Resting energy expenditure in malnourished older patients at hospital admission and three months after discharge: predictive equations versus measurements

Association of resting energy expenditure with phase angle in hospitalized older patients: a cross-sectional analysis

BACKGROUND/OBJECTIVES

Resting energy expenditure (REE) constitutes the largest component of total energy expenditure and undergoes an age-related decline that is unexplained by decreased fat-free mass. Phase angle (PhA) is a cellular health indicator that is possibly associated with REE. We investigated the association of REE and PhA in hospitalized older adults.

SUBJECTS/METHODS

This single-center, cross-sectional analysis utilized the baseline data from a prospective longitudinal study and included 131 eligible patients aged ≥70 years. The REE was measured using indirect calorimetry, and PhA and body composition were assessed using bioelectrical impedance. The association between REE, PhA, and body composition was examined, and REE was compared using previously reported PhA cutoff values.

RESULTS

In this cohort with a mean (±standard deviation) age of 87.4 (±7.0) years, 34.4% of the participants were men. REE and PhA correlated strongly (r: 0.562, p < 0.001) and significantly after adjusting for age and sex (r: 0.433, p < 0.001). Multivariate analysis showed a significant independent association between REE and PhA and skeletal muscle mass (standardized β [95% CI]; 28.072 [2.188-53.956], p = 0.035) without any significant interaction between PhA and age on REE. The low PhA group had a significantly lower REE (kcal/day; 890 [856-925] vs. 1077 [1033-1122], p < 0.001), and this remained significant after adjusting for age, sex, and skeletal muscle mass index.

CONCLUSIONS

PhA is associated with REE in older adults. Adjusting REE calculation algorithms based on PhA values and correcting predicted REE according to PhA may aid in determining more accurate energy requirements.

New prediction equations for resting energy expenditure in older hospitalized patients: Development and validation

OBJECTIVES

Accurate resting energy expenditure (REE) prediction is needed to prevent over- or underfeeding in older hospitalized patients. However, few validated REE prediction Equations are known for such patients. Therefore, this study aimed to develop new REE prediction Equations and evaluate their validity.

METHODS

This single-center, cross-sectional study enrolled 134 patients ages ≥70 y. For holdout validation, patients were randomized in a 3:1 ratio; for the development data set, a new Equation was developed according to the measured REE using indirect calorimetry. The new and existing Equations were compared using the validation data set.

RESULTS

Mean patient age was 87.4 ± 6.9 y, and 34.3% were male. Two Equations were developed in multivariable regression models: Equation 1: REE (kcal/day) = 313.582 + Height (cm) × 3.973 + Body weight (kg) × 5.332 - Age (y) × 5.474 - (0 if male; 1 if female) × 20.012 + Calf circumference (cm) × 12.174; and Equation 2: REE (kcal/day) = 594.819 + Height (cm) × 3.760 + Body weight (kg) × 8.888 - Age (y) × 6.298 - (0 if male; 1 if female) × 16.396. The mean relative bias (95% CI) with measured REE as a reference had a small bias for Equations 1 and 2 (-0.1 [-4.1 to 3.9]% and -0.2 [-4.4 to 4.1]%, respectively); however, the Harris-Benedict, Food and Agriculture Organization of the United Nations/World Health Organization/United Nations University, Ganpule, and body weight × 20 Equations had larger biases (-6.2 [-10.3 to -2.0]%; 5.3 [1.3 to 9.3]%; -13.9 [-18.6 to -9.3]%; and -11.6 [-16.1 to -7.1]%, respectively).

CONCLUSIONS

New prediction Equations using height, body weight, age, sex, and calf circumference improve REE prediction accuracy in older hospitalized patients.

Too low protein and energy intake in nursing homes residents

OBJECTIVES

An optimal diet contributes to reducing malnutrition prevalence in the nursing home population. For this population, recommended daily intakes are ≥1.0 g protein/kg body weight and ≥27 energy kcal/kg body weight. The aim of this study was to identify the protein and energy intake of nursing home residents and to assess groups at increased risk for low intake.

METHODS

Cross-sectional data regarding food intake was collected by three-day observations in 189 residents (aged ≥65 y, mean age 85.0 y) of five different nursing homes. Linear mixed models were used to examine associations of protein and energy intake as dependent variables with demographic and disease-related problems as determinants. Results were adjusted for age, sex, and mobility levels and stratified by a protein/energy-enriched diet (P/E+).

RESULTS

The daily protein intake of the residents was 0.80 (SD 0.22) g/kg body weight, with 84.7% having an intake below the recommended daily 1 g/kg body weight. Mean daily energy intake was 20.7 (SD 6.1) kcal/kg body weight, with 85.2% having an intake below recommendation. Protein/energy intake was higher in the P/E+ group compared with standard diet: 0.92 (SD 0.23) versus 0.74 (SD 0.19) g/kg body weight, and 23.9 (SD 6.1) versus 19.1 (SD 5.4) kcal/kg body weight, respectively. The oldest age groups (>85 y), chair-bound residents, women, and residents having difficulties with chewing, dysphagia, a reported decreased food intake, or a decreased appetite were at a higher risk for a low protein/energy intake.

CONCLUSION

Nearly all nursing home residents were at increased risk for not meeting the minimum protein/energy requirements. Intakes should, on average, be increased with ≥15 g protein and ≥520 kcal to reach the minimum intake targets. Although using a P/E+ diet was associated with higher intakes, even these residents had intakes below the requirements.

Resting Energy Expenditure in the Critically Ill and Healthy Elderly-A Retrospective Matched Cohort Study

The use of indirect calorimetry to measure resting energy expenditure (mREE) is widely recommended as opposed to calculating REE (cREE) by predictive equations (PE). The aim of this study was to compare mREE with cREE in critically ill, mechanically ventilated patients aged ≥ 75 years and a healthy control group matched by age, gender and body mass index. The primary outcome was the PE accuracy rate of mREE/cREE, derived using Bland Altman plots. Secondary analyses included linear regression analyses for determinants of intraindividual mREE/cREE differences in the critically ill and interindividual mREE differences in the matched healthy cohort. In this retrospective study, 90 critically ill patients (median age 80 years) and 58 matched healthy persons were included. Median mREE was significantly higher in the critically ill (1457 kcal/d) versus the healthy cohort (1351 kcal/d), with low PE accuracy rates (21% to 49%). Independent predictors of mREE/cREE differences in the critically ill were body temperature, heart rate, FiO₂, hematocrit, serum sodium and urea. Body temperature, respiratory rate, and FiO₂ were independent predictors of interindividual mREE differences (critically ill versus healthy control). In conclusion, the commonly used PE in the elderly critically ill are inaccurate. Respiratory, metabolic and energy homeostasis variables may explain intraindividual mREE/cREE as well as interindividual mREE differences.

Nutrition Intervention Informed by Indirect Calorimetry Compared to Predictive Equations to Achieve Weight Goals in Geriatric Rehabilitation Inpatients: The NEED Study

OBJECTIVES

To assess if nutritional interventions informed by indirect calorimetry (IC), compared to predictive equations, show greater improvements in achieving weight goals, muscle mass, strength, physical and functional performance.

DESIGN

Quasi-experimental study.

SETTING AND PARTICIPANTS

Geriatric rehabilitation inpatients referred to dietitian.

INTERVENTION AND MEASUREMENTS

Patients were allocated based on admission ward to either the IC or equation (EQ) group. Measured resting metabolic rate (RMR) by IC was communicated to the treating dietitian for the IC group but concealed for the EQ group. Achieving weight goals was determined by comparing individualised weight goals with weight changes from inclusion to discharge (weight gain/loss: >2% change, maintenance: ≤2%). Muscle mass, strength, physical and functional performance were assessed at admission and discharge. Food intake was assessed twice over three-days at inclusion and before discharge using plate waste observation.

RESULTS

Fifty-three patients were included (IC n=22; EQ n=31; age: 84.3±8.4 years). The measured RMR was lower than the estimated RMR within both groups [mean difference IC -282 (95%CI -490;-203), EQ -273 (-381;-42) kcal/day)] and comparable between-groups (median IC 1271 [interquartile range 1111;1446] versus EQ 1302 [1135;1397] kcal/day, p=0.800). Energy targets in the IC group were lower than the EQ group [mean difference -317 (95%CI -479;-155) kcal/day]. There were no between-group differences in energy intake, achieving weight goals, changes in muscle mass, strength, physical and functional performance.

CONCLUSIONS

In geriatric rehabilitation inpatients, nutritional interventions informed by IC compared to predictive equations showed no greater improvement in achieving weight goals, muscle mass, strength, physical and functional performance. IC facilitates more accurate determination of energy targets in this population. However, evidence for the potential benefits of its use in nutrition interventions was limited by a lack of agreement between patients' energy intake and energy targets.

Resting Energy Expenditure in Older Inpatients: A Comparison of Prediction Equations and Measurements

Determining energy requirements are an important component of nutritional support for patients with malnutrition; however, the validity of prediction equations for resting energy expenditure (REE) is disputed in older hospitalized patients. We aimed to assess the validity of these equations in older hospitalized patients in Japan. This was a single-center, cross-sectional study of 100 patients aged ≥70 years, hospitalized between January 2020 and December 2021. REE was measured using an indirect calorimeter and was compared to the predicted values calculated from five REE prediction equations. The mean (95% confidence interval) measured REE was 968.1 (931.0, 1005.3) kcal/day, and the mean predicted REE was higher for the FAO/WHO/UNU (1014.3 [987.1, 1041.6] kcal/day, p = 0.164) and Schofield (1066.0 [1045.8, 1086.2] kcal/day, p < 0.001) equations and lower for the Harris-Benedict (898.6 [873.1, 924.1] kcal/day, p = 0.011), Ganpule (830.1 [790.3, 869.9] kcal/day, p < 0.001), and body weight (kg) × 20 (857.7 [821.9, 893.5] kcal/day, p < 0.001) equations. In the age group analysis, none of the predicted values were within a 10% error for more than 80% of patients aged 70−89 years and ≥90 years. The five REE prediction equations did not provide accurate estimates. Validated REE prediction equations need to be developed for older hospitalized patients.

For you were hungry and I gave you food: The prevalence and treatment of malnutrition in patients with acute hip fracture

Hip fractures are a common clinical problem with substantial morbidity and difficult recovery. Preexisting malnutrition has been shown to be common in those patients and a substantial risk factor for poorer outcomes, increased length of stay, and mortality. Additionally, great variability exists in reported malnutrition prevalence rates owing to the variety of nutrition screening and assessment tools utilized globally to identify malnutrition in these patients. Although previous data and a recent Cochrane review have not proven the value of nutrition supplements, we highlight several articles that show an opportunity for improvement in the nutrition care of patients sustaining hip fractures along with a multicenter randomized trial demonstrating the value of carefully designed nutrition intervention. There is also evidence that malnourished patients with hip fracture are still being undertreated. Current guidelines are reviewed, and continued efforts on the part of the clinicians to follow the guidelines as well as lowering the barriers to high-quality nutrition research is discussed.

Resting metabolic rate and its adjustments as predictors of risk protein-energy wasting in hemodialysis patients

Background: The purpose of the present study was to explore the association between resting metabolic rate (RMR) and protein-energy wasting (PEW) risk in Chinese hemodialysis patients by age and gender subgroup.

Methods: RMR and body composition (body cell mass (BCM) and fat mass) of 774 patients undergoing hemodialysis were estimated by bio-electrical impedance analysis (BIA). Anthropometric data were collected by a standard measurement protocol, and the upper arm muscle circumference (AMC) was calculated. Biochemical nutritional and dialysis parameters were obtained. Linear regression analysis was used to analyze the relationship among RMR, body composition and nutritional factors.

Results: The mean age was 54.96 ± 15.78 years. RMR level in patients was 1463.0 (1240.5, 1669.0) kcal/d. In multiple linear regression models, BCM, left calf circumference (LCC), fat mass were the determinants association with RMR (P<0.001). Among the patients in the sample, 133 (17.2%) had been diagnosed with PEW per International Society of Renal Nutrition and Metabolism (ISRNM) criteria and 363 (46.9%) were being at risk PEW. The area under the receiver-operating characteristic curve (AUC) of RMR for predicting risk PEW was greater than RMR/BCM and RMR/body surface area (BSA). When the cutoff of RMR was 1481 kcal/d it had the higher sensitivity and specificity (82 and 42%), and the AUC was 0.68 in elderly maintenance hemodialysis (MHD) patients (P<0.001). After adjustment for potential confounders, lowest RMR quartile level (<1239) increased the risk of PEW (OR = 4.71, 95% CI: 1.33–16.64, P=0.016) in all patients.

Conclusions: Older patients with PEW have a lower RMR reduction. RMR and RMR/BCM may play the role in objective screening to detect risk PEW in MHD patients, especially in males.

Point-Counterpoint: Indirect Calorimetry Is not Necessary for Optimal Nutrition Therapy in Critical Illness

Clinicians have widely recognized that indirect calorimetry (IC) is the "gold standard" for measuring energy expenditure (EE) and thus would intuitively anticipate that its use would be needed to provide optimal nutrition support in critical illness. Recent studies in the literature as well as dramatic changes in clinical practice over the past decade, though, would suggest that such a precise measure by IC to set energy goals is not required to maximize clinical benefit from early feeding in the intensive care unit (ICU). Results from randomized controlled trials evaluating permissive underfeeding, use of supplemental parenteral nutrition to achieve tight calorie control, and caloric density of formulas to increase energy delivery have provided an important perspective on 3 pertinent issues. First, a simple weight-based predictive equation (25 kcal/kg/day) provides a clinically useful approximation of EE. Second, a precise measure of EE by IC does not appear to improve outcomes compared with use of this less accurate estimation of energy requirements. And third, providing some percentage of requirements (50%-80%), achieves similar clinical benefit to full feeding (100%) in the early phases of critical illness. The value from IC use lies in the determination of caloric requirements in conditions for which weight-based equations are rendered inaccurate (anasarca, amputation, severe obesity) or the clinical state is markedly altered (such as the prolonged hyperinflammatory state of coronavirus disease 2019 [COVID-19]). In most other circumstances, routine use of IC would not be expected to change clinical outcomes from early nutrition therapy in the ICU.

Resting Energy Expenditure in the Elderly: Systematic Review and Comparison of Equations in an Experimental Population

Elderly patients are at risk of malnutrition and need an appropriate assessment of energy requirements. Predictive equations are widely used to estimate resting energy expenditure (REE). In the study, we conducted a systematic review of REE predictive equations in the elderly population and compared them in an experimental population. Studies involving subjects older than 65 years of age that evaluated the performance of a predictive equation vs. a gold standard were included. The retrieved equations were then tested on a sample of 88 elderly subjects enrolled in an Italian nursing home to evaluate the agreement among the estimated REEs. The agreement was assessed using the intraclass correlation coefficient (ICC). A web application, equationer, was developed to calculate all the estimated REEs according to the available variables. The review identified 68 studies (210 different equations). The agreement among the equations in our sample was higher for equations with fewer parameters, especially those that included body weight, ICC = 0.75 (95% CI = 0.69-0.81). There is great heterogeneity among REE estimates. Such differences should be considered and evaluated when estimates are applied to particularly fragile populations since the results have the potential to impact the patient's overall clinical outcome.

The use of a portable metabolic monitoring device for measuring RMR in healthy adults

Objective measurement of RMR may be important for optimal nutritional care but is hindered by the price and practicality of the metabolic monitoring device. This study compared two metabolic monitoring devices for measuring RMR and VO2 and compared the measured RMR with the predicted RMR calculated from equations. RMR was measured using QUARK RMR (reference device) and Fitmate GS (COSMED) in a random order for 30 min, each on fasted participants. In total, sixty-eight adults participated (median age 22 years, interquartile range 21-32). Pearson correlation showed that RMR (r 0·86) and VO2 (r 0·86) were highly correlated between the two devices (P < 0·05). Intraclass correlation coefficients (ICC) showed good relative agreements regarding RMR (ICC = 0·84) and VO2 (ICC = 0·84) (P < 0·05). RMR measured by QUARK RMR was significantly higher (649 (sd 753) kJ/d) than Fitmate GS. Equations significantly overpredicted RMR. Accurate RMR (i.e. within ±10 % of the RMR measured by QUARK RMR) was found among 38 % of the participants for Fitmate GS and among 46-68 % depending on the equations. Bland-Altman analysis showed a low absolute agreement with QUARK RMR at an individual level for both Fitmate GS (limits of agreement (LOA): -828 to +2125 kJ/d) and equations (LOA ranged from -1979 to +1879 kJ/d). In conclusion, both Fitmate GS and predictive equations had low absolute agreements with QUARK RMR at an individual level. Therefore, these limitations should be considered when determining RMR using Fitmate GS or equations.

Measured and Predicted Resting Energy Expenditure in Malnourished Older Hospitalized Patients: A Cross-Sectional and Longitudinal Comparison

A number of equations have been proposed to predict resting energy expenditure (REE). The role of nutritional status in the accuracy and validity of the REE predicted in older patients has been paid less attention. We aimed to compare REE measured by indirect calorimetry (IC) and REE predicted by the Harris–Benedict formula in malnourished older hospitalized patients. Twenty-three malnourished older patients (age range 67–93 years, 65% women) participated in this prospective longitudinal observational study. Malnutrition was defined as Mini Nutritional Assessment Long Form (MNA-SF) score of less than 17. REE was measured (REE_measured) and predicted (REE_predicted) on admission and at discharge. REE_predicted within ±10% of the REE_measured was considered as accuracy. Nutritional support was provided to all malnourished patients during hospitalization. All patients were malnourished with a median MNA-LF score of 14. REE_measured and REE_predicted increased significantly during 2-week nutritional therapy (+212.6 kcal and +19.5 kcal, respectively). Mean REE_predicted (1190.4 kcal) was significantly higher than REE_measured (967.5 kcal) on admission (p < 0.001). This difference disappeared at discharge (p = 0.713). The average REE_predicted exceeded the REE_measured on admission and at discharge by 29% and 11%, respectively. The magnitude of difference between REE_measured and REE_predicted increased along with the degree of malnutrition (r = 0.42, p = 0.042) as deviations ranged from −582 to +310 kcal/day in severe to mildly malnourished patients, respectively. REE_predicted by the Harris–Benedict formula is not accurate in malnourished older hospitalized patients. REE measured by IC is considered precise, but it may not represent the true energy requirements to recover from malnutrition. Therefore, the effect of malnutrition on measured REE must be taken into account when estimating energy needs in these patients.

Clinical determinants of resting metabolic rate in geriatric outpatients

PURPOSE

Accurate estimation of the energy requirements including resting metabolic rate (RMR) is important for optimal nutritional care, yet its clinical determinants are unknown. This study examined the associations between clinical determinants of the Comprehensive Geriatric Assessment (CGA) domains with RMR among geriatric outpatients.

MATERIALS & METHODS

Data were retrieved from cohorts of community-dwelling older adults (n = 84, 54 female) referring to geriatrics outpatient mobility clinics in both Amsterdam, The Netherlands and Melbourne, Australia. Determinants within domains of the CGA included diseases (number, type and severity of diseases, polypharmacy), nutrition (body weight, body mass index, absolute and relative skeletal muscle mass, fat-free mass and fat mass, risk of malnutrition), physical function (handgrip strength, Short Physical Performance Battery, Timed Up & Go), cognition (Mini-Mental State Examination), psychological wellbeing (Geriatric Depression Scale) and blood pressure. RMR was objectively measured using indirect calorimetry with a canopy hood. Association between the clinical determinants with standardized RMR (country and sex-specific z-score) were analysed with linear regression adjusted for age, sex and body weight.

RESULTS

Determinants within the nutritional domain were associated with RMR; body weight showed the strongest association with RMR. Significant associations between determinants within the nutritional domain with RMR disappeared after further adjustment for body weight. None of the other domains were associated with RMR.

CONCLUSIONS

Body weight is the strongest clinical determinant of RMR and should be taken into account when estimating RMR in geriatric care.

Measurement Methods for Physical Activity and Energy Expenditure: a Review

Physical activity is defined as any bodily movement produced by skeletal muscles that results in energy expenditure. The benefits of physical activity for health maintenance have been well documented, especially in the prevention and management of chronic diseases. Therefore, accurate measurement of physical activity and energy expenditure is essential both for epidemiological studies and in the clinical context. Given the large number of available methods, it is important to have an understanding of each, especially when one needs to choose a technique to use. The purpose of this review was to discuss the components of total energy expenditure and present advantage and limitations of different methods of physical activity and energy expenditure assessment.

Considerations When Using Predictive Equations to Estimate Energy Needs Among Older, Hospitalized Patients: A Narrative Review

The aim of this narrative review was to summarize the accuracy of predictive equations used to estimate energy expenditure in older, hospitalized adults. More than 50% of patients admitted to intensive care units are older adults. Currently accepted prediction equations used to determine energy intake in the older, hospitalized patient were not specifically developed for the aging population. Rates of multimorbidity, polypharmacy and malnutrition, conditions that influence energy expenditure, are higher in older adults compared to younger adults. For these reasons, current equations may not accurately assess energy needs in this population. As the evidence demonstrating the importance of nutritional supplementation in older, hospitalized adults grows, more accurate energy assessment methods that account for age-related conditions are needed to predict nutritional requirements.

Predicting resting energy expenditure in underweight, normal weight, overweight, and obese adult hospital patients

Background

When indirect calorimetry is not available, predictive equations are used to estimate resing energy expenditure (REE). There is no consensus about which equation to use in hospitalized patients. The objective of this study is to examine the validity of REE predictive equations for underweight, normal weight, overweight, and obese inpatients and outpatients by comparison with indirect calorimetry.

Methods

Equations were included when based on weight, height, age, and/or gender. REE was measured with indirect calorimetry. A prediction between 90 and 110% of the measured REE was considered accurate. The bias and root-mean-square error (RMSE) were used to evaluate how well the equations fitted the REE measurement. Subgroup analysis was performed for BMI. A new equation was developed based on regression analysis and tested.

Results

513 general hospital patients were included, (253 F, 260 M), 237 inpatients and 276 outpatients. Fifteen predictive equations were used. The most used fixed factors (25 kcal/kg/day, 30 kcal/kg/day and 2000 kcal for female and 2500 kcal for male) were added. The percentage of accurate predicted REE was low in all equations, ranging from 8 to 49%. Overall the new equation performed equal to the best performing Korth equation and slightly better than the well-known WHO equation based on weight and height (49% vs 45% accurate). Categorized by BMI subgroups, the new equation, Korth and the WHO equation based on weight and height performed best in all categories except from the obese subgroup. The original Harris and Benedict (HB) equation was best for obese patients.

Conclusions

REE predictive equations are only accurate in about half the patients. The WHO equation is advised up to BMI 30, and HB equation is advised for obese (over BMI 30). Measuring REE with indirect calorimetry is preferred, and should be used when available and feasible in order to optimize nutritional support in hospital inpatients and outpatients with different degrees of malnutrition.

Resting Energy Expenditure of Black Overweight Women in South Africa Is Lower than of White Women

BACKGROUND

Overweight affects 65% of black South African women. Effective weight management requires accurate measurement or estimation of energy expenditure.

AIMS

The study aimed to determine, among overweight women, whether measured resting energy expenditure (REE) differs between black and white participants, and the performance of REE estimation equations.

METHODS

The REE of 44 black (age 39.6 ± 9.7 years, body mass index (BMI) 35.1 ± 6.2 kg/m2) and 41 white (age 38.0 ± 11.6 years, BMI 33.9 ± 7.6 kg/m2) women was measured with indirect calorimetry and estimated with equations. Body composition was assessed with multi-frequency bioelectrical impedance analysis. Differences in REE were determined with t tests (Welch), and included adjustment for fat free mass (FFM) and BMI, and for FFM index (FFMI).

RESULTS

Measured REE was 585 kJ/day (95% CI 264-905; p = 0.0005) and 861 kJ/day (95% CI 499-1,221; p < 0.0001) lower in black than in white women when adjusted for FFM and BMI, and FFMI, respectively. Out of 14 equations, 13 underestimated REE (error range 2,261 ± 727 kJ/day (Bernstein equation, white women) to 8 ± 782 kJ/day (BMI equation, black women)).

CONCLUSIONS

Black overweight women have significantly lower REE than their white counterparts. No tested estimation equation provided satisfactory results across race/ethnicity. REE measurements or development of overweight- or race/ethnicity-specific estimation equations are recommended.

Nitrogen Balance and Protein Requirements for Critically Ill Older Patients

Critically ill older patients with sarcopenia experience greater morbidity and mortality than younger patients. It is anticipated that unabated protein catabolism would be detrimental for the critically ill older patient. Healthy older subjects experience a diminished response to protein supplementation when compared to their younger counterparts, but this anabolic resistance can be overcome by increasing protein intake. Preliminary evidence suggests that older patients may respond differently to protein intake than younger patients during critical illness as well. If sufficient protein intake is given, older patients can achieve a similar nitrogen accretion response as younger patients even during critical illness. However, there is concern among some clinicians that increasing protein intake in older patients during critical illness may lead to azotemia due to decreased renal functional reserve which may augment the propensity towards worsened renal function and worsened clinical outcomes. Current evidence regarding protein requirements, nitrogen balance, ureagenesis, and clinical outcomes during nutritional therapy for critically ill older patients is reviewed.

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.

Comparison of the GEM and the ECAL indirect calorimeters against the Deltatrac for measures of RMR and diet-induced thermogenesis

The Deltatrac™ II Metabolic Monitor (Datex-Ohmeda Inc.) is considered the standard reference machine in indirect calorimetry; however, it is no longer commercially available thus there is a need for new machines. The gas exchange measurement (GEM; GEM Nutrition Ltd) and the ECAL (Health Professional Solutions) are alternative measuring systems. The aim of this study was to compare the ECAL and GEM with Deltatrac for measures of RMR and the GEM to the Deltatrac for measures of diet-induced thermogenesis (DIT). Twenty healthy participants were tested on test day 1 (T1) and test day 2 (T2). RMR was measured in a randomised order for 30 min on the Deltatrac, the GEM and the ECAL. Following this, a 1553 kJ meal was consumed and DIT was measured on the Deltatrac and the GEM in alternating 15 min intervals for 4 h. The GEM reported consistently higher values than the Deltatrac for VO2, VCO2, RMR and fat oxidation (P < 0·005). The ECAL was significantly higher than the Deltatrac for measures of VO2, RMR, carbohydrate oxidation (T2) and respiratory quotient and fat oxidation (T1, T2) (P < 0·05). There were no significant differences within repeated RMR measures on the ECAL, the GEM or the Deltatrac. DIT measures were consistently higher on the GEM (T1) (P < 0·005); however, there were no significant differences between repeated measures. The findings suggest that while the GEM and the ECAL were not accurate alternatives to the Deltatrac, they may be reliable for repeated measures.

Validity of predictive equations for resting energy expenditure according to the body mass index in a population of 1726 patients followed in a Nutrition Unit

BACKGROUND & AIMS

The resting energy expenditure (REE) predictive formulas are often used in clinical practice to adapt the nutritional intake of patients or to compare to REE measured by indirect calorimetry. We aimed to evaluate which predictive equations was the best alternative to REE measurements according to the BMI.

METHODS

28 REE prediction equations were studied in a population of 1726 patients without acute or chronic high-grade inflammatory diseases followed in a Nutrition Unit for malnutrition, eating disorders or obesity. REE was measured by indirect calorimetry for 30 min after a fasting period of 12 h. Some formulas requiring fat mass and free-fat mass, body composition was measured by bioelectrical impedance analysis. The percentage of accurate prediction (±10%/REE measured) and Pearson r correlations were calculated.

RESULTS

Original Harris & Benedict equation provided 73.0% of accurate predictions in normal BMI group but only 39.3% and 62.4% in patients with BMI < 16 kg m(-2) and BMI ≥ 40 kg m(-2), respectively. In particularly, this equation overestimated the REE in 51.74% of patients with BMI < 16 kg m(-2). Huang equation involving body composition provided the highest percent of accurate prediction, 42.7% and 66.0% in patients with BMI < 16 and >40 kg m(-2), respectively.

CONCLUSION

Usual predictive equations of REE are not suitable for predicting REE in patients with extreme BMI, in particularly in patients with BMI <16 kg m(-2). Indirect Calorimetry may still be recommended for an accurate assessment of REE in this population until the development of an adapted predictive equation.

A systematic review of malnutrition screening tools for the nursing home setting

RATIONALE

Malnutrition screening among nursing home residents is often performed with tools developed for use among older subjects, and sometimes with tools designed for an adult population. Only a few tools have been designed specifically for the nursing home setting. This systematic review assesses the criterion and predictive validity of malnutrition screening tools used in nursing homes.

METHODS

The databases MEDLINE, CINAHL, and EMBASE were searched on January 30, 2013, for manuscripts including search terms for malnutrition, screening or assessment tools, and setting. Articles were eligible for inclusion if they expressed criterion validity (how well can a tool assess nutritional status) or predictive validity (how well can a tool predict clinical outcome) of malnutrition screening tools in a nursing home population. Included were articles that had been published in the English, German, French, Dutch, Spanish, or Portuguese language.

RESULTS

The search yielded 8313 references. Of these, 24 met the inclusion criteria and were available; 2 extra manuscripts were retrieved by reference checking. Twenty tools were identified. Seventeen studies reported on criterion validity, and 9 on predictive validity. Four of the tools had been designed specifically for use in long term care. None of the tools, not even the ones specifically designed for the nursing home setting, performed (on average) better than "fair" in either assessing the residents' nutritional status or in predicting malnutrition-related outcomes.

CONCLUSION

The use of existing screening tools for the nursing home population carries limitations, as none performs better than "fair" in assessing nutritional status or in predicting outcome. Also, no superior tool can be pointed out. This systematic review implies that further considerations regarding malnutrition screening among nursing home residents are required.

Influence of aging on nitrogen accretion during critical illness

BACKGROUND

Aging adversely affects nitrogen accretion during health, but its effect during critical illness is unknown. Nitrogen balance (NB) response to varying protein intakes was compared between critically ill, older and younger patients.

METHODS

Adult patients admitted to the trauma intensive care unit, given enteral or parenteral nutrition, and who had a NB determination within 5-14 days after injury were evaluated. Patients with renal or hepatic disease were excluded. Patients were categorized as older (≥60 years) or younger (18-59 years of age). Data are given as mean ± SD or median [interquartile range].

RESULTS

Fifty-four older (69 [65, 77] years) and 195 younger (35 [27, 47] years) patients were evaluated. NB was blunted for the older patients with an observed trending improvement in NB from -13.5 ± 5.5 to -5.6 ± 8.8 g/d (P = NS) noted at 1.5-1.99 g/kg/d. NB improved from -22.2 ± 8.2 to -11.8 ± 9.9 g/d (P = .05) at 1-1.49 g/kg/d and modestly thereafter for each 0.5-g/kg/d increase in protein intake for the younger patients. Serum urea nitrogen concentration during the NB was highly variable but overall greater for the older patients (20 [14, 33] vs 15 [10, 20] mg/dL, P = .001).

CONCLUSIONS

Improvement in nitrogen accretion was blunted at lower protein intakes in critically ill, older patients compared with younger patients. Individualization of protein intake is warranted.

Comparison of three indirect calorimetry devices and three methods of gas collection: a prospective observational study

BACKGROUND & AIMS

Indirect calorimetry was performed for a long time with the DeltatracII(®) device (Datex, Finland), considered as a reference but no longer produced. This study aims at comparing the energy expenditure (EE), the volume of oxygen (VO2) and carbon dioxide (VCO2) measured by two new available indirect calorimeters, the QuarkRMR(®) (Cosmed, Italy) and the CCMexpress(®) (MedGraphic,USA), using three different methods of gas collection, with the DeltatracII(®) in healthy subjects.

METHODS

Twenty-four healthy subjects (15 women and 9 men, age 53 ± 15 yrs, mean BMI 25.5 ± 7.1 kg/m(2)) underwent measurements of EE with DeltatracII(®) using canopy, QuarkRMR(®) using canopy and CCMexpress(®) using canopy, face tent and facemask. All measurements were performed for 10 min in random order. Results are presented as mean ± SD and compared by linear regression, repeated measure one-way ANOVA with Bonferroni's post hoc test and Bland & Altman test.

RESULTS

EE was 1630 ± 340 kcal for DeltatracII(®) and 1607 ± 307 kcal, 1741 ± 360 kcal, 1666 ± 315 and 1626 ± 336 kcal for QuarkRMR(®) and CCMexpress(®) with canopy, face tent and facemask, respectively (p = 0.001). Compared to DeltatracII(®), Bland & Altman test showed a mean EE difference (2SD) of 24(220)kcal for QuarkRMR(®), and -111(260) kcal, -36(304) kcal, 5(402) kcal for CCMexpress(®) with canopy, face tent and facemask, respectively. There was no systematic over- or underestimation with any device or gas collection method.

CONCLUSION

Mean EE was similar between QuarkRMR(®) and DeltatracII(®) but not between CCMexpress(®), in any mode of gas collection, and DeltatracII(®). Bland & Altman test shows a large variability in EE differences with both devices compared to DeltatracII(®), highlighting the need for refining their accuracy.

The use of indirect calorimetry in the intensive care unit

PURPOSE OF REVIEW

This review evaluates whether improvements have occurred in the value of predictive equations for use in designing nutritional therapeutic regimens in the ICU. The report also seeks to determine whether emerging strategies for nutrition therapy in the ICU change the need for an accurate measurement of energy requirements by indirect calorimetry.

RECENT FINDINGS

Predictive equations remain problematic for use in the critically ill patient. Inaccuracy of predictive equations introduces error in the design of a nutritional therapy regimen. The epidemic of obesity renders the calculations of requirements by predictive equations increasingly inaccurate at extremes of BMI. Certain patient populations appear to be hypometabolic, contradicting the traditional notion that critical illness increases energy expenditure. More recent data indicates that determination of which patients benefit from nutritional therapy may be based both on assessment of nutrition risk and delivery of sufficient nutrition therapy.

SUMMARY

The role of indirect calorimetry in the ICU should be expected to increase in the near future, as predictive equations may be too inaccurate to identify the appropriate goals of nutrition therapy.