented as the means and percentiles for REE and the REE to fat-free mass (FFM) ratio; differences were described by gender and age. Partial correlation analysis was used to analyze the correlations between REE, tertiles of REE/FFM, and glycolipid metabolism and eating behaviors. In this study, we confirmed a decline in REE with age in women (p = 0.000) and men (p = 0.000), and we found that men have a higher REE (p = 0.000) and lower REE/FFM (p = 0.021) than women. Furthermore, we observed no associations among glycolipid metabolism, eating behaviors, and REE in healthy Chinese adults. In conclusion, the results presented here may be useful to clinicians and nutritionists for comparing healthy and ill subjects and identifying changes in REE that are related to aging, malnutrition, and chronic diseases.

Nutritional Alterations Associated with Neurological and Neurosurgical Diseases

Neurological and neurosurgical diseases lead to complications producing malnutrition increasing pathology and mortality. In order to avoid complications because of malnutrition or overcome deficiencies in nutrients supplements are often used for these subjects. The physiopathological mechanisms of malnutrition, methods of nutritional assessment and the supplemental support are reviewed in this paper based on the assumption that patients need to receive adequate nutrition to promote optimal recovery, placing nutrition as a first line treatment and not an afterthought in the rehabilitation.

ACG Clinical Guideline: Nutrition Therapy in the Adult Hospitalized Patient

The value of nutrition therapy for the adult hospitalized patient is derived from the outcome benefits achieved by the delivery of early enteral feeding. Nutritional assessment should identify those patients at high nutritional risk, determined by both disease severity and nutritional status. For such patients if they are unable to maintain volitional intake, enteral access should be attained and enteral nutrition (EN) initiated within 24-48 h of admission. Orogastric or nasogastric feeding is most appropriate when starting EN, switching to post-pyloric or deep jejunal feeding only in those patients who are intolerant of gastric feeds or at high risk for aspiration. Percutaneous access should be used for those patients anticipated to require EN for >4 weeks. Patients receiving EN should be monitored for risk of aspiration, tolerance, and adequacy of feeding (determined by percent of goal calories and protein delivered). Intentional permissive underfeeding (and even trophic feeding) is appropriate temporarily for certain subsets of hospitalized patients. Although a standard polymeric formula should be used routinely in most patients, an immune-modulating formula (with arginine and fish oil) should be reserved for patients who have had major surgery in a surgical ICU setting. Adequacy of nutrition therapy is enhanced by establishing nurse-driven enteral feeding protocols, increasing delivery by volume-based or top-down feeding strategies, minimizing interruptions, and eliminating the practice of gastric residual volumes. Parenteral nutrition should be used in patients at high nutritional risk when EN is not feasible or after the first week of hospitalization if EN is not sufficient. Because of their knowledge base and skill set, the gastroenterologist endoscopist is an asset to the Nutrition Support Team and should participate in providing optimal nutrition therapy to the hospitalized adult patient.

Energy Expenditure in Critically Ill Elderly Patients: Indirect Calorimetry vs Predictive Equations

BACKGROUND

Predictive equations (PEs) are used for estimating resting energy expenditure (REE) when the measurements obtained from indirect calorimetry (IC) are not available. This study evaluated the degree of agreement and the accuracy between the REE measured by IC (REE-IC) and REE estimated by PE (REE-PE) in mechanically ventilated elderly patients admitted to the intensive care unit (ICU).

METHODS

REE-IC of 97 critically ill elderly patients was compared with REE-PE by 6 PEs: Harris and Benedict (HB) multiplied by the correction factor of 1.2; European Society for Clinical Nutrition and Metabolism (ESPEN) using the minimum (ESPENmi), average (ESPENme), and maximum (ESPENma) values; Mifflin-St Jeor; Ireton-Jones (IJ); Fredrix; and Lührmann. Degree of agreement between REE-PE and REE-IC was analyzed by the interclass correlation coefficient and the Bland-Altman test. The accuracy was calculated by the percentage of male and/or female patients whose REE-PE values differ by up to ±10% in relation to REE-IC.

RESULTS

For both sexes, there was no difference for average REE-IC in kcal/kg when the values obtained with REE-PE by corrected HB and ESPENme were compared. A high level of agreement was demonstrated by corrected HB for both sexes, with greater accuracy for women. The best accuracy in the male group was obtained with the IJ equation but with a low level of agreement.

CONCLUSIONS

The effectiveness of PEs is limited for estimating REE of critically ill elderly patients. Nonetheless, HB multiplied by a correction factor of 1.2 can be used until a specific PE for this group of patients is developed.

Ventilator-derived carbon dioxide production to assess energy expenditure in critically ill patients: proof of concept

Introduction

Measurement of energy expenditure (EE) is recommended to guide nutrition in critically ill patients. Availability of a gold standard indirect calorimetry is limited, and continuous measurement is unfeasible. Equations used to predict EE are inaccurate. The purpose of this study was to provide proof of concept that EE can be accurately assessed on the basis of ventilator-derived carbon dioxide production (VCO₂) and to determine whether this method is more accurate than frequently used predictive equations.

Methods

In 84 mechanically ventilated critically ill patients, we performed 24-h indirect calorimetry to obtain a gold standard EE. Simultaneously, we collected 24-h ventilator-derived VCO₂, extracted the respiratory quotient of the administered nutrition, and calculated EE with a rewritten Weir formula. Bias, precision, and accuracy and inaccuracy rates were determined and compared with four predictive equations: the Harris–Benedict, Faisy, and Penn State University equations and the European Society for Clinical Nutrition and Metabolism (ESPEN) guideline equation of 25 kcal/kg/day.

Results

Mean 24-h indirect calorimetry EE was 1823 ± 408 kcal. EE from ventilator-derived VCO₂ was accurate (bias +141 ± 153 kcal/24 h; 7.7 % of gold standard) and more precise than the predictive equations (limits of agreement −166 to +447 kcal/24 h). The 10 % and 15 % accuracy rates were 61 % and 76 %, respectively, which were significantly higher than those of the Harris–Benedict, Faisy, and ESPEN guideline equations. Large errors of more than 30 % inaccuracy did not occur with EE derived from ventilator-derived VCO₂. This 30 % inaccuracy rate was significantly lower than that of the predictive equations.

Conclusions

In critically ill mechanically ventilated patients, assessment of EE based on ventilator-derived VCO₂ is accurate and more precise than frequently used predictive equations. It allows for continuous monitoring and is the best alternative to indirect calorimetry.

Estimating dead-space fraction for secondary analyses of acute respiratory distress syndrome clinical trials

OBJECTIVES

Pulmonary dead-space fraction is one of few lung-specific independent predictors of mortality from acute respiratory distress syndrome. However, it is not measured routinely in clinical trials and thus altogether ignored in secondary analyses that shape future research directions and clinical practice. This study sought to validate an estimate of dead-space fraction for use in secondary analyses of clinical trials.

DESIGN

Analysis of patient-level data pooled from acute respiratory distress syndrome clinical trials. Four approaches to estimate dead-space fraction were evaluated: three required estimating metabolic rate; one estimated dead-space fraction directly.

SETTING

U.S. academic teaching hospitals.

PATIENTS

Data from 210 patients across three clinical trials were used to compare performance of estimating equations with measured dead-space fraction. A second cohort of 3,135 patients from six clinical trials without measured dead-space fraction was used to confirm whether estimates independently predicted mortality.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Dead-space fraction estimated using the unadjusted Harris-Benedict equation for energy expenditure was unbiased (mean ± SD Harris-Benedict, 0.59 ± 0.13; measured, 0.60 ± 0.12). This estimate predicted measured dead-space fraction to within ±0.10 in 70% of patients and ±0.20 in 95% of patients. Measured dead-space fraction independently predicted mortality (odds ratio, 1.36 per 0.05 increase in dead-space fraction; 95% CI, 1.10-1.68; p < 0.01). The Harris-Benedict estimate closely approximated this association with mortality in the same cohort (odds ratio, 1.55; 95% CI, 1.21-1.98; p < 0.01) and remained independently predictive of death in the larger Acute Respiratory Distress Syndrome Network cohort. Other estimates predicted measured dead-space fraction or its association with mortality less well.

CONCLUSIONS

Dead-space fraction should be measured in future acute respiratory distress syndrome clinical trials to facilitate incorporation into secondary analyses. For analyses where dead-space fraction was not measured, the Harris-Benedict estimate can be used to estimate dead-space fraction and adjust for its association with mortality.

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

BACKGROUND

Nutrition equations have been validated with indirect calorimetry for determining energy needs in intensive care unit (ICU) populations. This study tested the hypothesis that mechanically ventilated cardiothoracic surgical patients would have significantly different energy requirements when determined by indirect calorimetry vs the Penn State equations.

MATERIALS AND METHODS

This single-center, retrospective cohort analysis of consecutive cardiothoracic surgical patients adhered to a prospectively designed protocol for indirect calorimetry energy measurements. Energy needs were estimated by Penn State equations 2010 and 2003b and then indirect calorimetry.

RESULTS

Analyzed patients (n = 71) had a mean ± SD difference of 556 ± 543 calories/d between indirect calorimetry and Penn State formulae, as well as a mean ± SD percentage caloric difference of 32% ± 31% (95% confidence interval [CI], -20 to 87) with a range of 1311 calories (minimum difference, -379; maximum difference, 933). There was a 10% or greater difference in resting metabolic rate between indirect calorimetry and the Penn State equations in 89% of patients (95% CI, 79%-95%). Based on Lin's concordance correlation of 0.20 (95% CI, 0.09-0.32), the strength of agreement between the resting metabolic rates determined by indirect calorimetry compared with the Penn State equations was poor within this patient sample. Indirect calorimetry performance showed a 10% increase in caloric need in 77% of patients and was associated with a nutrition prescription change in 66%.

CONCLUSIONS

Mechanically ventilated cardiothoracic surgical ICU patients appear to have higher energy requirements by indirect calorimetry than those determined by Penn State equations. Future studies targeting indirect calorimetry in relation to clinical outcomes are needed.

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

BACKGROUND

Current guidelines from the American Society for Parenteral and Enteral Nutrition and the Society of Critical Care Medicine (ASPEN/SCCM) regarding caloric requirements and the provision of nutrition support in critically ill, obese adults may not be suitable for similar patients with cancer. We sought to determine whether the current guidelines accurately estimate the energy requirements, as measured by indirect calorimetry (IC), of critically ill, obese cancer patients.

MATERIALS AND METHODS

This was a retrospective validation study of critically ill, obese cancer patients from March 1, 2007, to July 31, 2010. All patients ≥18 years of age with a body mass index (BMI) ≥30 kg/m(2) who underwent IC were included. We compared the measured energy expenditure (MEE) against the upper limit of the recommended guideline (25 kcal/kg of ideal body weight [IBW]) and MEE between medical and surgical patients in the intensive care unit.

RESULTS

Thirty-three patients were included in this study. Mean MEE (28.7 ± 5.2 kcal/kg IBW) was significantly higher than 25 kcal/kg IBW (P < .001), and 78% of patients had nutrition requirements greater than the current guideline recommendations. No significant differences in MEE between medical and surgical patients in the ICU were observed.

CONCLUSIONS

Critically ill, obese cancer patients require more calories than the current guidelines recommend, likely due to malignancy-associated metabolic variations. Our results demonstrate the need for IC studies to determine the energy requirements in these patients and for reassessment of the current recommendations.

Energy requirements and the use of predictive equations versus indirect calorimetry in critically ill patients

Nutrition support has been shown to have a positive impact on critically ill patients who meet their defined goals of nutrition therapy. However, inappropriate energy assessment can contribute to under- or overfeeding resulting in deleterious effects. Thus, assessment of energy expenditure in critically ill patients is crucial to prevent negative impacts from inappropriate feeding. Currently, the optimal energy requirement and appropriate energy assessment in these patients is controversial. Indirect calorimetry or predictive equations have been suggested to evaluate energy expenditure in critically ill patients. Indirect calorimetry is a gold standard for evaluating energy expenditure, but it is not always available and has some limitations. Many predictive equations, therefore, have been developed to predict energy expenditure in critically ill patients. However, these equations cannot be used generally in these patients since they were developed in a unique patient population. Many studies compared measured energy expenditure with predictive energy expenditure, but the data regarding accuracy is not robust. Therefore, clinicians should consider using these equations carefully based on the current supporting data. Indirect calorimetry is recommended for use in evaluating energy expenditure in critically ill patients if it is available.

Clinical Outcomes in Critically Ill Patients Associated With the Use of Complex vs Weight-Only Predictive Energy Equations

BACKGROUND

The energy intake goal is important to achieving energy intake in critically ill patients, yet clinical outcomes associated with energy goals have not been reported.

METHODS

This secondary analysis used the Improving Nutrition Practices in the Critically III International Nutrition Surveys database from 2007-2009 to evaluate whether mortality or time to discharge alive is related to use of complex energy prediction equations vs weight only. The sample size was 5672 patients in the intensive care unit (ICU) ≥ 4 days and a subset of 3356 in the ICU ≥ 12 days. Mortality and time to discharge alive were compared between groups by regression, controlling for age, sex, admission type, Acute Physiology and Chronic Health Evaluation II score, ICU geographic region, actual energy intake, and obesity.

RESULTS

There was no difference in mortality between the use of complex and weight-only equations (odds ratio [OR], 0.90; 95% confidence interval [CI], 0.86-1.15), but obesity (OR, 0.83; 95% CI, 0.71-0.96) and higher energy intake (OR, 0.65; 95% CI, 0.56-0.76) had lower odds of mortality. Time to discharge alive was shorter in patients fed using weight-only equations (hazard ratio [HR], 1.11; 95% CI, 1.01-1.23) in patients staying ≥ 4 days and with greater energy intake (HR, 1.19; 95% CI, 1.06-1.34) in patients in the ICU ≥ 12 days.

CONCLUSION

These data suggest that higher energy intake is important to survival and time to discharge alive. However, the analysis was limited by actual energy intake <70% of goal. Delivery of full goal intake will be needed to determine the relationship between the method of determining energy goal and clinical outcomes.

Development of a predictive energy equation for maintenance hemodialysis patients: a pilot study

OBJECTIVE

The study objectives were to explore the predictors of measured resting energy expenditure (mREE) among a sample of maintenance hemodialysis (MHD) patients, to generate a predictive energy equation (MHDE), and to compare such models to another commonly used predictive energy equation in nutritional care, the Mifflin-St. Jeor equation (MSJE).

DESIGN AND METHODS

The study was a retrospective, cross-sectional cohort design conducted at the Vanderbilt University Medical Center. Study subjects were adult MHD patients (N = 67). Data collected from several clinical trials were analyzed using Pearson's correlation and multivariate linear regression procedures. Demographic, anthropometric, clinical, and laboratory data were examined as potential predictors of mREE. Limits of agreement between the MHDE and the MSJE were evaluated using Bland-Altman plots. The a priori α was set at P < .05. The main outcome measure was mREE.

RESULTS

The mean age of the sample was 47 ± 13 years. Fifty participants (75.6%) were African American, 7.5% were Hispanic, and 73.1% were males. Fat-free mass (FFM), serum albumin (ALB), age, weight, serum creatinine (CR), height, body mass index, sex, high-sensitivity C-reactive protein (CRP), and fat mass (FM) were all significantly (P < .05) correlated with mREE. After screening for multi-collinearity, the best predictive model (MHDE-lean body mass [LBM]) of mREE included (R(2) = 0.489) FFM, ALB, age, and CRP. Two additional models (MHDE-CRP and MHDE-CR) with acceptable predictability (R(2) = 0.460 and R(2) = 0.451) were derived to improve the clinical utility of the developed energy equation (MHDE-LBM). Using Bland-Altman plots, the MHDE over- and underpredicted mREE less often than the MSJE.

CONCLUSIONS

Predictive models (MHDE) including selective demographic, clinical, and anthropometric data explained less than 50% variance of mREE but had better precision in determining energy requirements for MHD patients when compared with MSJE. Further research is necessary to improve predictive models of mREE in the MHD population and to test its validity and clinical application.

Predicted and measured resting metabolic rate in young, non-obese women

PURPOSE

Measured resting metabolic rate (RMR) was compared with predicted RMR in a sample of young, non-obese women.

METHODS

In 52 women aged 19 to 30 with a body mass index of 16 to 29 kg/m2, RMR was measured with a MedGem indirect calorimeter and predicted with five commonly used equations: the Harris-Benedict (1919), Mifflin (1989), Owen (1985), Schofield (weight) (1985), and Schofield (weight and height) (1985) equations. Measured RMR and predicted RMR were compared through the use of various measures.

RESULTS

In comparison with the measured RMR, the RMR predicted with four of the five equations was significantly higher (by 16 to 225 kcal/day, p < 0.001). At the group level, the Owen equation performed best and captured the greatest proportion of individuals (65%) for whom predicted RMR differed from measured RMR by less than 10%. With the other four equations, residuals exceeded 10% for more than two-thirds of participants. For the Harris-Benedict, Mifflin, and Owen equations, every 100 kcal/day increase in measured RMR was associated with a 6% to 8% decrease in error. The optimal prediction range (within 10% of the measured RMR) was different for each: Owen equation 1105 to 1400 kcal/day, Mifflin equation 1280 to 1595 kcal/day, and Harris-Benedict equation 1345 to 1630 kcal/day.

CONCLUSIONS

Prediction equations should be modified according to the amount of corresponding percentage error. Where possible, RMR should be measured. Barring this, the Owen equation should be used for young, non-obese women.

Common Prediction Equations Overestimate Measured Resting Metabolic Rate in Young Hispanic Women

The accuracy of 6 resting metabolic rate (RMR) prediction equations to indirect calorimetry was compared in 38 Hispanic women (age = 30 ± 7 years; body mass index = 28.9 ± 7.2 kg/m2; body fat = 42% ± 8%). Paired t tests examined differences between predicted and measured RMR; significance defined as P < 0.05. Bias and agreement were displayed using Bland-Altman plots. Accuracy was defined when the predicted RMR was ± 10% of the measured RMR. Data were analyzed with SPSS (version 19). Only the equation of Owen et al was not significantly different from the measured RMR (1336 ± 142 and 1322 ± 203 kcal/d, respectively). The equation of Owen et al was accurate for 84.2% of women; RMR prediction equations had limited applicability for young Hispanic women.

[Guidelines for specialized nutritional and metabolic support in the critically ill-patient. Update. Consensus of the Spanish Society of Intensive Care Medicine and Coronary Units-Spanish Society of Parenteral and Enteral Nutrition (SEMICYUC-SENPE): obese patient]

As a response to metabolic stress, obese critically-ill patients have the same risk of nutritional deficiency as the non-obese and can develop protein-energy malnutrition with accelerated loss of muscle mass. The primary aim of nutritional support in these patients should be to minimize loss of lean mass and accurately evaluate energy expenditure. However, routinely-used formulae can overestimate calorie requirements if the patient's actual weight is used. Consequently, the use of adjusted or ideal weight is recommended with these formulae, although indirect calorimetry is the method of choice. Controversy surrounds the question of whether a strict nutritional support criterion, adjusted to the patient's requirements, should be applied or whether a certain degree of hyponutrition should be allowed. Current evidence suggested that hypocaloric nutrition can improve results, partly due to a lower rate of infectious complications and better control of hyperglycemia. Therefore, hypocaloric and hyperproteic nutrition, whether enteral or parenteral, should be standard practice in the nutritional support of critically-ill obese patients when not contraindicated. Widely accepted recommendations consist of no more than 60-70% of requirements or administration of 11-14 kcal/kg current body weight/day or 22-25 kcal/kg ideal weight/day, with 2-2.5 g/kg ideal weight/day of proteins. In a broad sense, hypocaloric-hyperprotein regimens can be considered specific to obese critically-ill patients, although the complications related to comorbidities in these patients may require other therapeutic possibilities to be considered, with specific nutrients for hyperglycemia, acute respiratory distress syndrome (ARDS) and sepsis. However, there are no prospective randomized trials with this type of nutrition in this specific population subgroup and the available data are drawn from the general population of critically-ill patients. Consequently, caution should be exercised when interpreting these data.

[Guidelines for specialized nutritional and metabolic support in the critically-ill patient. Update. Consensus of the Spanish Society of Intensive Care Medicine and Coronary Units-Spanish Society of Parenteral and Enteral Nutrition (SEMICYUC-SENPE): macro-and micronutrient requirements]

Energy requirements are altered in critically-ill patients and are influenced by the clinical situation, treatment, and phase of the process. Therefore, the most appropriate method to calculate calorie intake is indirect calorimetry. In the absence of this technique, fixed calorie intake (between 25 and 35 kcal/kg/day) or predictive equations such as the Penn State formula can be used to obtain a more accurate evaluation of metabolic rate. Carbohydrate administration should be limited to a maximum of 4 g/kg/day and a minimum of 2g/kg/day. Plasma glycemia should be controlled to avoid hyperglycemia. Fat intake should be between 1 and 1.5 g/kg/day. The recommended protein intake is 1-1.5 g/kg/day but can vary according to the patient's clinical status. Particular attention should be paid to micronutrient intake. Consensus is lacking on micronutrient requirements. Some vitamins (A, B, C, E) are highly important in critically-ill patients, especially those undergoing continuous renal replacement techniques, patients with severe burns and alcoholics, although the specific requirements in each of these types of patient have not yet been established. Energy and protein intake in critically-ill patients is complex, since both clinical factors and the stage of the process must be taken into account. The first step is to calculate each patient's energy requirements and then proceed to distribute calorie intake among its three components: proteins, carbohydrates and fat. Micronutrient requirements must also be considered.

The challenge of developing a new predictive formula to estimate energy requirements in ventilated critically ill children

BACKGROUND

Traditionally, energy requirements have been calculated using predictive equations. These methods have failed to calculate energy expenditure accurately. Routine indirect calorimetry has been suggested, but this method is technically demanding and costly. This study aimed to develop a new predictive equation to estimate energy requirements for critically ill children.

METHODS

This prospective, observational study on ventilated children included patients with an endotracheal tube leak of < 10% and fractional inspired oxygen of < 60%. An indirect calorimetry energy expenditure measurement was performed and polynomial regression analysis was used to develop new predictive equations. The new formulas were then compared with existing prediction equations.

RESULTS

Data from 369 measurements were included in the formula design. Only weight and diagnosis influenced energy expenditure significantly. Three formulas (A, B, C) with an R² > 0.8 were developed. When we compared the new formulas with commonly used equations (Schofield, Food and Agriculture Organization/World Health Organization/United Nations University, and White equation), all formulas performed very similar, but the Schofield equation seemed to have the lowest SD.

CONCLUSIONS

All 3 new pediatric intensive care unit equations have R² values of > 0.8; however, the Schofield equation still performed better than other predictive methods in predicting energy expenditure in these patients. Still, none of the predictive equations, including the new equations, predicted energy expenditure within a clinically accepted range, and further research is required, particularly for patients outside the technical scope of indirect calorimetry.

Bedside calculation of energy expenditure does not guarantee adequate caloric prescription in long-term mechanically ventilated critically ill patients: a quality control study

Nutrition is essential in critically ill patients, but translating caloric prescriptions into adequate caloric intake remains challenging. Caloric prescriptions (P), effective intake (I), and caloric needs (N), calculated with modified Harris-Benedict formulas, were recorded during seven consecutive days in ventilated patients. Adequacy of prescription was estimated by P/N ratio. I/P ratio assessed accuracy of translating a prescription into administered feeding. I/N ratio compared delivered calories with theoretical caloric needs. Fifty patients were prospectively studied in a mixed medicosurgical ICU in a teaching hospital. Basal and total energy expenditure were, respectively, 1361 ± 171 kcal/d and 1649 ± 233 kcal/d. P and I attained 1536 ± 602 kcal/d and 1424 ± 572 kcal/d, respectively. 24.6% prescriptions were accurate, and 24.3% calories were correctly administered. Excessive calories were prescribed in 35.4% of patients, 27.4% being overfed. Caloric needs were underestimated in 40% prescriptions, with 48.3% patients underfed. Calculating caloric requirements by a modified standard formula covered energy needs in only 25% of long-term mechanically ventilated patients, leaving many over- or underfed. Nutritional imbalance mainly resulted from incorrect prescription. Failure of "simple" calculations to direct caloric prescription in these patients suggests systematic use of more reliable methods, for example, indirect calorimetry.