Measured versus estimated energy expenditure in mechanically ventilated critically ill patients

A Proposed Predictive Equation for Energy Expenditure Estimation Among Noncritically Ill Patients With Acute Kidney Injury

OBJECTIVE

The incidence of acute kidney injury (AKI) is identified more frequently in noncritical compared with intensive care settings. The prognosis of malnourished AKI patients is far worse than those with normal nutritional status. However, a method for estimating the optimal amount of energy required to guide nutritional support among noncritically ill AKI patients is yet to be determined.

METHODS

We evaluated the performance of weight-based formulas (20-30 kcal/kg/day) with the reference values of energy expenditure (EE) measured by indirect calorimetry (IC) among noncritically ill AKI patients during hospitalization. The statistics for assessing agreement, including total deviation index and accuracy within 10% represent the percentage of estimations falling within the IC value range of ±10%, were tested. Parameters for predicting the EE equation were also developed using a regression analysis model.

RESULTS

A total of 40 noncritically ill AKI patients were recruited. The mean age of participants was 62.5 ± 16.5 years with 50% being male. The average IC-derived EE was 1,124.6 ± 278.9 kcal/day with respiratory quotients 0.8-1.3, indicating good validity of the IC test. Receiving dialysis, protein catabolic rate, and age was not significantly associated with measured EE. Nearly all weight-based formulas overestimated measured EE. The magnitude of total deviation index values was broad with the proportion of patients achieving an accuracy of 10% being as low as 20%. The proposed equation to predict EE derived from this study was EE (kcal/day) = 618.27 + (8.98 x weight in kg) + 137.0 if diabetes - 199.7 if female (r2 = 0.68, P < .001). In the validation study with an independent group of noncritically ill AKI patients, predicted EE using the newly derived equation was also significantly correlated with measured EE by IC (r = 0.69, P = .004).

CONCLUSION

Estimation of EE by weight-based formulas usually overestimated measured EE among noncritically ill AKI patients. In the absence of IC, the proposed predictive equation, specifically for noncritically ill AKI patients might be useful, in addition to weight-based formulas, for guiding caloric dosing in clinical practice.

Nutrition support for acute kidney injury 2020-consensus of the Taiwan AKI task force

BACKGROUND

We used evidence-based medicine to suggest guidelines of nutritional support for Taiwanese patients with acute kidney injury (AKI).

METHODS

Our panel reviewed the medical literature in group meetings to reach a consensus on answering clinical questions related to the effects of the nutritional status, energy/protein intake recommendations, timing of enteral, and parenteral nutrition supplementation.

RESULTS

Markers of the nutritional status of serum albumin, protein intake, and nitrogen balance had positive relationships with low mortality. A forest plot of the comparison of mortality between a body mass index (BMI) of <18.5 and ≥18.5 kg/m2 was produced using data from seven observational studies which showed that a lower BMI was associated with higher mortality. The energy recommendation of 20-30 kcal/kg body weight (BW)/day was determined to be valid for all stages of AKI. The protein recommendation for noncatabolic AKI patients is 0.8-1.0 g/kg BW/day, and 1.2-2.0 g/kg BW/day is the same as that for the underlying disease that is causing AKI. Protein intake should be at least 1.5 g/kg BW/day and up to 2.5 g/kg BW/day in patients receiving continuous renal replacement therapy. Considering that patients with AKI often have other critical comorbid situations, early enteral nutrition (EN) is suggested, and parenteral nutrition is needed when >60% energy and protein requirements cannot be met via the enteral route in 7-10 days. Low energy intake is suggested in critically ill patients with AKI, which should gradually be increased to meet 80%-100% of the energy target.

CONCLUSION

By examining evidence-based research, we provide practicable nutritional guidelines for AKI patients.

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

OBJECTIVES

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

DESIGN

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

SETTING

General Intensive Care Unit, University of Malaya Medical Centre.

PATIENTS

Mechanically ventilated critically ill patients.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

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

CONCLUSIONS

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

Comparison of Energy Expenditure in Mechanically Ventilated Septic Shock Patients in Acute and Recovery Periods via Indirect Calorimetry

BACKGROUND

Nutrition in intensive care units (ICUs) affects morbidity and mortality. We aimed to evaluate the energy expenditure of mechanically ventilated patients in early and late septic shock periods.

METHODS

This study retrospectively evaluated 28 mechanically ventilated septic shock patients (11 female/17 male) in a medical ICU. Indirect calorimetry (IC) measurement was performed for 24 hours during the acute and recovery periods of septic shock. The energy values calculated by Harris-Benedict equation (predicted resting energy expenditure [PREE]), measured by IC (measured resting energy expenditure [MREE]), and given to each patient were obtained in the acute and recovery periods.

RESULTS

The mean age was 67.46 ± 14.92 (36-91) years. The MREE was 2741.1 ± 706.3 kcal/d (38.61 ± 11.44 kcal/kg/d) and 2332.8 ± 426.6 kcal/d (32.65 ± 7.8 kcal/kg/d) in the acute and recovery periods, respectively, and showed significant differences (P = 0.001). The patients' energy intake was 1152.7 ± 207.1 kcal/d and 1542.7 ± 433.3 kcal/d in the acute and recovery periods, respectively. A significant difference existed between energy intake and MREE during the acute and recovery periods (P < 0.001 for both).

CONCLUSION

Our findings showed that energy expenditure increases in septic shock. Significant differences existed between MREE, PREE, and energy intake, which were not correlated. The MREE was higher in the acute period. Despite the increasing energy requirement, the PREE and energy intake were well below MREE. For better clinical outcomes, each patient's energy expenditure must be closely monitored and evaluated using intermittent IC measurements.

Graphical Calculation of Estimated Energy Expenditure in Burn Patients

BACKGROUND

Historically, estimated energy expenditure (EEE) has been related to the percent of body surface area burned. Subsequent evaluations of these estimates have indicated that the earlier formulas may overestimate the amount of caloric support necessary for burn-injured patients. Ireton-Jones et al derived 2 equations for determining the EEE required to support burn patients, 1 for ventilator-dependent patients and 1 for spontaneously breathing patients. Evidence has proved their reliability, but they remain challenging to apply in a clinical setting given the difficult and cumbersome mathematics involved. This study aims to introduce a graphical calculation of EEE in burn patients that can be easily used in the clinical setting.

METHODS

The multivariant linear regression analysis from Ireton-Jones et al yielded equations that were rearranged into the form of a simple linear equation of the type y = mx + b. By choosing an energy expenditure and the age of the subject, the weight was calculated. The endpoints were then calculated, and a graph was mapped by means of Adobe FrameMaker.

RESULTS

A graphical representation of Ireton-Jones et al's equations was obtained by plotting the weight (kg) on the y axis, the age (years) on the x axis, and a series of parallel lines representing the EEE in burn patients. The EEE has been displayed graphically on a grid to allow rapid determination of the EEE needed for a given patient of a designated weight and age. Two graphs were plotted: 1 for ventilator-dependent patients and 1 for spontaneously breathing patients. Correction factors for sex, the presence of additional trauma, and obesity are indicated on the graphical calculators.

CONCLUSIONS

We propose a graphical tool to calculate caloric requirements in a fast, easy, and portable manner.

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

BACKGROUND & AIMS

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Resting energy expenditure in critically ill patients: Evaluation methods and clinical applications

Patients on intensive care present systemic, metabolic, and hormonal alterations that may adversely affect their nutritional condition and lead to fast and important depletion of lean mass and malnutrition. Several factors and medical conditions can influence the energy expenditure (EE) of critically ill patients, such as age, gender, surgery, serious infections, medications, ventilation modality, and organ dysfunction. Clinical conditions that can present with EE change include acute kidney injury, a complex disorder commonly seen in critically ill patients with manifestations that can range from minimum elevations in serum creatinine to renal failure requiring dialysis. The nutritional needs of this population are therefore complex, and determining the resting energy expenditure is essential to adjust the nutritional supply and to plan a proper diet, ensuring that energy requirements are met and avoiding complications associated with overfeeding and underfeeding. Several evaluation methods of EE in this population have been described, but all of them have limitations. Such methods include direct calorimetry, doubly labeled water, indirect calorimetry (IC), various predictive equations, and, more recently, the rule of thumb (kcal/kg of body weight). Currently, IC is considered the gold standard.

Energy Expenditure and Protein Requirements After Traumatic Injury

Traumatic injury induces hypermetabolism. The degree of hypermetabolism can be variable, depending on the type of injury, the degree of inflammation, body composition, age, and treatment regimens. To estimate metabolic rate in some types of injury, predictive equations have been published. Some of these equations have been tested in validation studies. For other types of injury, equations do not exist. Some expert panels have recommended measuring in lieu of estimating metabolic rate, though studies have not been performed to determine whether clinical outcome is affected by the method used to determine energy requirements. Traumatically injured patients are usually catabolic, but protein needs after traumatic injury continue to be debated. Some suggest that 1.5 g protein per kg body weight is adequate and that any additional protein is simply oxidized, adding to the nitrogen load to be excreted. Alternately, protein intake >2.0 g/kg body weight increases the absolute rate of body protein synthesis, and achievement of nitrogen balance has been associated with survival. Thus, provision of high-protein feeding to achieve nitrogen balance might be worthwhile, even if that balance is achieved at the cost of additional nitrogen production.

Hypocaloric Feeding of the Critically Ill

During critical illness, the stress response causes accelerated gluconeogenesis and lipolysis, leading to hyperglycemia and elevated serum triglyceride levels. The traditional nutrition support strategy of meeting or exceeding calorie requirements may compound the metabolic alterations of the stress response. Hypocaloric nutrition support has the potential to provide nutrition support without exacerbating the stress response. Studies have shown hypocaloric nutrition support to be safe and to achieve nitrogen balance comparable with traditional regimens. Benefits shown include improved glycemic control, decreased intensive care unit (ICU) length of stay (LOS), and decreased ventilator days and infection rate; however, not all studies have produced identical results. Providing adequate dietary protein has emerged as an important factor in efficacy of the hypocaloric regimen. Although it is inconclusive, currently available research suggests that a nutrition support goal of 10-20 kcal/kg of ideal or adjusted weight and 1.5-2 g/kg ideal weight of protein may be beneficial during the acute stress response. Well-designed, randomized, controlled studies with adequate sample size that evaluate relevant clinical outcomes such as mortality, ICU LOS, and infection while controlling for factors such as glycemic control, severity of illness, incorporation of calories from all sources, in addition to feeding regimens, are needed to definitively determine the effects of hypocaloric nutrition support.

Energy expenditure in critically ill patients estimated by population-based equations, indirect calorimetry and CO2-based indirect calorimetry

Background

Indirect calorimetry (IC) is the reference method for measurement of energy expenditure (EE) in mechanically ventilated critically ill patients. When IC is unavailable, EE can be calculated by predictive equations or by VCO₂-based calorimetry. This study compares the bias, quality and accuracy of these methods.

Methods

EE was determined by IC over a 30-min period in patients from a mixed medical/postsurgical intensive care unit and compared to seven predictive equations and to VCO₂-based calorimetry. The bias was described by the mean difference between predicted EE and IC, the quality by the root mean square error (RMSE) of the difference and the accuracy by the number of patients with estimates within 10 % of IC. Errors of VCO₂-based calorimetry due to choice of respiratory quotient (RQ) were determined by a sensitivity analysis, and errors due to fluctuations in ventilation were explored by a qualitative analysis.

Results

In 18 patients (mean age 61 ± 17 years, five women), EE averaged 2347 kcal/day. All predictive equations were accurate in less than 50 % of the patients with an RMSE ≥ 15 %. VCO₂-based calorimetry was accurate in 89 % of patients, significantly better than all predictive equations, and remained better for any choice of RQ within published range (0.76–0.89). Errors due to fluctuations in ventilation are about equal in IC and VCO₂-based calorimetry, and filtering reduced these errors.

Conclusions

This study confirmed the inaccuracy of predictive equations and established VCO₂-based calorimetry as a more accurate alternative. Both IC and VCO₂-based calorimetry are sensitive to fluctuations in respiration.

Energy Balance in Huntington's Disease

INTRODUCTION

Little is known about the energy needs in Huntington's disease (HD). The aims of this study are to analyze and compare the total energy expenditure (TEE) and energy balance (EB) in a representative sample of HD patients with healthy controls.

METHODS

This is an observational, case-control single-center study. Food caloric energy intake (EI) and TEE were considered for estimating EB. A dietary recall questionnaire was used to assess the EI. TEE was computed as the sum of resting energy expenditure (REE), measured by indirect calorimetry and physical activity (PA) monitored by an actigraph.

RESULTS

A total of 22 patients were included (36% men, mean age 50.3 ± 15.6 years, motor Unified Huntington's Disease Scale 27.9 ± 23.7, total functional capacity 11.0 (7.0-13.0), EI 38.6 ± 10.0 kcal/kg, PA 5.3 (3.0-7.4) kcal/kg, REE 30.9 ± 6.4 kcal/kg, TEE 2,023.4 (1,592.0-2,226.5) kcal/day) and 18 controls (50% men, mean age 47.4 ± 13.8 years, EI 38.6 ± 10.3 kcal/kg, PA 8.4 (5.0-13.8) kcal/kg, REE 30.8 ± 6.6 kcal/kg, TEE 2,281.0 (2,057.3-2,855.3) kcal/day). TEE was significantly lower in patients compared to controls (p = 0.03). PA was lower in patients compared to controls (p = 0.02).

CONCLUSIONS

Although patients with HD appeared to have lower energy expenditure, mainly due to decreased voluntary PA, they were still able to maintain their energy needs with an adequate food intake. © 2015 S. Karger AG, Basel.

Prevalence of Underprescription or Overprescription of Energy Needs in Critically Ill Mechanically Ventilated Adults as Determined by Indirect Calorimetry: A Systematic Literature Review

BACKGROUND

Underfeeding and overfeeding has been associated with adverse patient outcomes. Resting energy expenditure can be measured using indirect calorimetry. In its absence, predictive equations are used. A systematic literature review was conducted to determine the prevalence of underprescription and overprescription of energy needs in adult mechanically ventilated critically ill patients by comparing predictive equations to indirect calorimetry measurements.

METHODS

Ovid MEDLINE, CINAHL Plus, Scopus, and EMBASE databases were searched in May 2013 to identify studies that used both predictive equations and indirect calorimetry to determine energy expenditure. Reference lists of included publications were also searched. The number of predictive equations that underestimated or overestimated energy expenditure by ±10% when compared to indirect calorimetry measurements were noted at both an individual and group level.

RESULTS

In total, 2349 publications were retrieved, with 18 studies included. Of the 160 variations of 13 predictive equations reviewed at a group level, 38% underestimated and 12% overestimated energy expenditure by more than 10%. The remaining 50% of equations estimated energy expenditure to within ±10 of indirect calorimetry measurements. On an individual patient level, predictive equations underestimated and overestimated energy expenditure in 13-90% and 0-88% of patients, respectively. Differences of up to 43% below and 66% above indirect calorimetry values were observed.

CONCLUSIONS

Large discrepancies exist between predictive equation estimates and indirect calorimetry measurements in individuals and groups. Further research is needed to determine the influence of indirect calorimetry and predictive equation limitations in contributing to these observed differences.

Nurse to bed ratio and nutrition support in critically ill patients

BACKGROUND

Inadequate nutrition support is common among critically ill patients, and identification of risk factors for such inadequacy might help in improving nutrition support.

OBJECTIVE

To determine how often daily calorie goals are met and the factors responsible for inadequate nutrition support. Methods A single-center prospective cohort study. Each patient's demographic and clinical characteristics, the need for ventilatory support, the use and dosage of medications, the number of nursing staff per bed, the time elapsed from admission to the intensive care unit until the effective start of enteral feeding, and the causes for nonadministration were recorded. Achievement of daily calorie goals was determined and correlated with risk factors.

RESULTS

A total of 262 daily evaluations were done in 40 patients. Daily calorie goal was achieved in only 46.2% of the evaluations (n = 121), with a mean of 74.8% of the prescribed volume of enteral nutrition infused daily. Risk factors for inadequate nutrition support were the use of midazolam (odds ratio, 1.58; 95% CI, 1.18-2.11) and fewer nursing professionals per bed (odds ratio, 2.56; 95% CI, 1.43-4.57). Conclusion Achievement of daily calorie goals was inadequate, and the main factors associated with this failure were the use and dosage of midazolam and the number of nurses available.

Terapia nutricional enteral em politraumatizados sob ventilação mecânica e oferta energética

OBJETIVO: O objetivo deste estudo foi avaliar a adequação energética dos pacientes politraumatizados em suporte ventilatório internados na unidade de terapia intensiva de um hospital público de Porto Alegre (RS), por meio da comparação entre as calorias prescritas e as efetivamente administradas, assim como entre as calorias estimadas pela equação de Harris-Benedict e a prescrição energética de cada paciente. MÉTODOS: Estudo de coorte prospectivo de pacientes politraumatizados, simultaneamente sob ventilação mecânica e terapia nutricional enteral. Verificou-se o tempo de permanência sob ventilação mecânica e a oferta energética durante o período de terapia nutricional enteral. A associação entre as variáveis quantitativas foi avaliada através do teste de correlação de Spearman devido à assimetria das variáveis. RESULTADOS: Foram acompanhados 60 pacientes, na faixa etária de 18 a 78 anos, sendo 81,7% do sexo masculino. Os tempos medianos de internação hospitalar, permanência na unidade de terapia intensiva e ventilação mecânica foram de 29, 14 e 6 dias, respectivamente. A média do percentual de dieta administrada foi de 68,6% (DP=18,3%). Da amostra total, 16 (26,7%) pacientes receberam no mínimo 80% de suas necessidades diárias. Não houve associação estatisticamente significativa entre o valor energético total administrado e os tempos de ventilação mecânica (r s=0,130; p=0,321), de unidade de terapia intensiva (r s=-0,117; p=0,372) e de internação hospitalar (r s=-0,152; p=0,246). CONCLUSÃO: Os pacientes incluídos neste estudo não receberam com precisão o aporte energético prescrito, ficando expostos aos riscos da desnutrição e seus desfechos clínicos desfavoráveis.

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.

Energy expenditure during weaning from mechanical ventilation: is there any difference between pressure support and T-tube?

BACKGROUND

The objectives of this study were to compare patients' energy expenditure (EE) during pressure support (PS) and T-tube (TT) weaning from mechanical ventilation (MV) through indirect calorimetry (IC) and to crosscheck these findings with the results calculated using Harris-Benedict (HB) equation.

METHODS

This study is a randomized crossover controlled trial. Patients with clinical criteria for weaning from MV were randomized to PS-TT or TT-PS, with EE measurement for 20 minutes in PS and TT through IC. Energy expenditure was estimated through HB equation with and without activity factor. Statistical analysis used the Student t test for paired samples and Pearson correlation coefficient, as well as Bland-Altman method.

RESULTS

Forty patients were included. The mean age and Acute Physiology and Chronic Health Evaluation II score were 56 ± 16 years and 23 ± 8, respectively, with predominance of male patients (70%). Mean EE of patients in TT (1782 ± 375 kcal/d) was 14.4% higher than in PS (1558 ± 304 kcal/d; P < .001). In relation to the EE obtained with the HB equation, the mean (SD) value calculated was 1455 (210) kcal/d, and when considering the activity factor, it was 1609 (236) kcal/d, all of them presenting correlation with the values from IC in PS (r = 0.647) and TT (r = 0.539). However, the limits of agreement between the measured EE and the estimated EE suggest that the HB equation tends to underestimate the EE.

CONCLUSION

Comparison of EE in PS and in TT through IC demonstrated that there is increased EE in the TT mode. The results suggest that the HB equation underestimates the EE of patients in weaning from MV.

A comparison between ventilation modes: how does activity level affect energy expenditure estimates?

BACKGROUND

An appropriate diet is essential to avoid complications of overfeeding or underfeeding in mechanically ventilated intensive care unit (ICU) patients. The paucity of consistent comparative data on energy expenditure for each ventilation mode complicates diet prescription. This study evaluates caloric requirements by comparing estimated and measured energy expenditure values for 2 ventilation modes.

METHODS

The energy expenditure of 100 ICU patients on assisted or controlled mechanical ventilation was measured by indirect calorimetry for 20 minutes. Values were calculated for a 24-hour period and compared with Harris-Benedict estimates multiplied by an injury factor and either multiplied or not by a 10% activity factor.

RESULTS

The mean Harris-Benedict estimate was 1858.87 +/- 488.67 kcal/24 h when multiplied by an injury factor and a 10% activity factor. The mean energy expenditure values measured by indirect calorimetry were 1712.76 +/- 491.95 kcal/24 h for controlled and 1867.33 +/- 542.67 kcal/24 h for assisted ventilation. The mean total energy expenditure for assisted ventilation was 10.71% greater than the mean for controlled ventilation (P < .001). For controlled ventilation, Harris-Benedict results overestimated indirect calorimetry values by 141.10 +/- 10 kcal/24 h (8.2%, P = .012) when multiplied by injury and activity factors, and underestimated values by 44.28 +/- 28 kcal/24 h (2.6%, P = .399) when the equation was calculated without the activity factor. For assisted ventilation, Harris-Benedict results underestimated indirect calorimetry values by 198.84 +/- 84 kcal/24 h (10.7%, P = .001) when not multiplied by the activity factor and by 13.46 kcal/24 h (0.75%) when the activity factor was used, but differences were not statistically significant (P = .829).

CONCLUSIONS

Results suggest that a 10% activity factor should be adopted only for assisted ventilation because multiplication by an activity factor may lead to overfeeding of patients on controlled ventilation.

Validation of a predictive method for an accurate assessment of resting energy expenditure in medical mechanically ventilated patients

OBJECTIVE

Use comparison with indirect calorimetry to confirm the ability of our previously described equation to predict resting energy expenditure in mechanically ventilated patients.

DESIGN

Prospective, validation study.

SETTING

Eighteen-bed, medical intensive care unit at a teaching hospital.

PATIENTS

All adult patients intubated >24 hrs were assessed for eligibility. Exclusion criteria were clinical situations that could contribute to erroneous calorimetric measurements.

INTERVENTIONS

Resting energy expenditure was calculated using the original Harris-Benedict equations and those corrected for usual stress factors, the Swinamer equation, the Fusco equation, the Ireton-Jones equation, and our equation: resting energy expenditure (kcal/day) = 8 x weight (kg) + 14 x height (cm) + 32 x minute ventilation (L/min) + 94 x temperature (degrees C) - 4834.

MEASUREMENTS AND MAIN RESULTS

Resting energy expenditure was measured by indirect calorimetry for the 45 included patients. Resting energy expenditure calculated with our predictive model correlated with the measured resting energy expenditure (r2 = .62, p < .0001), and Bland-Altman analysis showed a mean bias of -192 +/- 277 kcal/day, with limits of agreement ranging from -735 to 351 kcal/day. Resting energy expenditure calculated with the Harris-Benedict equations was more weakly correlated with measured resting energy expenditure (r2 = .41, p < .0001), with Bland-Altman analysis showing a mean bias of 279 +/- 346 kcal/day between them and the limits of agreement ranging from -399 to 957 kcal/day. Applying usual stress-correction factors to the Harris-Benedict equations generated wide variability, and the correlation with measured resting energy expenditure was poorer (r2 = .18, p < .0001), with Bland-Altman analysis showing a mean bias of -357 +/- 750 kcal/day and limits of agreement ranging from -1827 to 1113 kcal/day. The use of the Swinamer, Fusco, or Ireton-Jones predictive methods yielded weaker correlation between calculated and measured resting energy expenditure (r2 = .41, p < .0001; r2 = .38, p < .0001; r2 = .39, p < .0001, respectively) than our equation, and Bland-Altman analysis showed no improvement in agreement and variability between methods.

CONCLUSIONS

The Faisy equation, based on static (height), less stable (weight), and dynamic biometric variables (temperature and minute ventilation), provided precise and unbiased resting energy expenditure estimations in mechanically ventilated patients.

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

Metabolic rate is generally assessed by use of equations in critically ill patients, but evidence pertaining to the validity of these equations in this population has not been systematically evaluated. This paper represents the first such systematic analysis in adult patients. A work group created by the American Dietetic Association identified pertinent peer-reviewed articles. The work group systematically evaluated these articles and formulated conclusion statements and grades based on the available evidence. Seven equations plus the Fick method were found to have validation work that met criteria for inclusion in this analysis. The Harris-Benedict equation with and without modifiers had the most validation work behind it (n=13), followed by Ireton-Jones (1992 and 1997) (n=9), Penn State (1998, 2003) (n=2), and Swinamer (n=1). Five studies pertaining to the Fick method met acceptance criteria. Based on these validation studies, the Harris-Benedict, Ireton-Jones 1997, and Fick methods can be confidently eliminated from use in assessment of energy expenditure in critically ill patients. The Penn State 2003, Swinamer, and Ireton-Jones 1992 equations may be useful in critically ill nonobese patients, whereas the Ireton-Jones 1992 and Penn State 1998 equations seem to be useful in obese patients. The strength of these conclusions is moderated because of limited and sometimes inconsistent data. More validation work is needed to confirm and increase the strength of these conclusions.

Controversies in the determination of energy requirements

To avoid any negative outcomes associated with under- or overfeeding it is essential to estimate nutrient requirements before commencing nutrition support. The energy requirements of an individual vary with current and past nutritional status, clinical condition, physical activity and the goals and likely duration of treatment. The evidence-base for prediction methods in current use, however, is poor and the equations are thus open to misinterpretation. In addition, most methods require an accurate measurement of current weight, which is problematic in some clinical situations. The estimation of energy requirements is so challenging in some conditions, e.g. critical illness, obesity and liver disease, that it is recommended that expenditure be measured on an individual basis by indirect calorimetry. Not only is this technique relatively expensive, but in the clinical setting there are several obstacles that may complicate, and thus affect the accuracy of, any such measurements. A review of relevant disease-specific literature may assist in the determination of energy requirements for some patient groups, but the energy requirements for a number of clinical conditions have yet to be established. Regardless of the method used, estimated energy requirements should be interpreted with care and only used as a starting point. Practitioners should regularly review the patient and reassess requirements to take account of any major changes in clinical condition, nutritional status, activity level and goals of treatment. There is a need for large randomised controlled trials that compare the effects of different levels of feeding on clinical outcomes in different disease states and care settings.

Poor agreement between continuous measurements of energy expenditure and routinely used prediction equations in intensive care unit patients

BACKGROUND & AIMS

A wide variation in 24h energy expenditure has been demonstrated previously in intensive care unit (ICU) patients. The accuracy of equations used to predict energy expenditure in critically ill patients is frequently compared with single or short-duration indirect calorimetry measurements, which may not represent the total energy expenditure (TEE) of these patients. To take into account this variability in energy expenditure, estimates have been compared with continuous indirect calorimetry measurements.

METHODS

Continuous (24h/day for 5 days) indirect calorimetry measurements were made in patients requiring mechanical ventilation for 5 days. The Harris-Benedict, Schofield and Ireton-Jones equations and the American College of Chest Physicians recommendation of 25 kcal/kg/day were used to estimate energy requirements.

RESULTS

A total of 192 days of measurements, in 27 patients, were available for comparison with the different equations. Agreement between the equations and measured values was poor. The Harris-Benedict, Schofield and ACCP equations provided more estimates (66%, 66% and 65%, respectively) within 80% and 110% of TEE values. However, each of these equations would have resulted in clinically significant underfeeding (<80% of TEE) in 16%, 15% and 22% of patients, respectively, and overfeeding (>110% of TEE) in 18%, 19% and 13% of patients, respectively.

CONCLUSIONS

Limits of agreement between the different equations and TEE values were unacceptably wide. Prediction equations may result in significant under or overfeeding in the clinical setting.

Accurate Determination of Energy Needs in Hospitalized Patients

OBJECTIVE

To evaluate the accuracy of seven predictive equations, including the Harris-Benedict and the Mifflin equations, against measured resting energy expenditure (REE) in hospitalized patients, including patients with obesity and critical illness.

DESIGN

A retrospective evaluation using the nutrition support service database of a patient cohort from a similar timeframe as those used to develop the Mifflin equations.

SUBJECTS/SETTING

All patients with an ordered nutrition assessment who underwent indirect calorimetry at our institution over a 1-year period were included.

INTERVENTION

Available data was applied to REE predictive equations, and results were compared to REE measurements.

MAIN OUTCOME MEASURES

Accuracy was defined as predictions within 90% to 110% of the measured REE. Differences >10% or 250 kcal from REE were considered clinically unacceptable.

STATISTICAL ANALYSES PERFORMED

Regression analysis was performed to identify variables that may predict accuracy. Limits-of-agreement analysis was carried out to describe the level of bias for each equation.

RESULTS

A total of 395 patients, mostly white (61%) and African American (36%), were included in this analysis. Mean age+/-standard deviation was 56+/-18 years (range 16 to 92 years) in this group, and mean body mass index was 24+/-5.6 (range 13 to 53). Measured REE was 1,617+/-355 kcal/day for the entire group, 1,790+/-397 kcal/day in the obese group (n=51), and 1,730+/-402 kcal/day in the critically ill group (n=141). The most accurate prediction was the Harris-Benedict equation when a factor of 1.1 was multiplied to the equation (Harris-Benedict 1.1), but only in 61% of all the patients, with significant under- and over-predictions. In the patients with obesity, the Harris-Benedict equation using actual weight was most accurate, but only in 62% of patients; and in the critically ill patients the Harris-Benedict 1.1 was most accurate, but only in 55% of patients. The bias was also lowest with Harris-Benedict 1.1 (mean error -9 kcal/day, range +403 to -421 kcal/day); but errors across all equations were clinically unacceptable.

CONCLUSIONS

No equation accurately predicted REE in most hospitalized patients. Without a reliable predictive equation, only indirect calorimetry will provide accurate assessment of energy needs. Although indirect calorimetry is considered the standard for assessing REE in hospitalized patients, several predictive equations are commonly used in practice. Their accuracy in hospitalized patients has been questioned. This study evaluated several of these equations, and found that even the most accurate equation (the Harris-Benedict 1.1) was inaccurate in 39% of patients and had an unacceptably high error. Without knowing which patient's REE is being accurately predicted, indirect calorimetry may still be necessary in difficult to manage hospitalized patients.

Assessing Nutritional Status in Chronically Critically Ill Adult Patients

• Background Numerous methods are used to measure and assess nutritional status of chronically critically ill patients.

• Objectives To discuss the multiple methods used to assess nutritional status in chronically critically ill patients, describe the nutritional status of chronically critically ill patients, and assess the relationship between nutritional indicators and outcomes of mechanical ventilation.

• Methods A descriptive, longitudinal design was used to collect weekly data on 360 adult patients who required more than 72 hours of mechanical ventilation and had a hospital stay of 7 days or more. Data on body mass index and biochemical markers of nutritional status were collected. Patients’ nutritional intake compared with physicians’ orders, dieticians’ recommendations, and indirect calorimetry and physicians’ orders compared with dieticians’ recommendations were used to assess nutritional status. Relationships between nutritional indicators and variables of mechanical ventilation were determined.

• ResultsInconsistencies among nurses’ implementation, physicians’ orders, and dieticians’ recommendations resulted in wide variations in patients’ calculated nutritional adequacy. Patients received a mean of 83% of the energy intake ordered by their physicians (SD 33%, range 0%–200%). Patients who required partial or total ventilator support upon discharge had a lower body mass index at admission than did patients with spontaneous respirations (Mann-Whitney U = 8441, P = .001).

• Conclusions In this sample, the variability in weaning progression and outcomes most likely reflects illness severity and complexity rather than nutritional status or nutritional therapies. Further studies are needed to determine the best methods to define nutritional adequacy and to evaluate nutritional status.

Can measured resting energy expenditure be estimated by formulae in daily clinical nutrition practice?

PURPOSE OF REVIEW

To recognize the fundamental factors that alter energy expenditure on a daily basis and the impact they have on the measurement of caloric consumption by the human body, through respiratory indirect calorimetry, and thus to try to determine which predictive equation best correlates with total energy expenditure generated from energy measurements.

RECENT FINDINGS

The most important compartment of the body, for its metabolic activity and influence upon resting metabolic rate, is fat-free mass. Other variables affecting energy expenditure are sex, weight, height, age, body surface area, fat mass and ethnicity. Metabolic and activity factors such as the thermic effect of nutrients, facultative thermogenesis, anabolism/growth and physical activity, also contribute, comprising total daily energy expenditure. Following the pioneering work of Harris and Benedict for the estimation of energy expenditure, several authors turned their experimental interest to this area, and various recent predictive formulae were derived. These are useful and easy to apply in daily clinical nutrition practice. However, because of the cited variables upon energy expenditure, the final daily caloric estimates show inherent errors ranging from -23.5 to +22.5% upon measured caloric expenditure. These are particularly remarkable in critically ill patients who are exposed to medical and surgical interventions.

SUMMARY

One has to be careful in choosing, understanding and clinically applying the results from predictive equations, bearing in mind that the original population from which the equation was derived does not always correspond to that currently being evaluated.