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

Measured energy expenditure according to the phases of critical illness: A descriptive cohort study

BACKGROUND

Indirect calorimetry is recommended for directing energy provision in the intensive care unit (ICU). However, limited reports exist of measured energy expenditure according to the phases of critical illness in large cohorts of patients during ICU admission. This study aimed to analyze measured energy expenditure overall in adult patients who were critically ill and across the different phases of critical illness.

METHODS

Indirect calorimetry measurements completed at a mixed ICU between January 2010 and July 2019 were eligible. Measured energy expenditure was analyzed and reported as kcal/day and kcal/kg/day overall, as the percentage increase above predicted basal metabolic rate and according to the phases of critical illness; acute early (day 1-2), acute late (day 3-7) and recovery (>7 days) phases using mixed effects linear modelling.

RESULTS

There were 629 patients with 863 measurements included; age mean (standard deviation) 48 (18) years, 68% male and 269 (43%) with a traumatic brain injury. Measured energy expenditure overall was 2263 (626) kcal/day (30 (7) kcal/kg/day), which corresponded to a median [interquartile range] of 135 [117-155] % increase above predicted basal metabolic rate. In patients with repeat measurements (n = 158), measured energy expenditure (mean ± standard error) increased over time; 27 ± 0.5 kcal/kg/day in the early acute, 30 ± 0.4 kcal/kg/day in the late acute, and 31 ± 0.4 kcal/kg/day in the recovery phases of critical illness (P < 0.001).

CONCLUSION

In a large cohort of ICU patients, measured energy expenditure was 135% above the basal metabolic rate and increased from the early acute to the late acute and recovery phases of critical illness.

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.

Measured resting energy expenditure and predicted resting energy expenditure based on ASPEN critical care guidelines for nutrition support: An agreement study

BACKGROUND

Predictive equations often inaccurately estimate energy needs in critically ill patients. This study evaluated the level of agreement between resting energy expenditure using 12 and 25 kcal/kg as recommended by the 2021 American Society for Parenteral and Enteral Nutrition critical care guidelines for nutrition support and energy expenditure measured by indirect calorimetry in patients in the intensive care unit.

METHODS

An agreement study was conducted on mechanically ventilated adults who had a documented measured energy expenditure within 10 days of intensive care unit admission. Agreement was assessed using Bland-Altman plots and Wilcoxon signed rank tests. A subgroup analysis was performed for patients with a body mass index of ≥30 kg/m² using actual body weight, adjusted body weight, and ideal body weight. Correlations between measured energy expenditure and patient characteristics were also explored.

RESULTS

Fifty-eight patients were included and were a median age of 64 years, 63.8% male, and a median body mass index of 28.0 kg/m2. The 12 kcal/kg and 25 kcal/kg differed significantly from measured energy expenditure (P < 0.001). Bland-Altman plots showed mean biases of -644.6 kcal/day for 12 kcal/kg and 406.5 kcal/day for 25 kcal/kg. In the body mass index ≥30 kg/m² subgroup (n = 22), 12 kcal/kg underestimated measured energy expenditure across all weights, and 25 kcal/kg was more accurate when using ideal or adjusted body weights.

CONCLUSIONS

Predicted energy expenditure using 12 kcal/kg and 25 kcal/kg based on the 2021 American Society for Parenteral and Enteral Nutrition critical care guidelines for nutrition support had poor agreement with measured energy expenditure in mechanically ventilated patients.

Course of measured energy expenditure over the first 10 days of critical illness: A nested prospective study in an adult surgical ICU

BACKGROUND & AIMS

Various factors may cause significant daily variations in energy expenditure in and between critically patients. This nested, prospective study (ClinicalTrials.gov Identifier: NCT04099108) in a predominantly trauma surgical ICU, aimed to determine the course of measured energy expenditure over the first 10 days of critical illness, and to identify factors contributing to energy expenditure.

METHODS

Indirect calorimetry was performed on alternate days from ICU Day 3 ± 1 until Day 10 ± 1. The mean daily measured energy expenditure, respiratory quotient and total energy delivery as a percentage of measured energy expenditure were modelled using linear mixed regression with two fractional polynomial terms to accommodate non-linear responses over time.

RESULTS

Fifty ICU patients (mean age 36.9 ± 11.8 years, Acute Physiology and Chronic Health Evaluation (APACHE II) 13.5 ± 6.6, Sequential Organ Failure Assessment (SOFA) 4.5 ± 3.2) were included. Mean body mass index (BMI) was 24.8 ± 4.0 kg/m2 and mean ventilation duration 7.7 ± 2.7 days. Mean daily measured energy expenditure showed a significant non-linear response (p = 0.006) increasing over the first 4 days peaking on day 5 and then plateauing. Mean daily respiratory quotient increased over the first 7 days, thereafter plateauing with a slight downward trend from day 8 despite a progressive increase in total energy delivery as a percentage of measured energy expenditure. Mean daily measured energy expenditure was significantly lower in the early than in the late acute phases (p = 0.024), whereas the late- and post-acute phases were similar. Age, sex and BMI significantly influenced measured energy expenditure.

CONCLUSION

Measured energy expenditure showed a significant non-linear response over the first 10 days in ICU, increasing over the first 4 days peaking on day 5 then plateauing. The observed variability highlights the complexity of managing critically ill patients and the importance of personalised nutrition therapy. Additionally, the observed trend with a peak in measured energy expenditure around day five could inform timing and strategies for nutritional intervention in this patient cohort.

Evaluating the effects of continuous veno-venous hemodiafiltration on O2 and CO2 removal and energy expenditure measurement using indirect calorimetry

BACKGROUND

Continuous veno-venous hemodiafiltration (CVVHDF) is used in critically ill patients, but its impact on O₂ and CO₂ removal, as well as the accuracy of resting energy expenditure (REE) measurement using indirect calorimetry (IC) remains unclear. This study aims to evaluate the effects of CVVHDF on O₂ and CO₂ removal and the accuracy of REE measurement using IC in patients undergoing continuous renal replacement therapy.

DESIGN

Prospective, observational, single-center study.

METHODOLOGY

Patients with sepsis undergoing CVVHDF had CO₂ flow (QCO₂) and O₂ flow (QO₂) measured at multiple sampling points before and after the filter. REE was calculated using the Weir equation based on V̇CO₂ and V̇O₂ measured by IC, using true V̇CO₂ accounting for the CRRT balance, and estimated using the Harris-Benedict equation. The respiratory quotient (RQ), the ratio of V̇CO₂ to V̇O₂, was evaluated by comparing measured and true values.

RESULTS

The mean QCO₂ levels measured upstream of the filter were 76.26 ± 17.33 ml/min and significantly decreased to 62.12 ± 13.64 ml/min downstream of the filter (p < 0.0001). The mean QO₂ levels remained relatively unchanged. The mean true REE was 1774.28 ± 438.20 kcal/day, significantly different from both the measured REE of 1758.59 ± 434.06 kcal/day (p = 0.0029) and the estimated REE of 1619.36 ± 295.46 kcal/day (p = 0.0475). The mean measured RQ value was 0.693 ± 0.118, while the mean true RQ value was 0.731 ± 0.121, with a significant difference (p < 0.0001).

CONCLUSIONS

CVVHDF may significantly alter QCO₂ levels without affecting QO₂, influencing the REE and RQ results measured by IC. However, the impact on REE is not clinically significant, and the REE value obtained via IC is closer to the true REE than that estimated using the Harris-Benedict equation. Further studies are recommended to confirm these findings.

Components of Total Energy Expenditure in Healthy and Critically Ill Children: A Comprehensive Review

BACKGROUND

Total energy expenditure (TEE) is the total energy expended by an individual to sustain life, activities, and growth. TEE is formed by four components: resting energy expenditure (REE), activity energy expenditure (AEE), growth-related energy expenditure (GEE), and the thermic effect of feeding (TEF). Some energy expenditure (EE) components may change throughout childhood and cannot be reliably estimated using prediction formulae.

OBJECTIVE

To summarize measured TEE components as reported in the literature in healthy and critically ill children.

METHODS

We searched MEDLINE, EMBASE, and CINAHL for studies published between 1946 and 7 September 2023. The primary outcome was energy expenditure. Included studies were published in English and measured one or more of TEE, AEE, GEE, and TEF with Indirect Calorimetry or Doubly Labeled Water in participants between 1 month and 18 years of age. We excluded studies reporting only REE or using predictive equations. Following abstraction, reported values were converted into kcal/kg/day or kcal/day as possible. Weighted mean values were calculated using median or means of EE measurements.

RESULTS

We found 138 studies, 8163 patients, and 16,636 eligible measurements. The median (IQR) study sample size was 20 (12, 35) patients. TEE was the most evaluated component. The median (IQR) TEE in infants was 73.1 (67.0, 76.5), in children 78.0 (66.0, 81.3), and in adolescents was 44.2 (41.8, 51.9) kcal/kg/day. Very few studies reported on GEE and TEF.

CONCLUSIONS

This is one of the first studies that summarizes components of total energy expenditure in different pediatric age groups in healthy and critically ill children. Growth- and feeding-associated energy expenditure are poorly reported in healthy children, while all components of TEE (except REE) are poorly reported in critically ill children.

Nutrition considerations for patients with persistent critical illness: A narrative review

Critically ill patients experience high rates of malnutrition and significant muscle loss during their intensive care unit (ICU) admission, impacting recovery. Nutrition is likely to play an important role in mitigating the development and progression of malnutrition and muscle loss observed in ICU, yet definitive clinical trials of nutrition interventions in ICU have failed to show benefit. As improvements in the quality of medical care mean that sicker patients are able to survive the initial insult, combined with an aging and increasingly comorbid population, it is anticipated that ICU length of stay will continue to increase. This review aims to discuss nutrition considerations unique to critically ill patients who have persistent critical illness, defined as an ICU stay of >10 days. A discussion of nutrition concepts relevant to patients with persistent critical illness will include energy and protein metabolism, prescription, and delivery; monitoring of nutrition at the bedside; and the role of the healthcare team in optimizing nutrition support.

Trajectories of resting energy expenditure and performance of predictive equations in children hospitalized with an acute illness and malnutrition: a longitudinal study

There is scarce data on energy expenditure in ill children with different degrees of malnutrition. This study aimed to determine resting energy expenditure (REE) trajectories in hospitalized malnourished children during and after hospitalization. We followed a cohort of children in Bangladesh and Malawi (2-23 months) with: no wasting (NW); moderate wasting (MW), severe wasting (SW), or edematous malnutrition (EM). REE was measured by indirect calorimetry at admission, discharge, 14-and-45-days post-discharge. 125 children (NW, n = 23; MW, n = 29; SW, n = 51; EM, n = 22), median age 9 (IQR 6, 14) months, provided 401 REE measurements. At admission, the REE of children with NW and MW was 67 (95% CI [58, 75]) and 70 (95% CI [63, 76]) kcal/kg/day, respectively, while REE in children with SW was higher, 79 kcal/kg/day (95% CI [74, 84], p = 0.018), than NW. REE in these groups was stable over time. In children with EM, REE increased from admission to discharge (65 kcal/kg/day, 95% CI [56, 73]) to 79 (95% CI [72, 86], p = 0.0014) and was stable hereafter. Predictive equations underestimated REE in 92% of participants at all time points. Recommended feeding targets during the acute phase of illness in severely malnourished children exceeded REE. Acutely ill malnourished children are at risk of being overfed when implementing current international guidelines.

Measured versus estimated energy requirement in hospitalized patients

BACKGROUND & AIM

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

METHODS

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

RESULTS

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

CONCLUSION

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

Determining energy and protein needs in critically ill pediatric patients: A scoping review

INTRODUCTION

In critically ill pediatric patients, optimal energy and protein intakes are associated with a decreased risk of morbidity and mortality. However, the determination of energy and protein needs is complex. The objective of this scoping review was to understand the extent and type of evidence related to the methods used to determine energy and protein needs in critically ill pediatric patients.

METHODS

An international expert group composed of dietitians, pediatric intensivists, a nurse, and a methodologist conducted the review, based on the Johanna Briggs Institute methodology. Two researchers searched for studies published between 2008 and 2023 in two electronic databases, screened abstracts and relevant full texts for eligibility, and extracted data.

RESULTS

A total of 39 studies were included, mostly conducted in critically ill children undergoing ventilation, to assess the accuracy of predictive equations for estimating resting energy expenditure (REE) (n = 16, 41%) and the impact of clinical factors (n = 22, 56%). They confirmed the risk of underestimation or overestimation of REE when using predictive equations, of which the Schofield equation was the least inaccurate. Apart from weight and age, which were positively correlated with REE, the impact of other factors was not always consistent. No new indirect calorimeter method used to determine protein needs has been validated.

CONCLUSION

This scoping review highlights the need for scientific data on the methods used to measure energy expenditure and determine protein needs in critically ill children. Studies using a reference method are needed to validate an indirect calorimeter.

The metabolic cost of inspiratory muscle training in mechanically ventilated patients in critical care

BACKGROUND

Diaphragmatic dysfunction is well documented in patients receiving mechanical ventilation. Inspiratory muscle training (IMT) has been used to facilitate weaning by strengthening the inspiratory muscles, yet the optimal approach remains uncertain. Whilst some data on the metabolic response to whole body exercise in critical care exist, the metabolic response to IMT in critical care is yet to be investigated. This study aimed to quantify the metabolic response to IMT in critical care and its relationship to physiological variables.

METHODS

We conducted a prospective observational study on mechanically ventilated patients ventilated for ≥ 72 h and able to participate in IMT in a medical, surgical, and cardiothoracic intensive care unit. 76 measurements were taken on 26 patients performing IMT using an inspiratory threshold loading device at 4 cmH₂O, and at 30, 50 and 80% of their negative inspiratory force (NIF). Oxygen consumption (VO₂) was measured continuously using indirect calorimetry.

RESULTS

First session mean (SD) VO₂ was 276 (86) ml/min at baseline, significantly increasing to 321 (93) ml/min, 333 (92) ml/min, 351(101) ml/min and 388 (98) ml/min after IMT at 4 cmH₂O and 30, 50 and 80% NIF, respectively (p = 0.003). Post hoc comparisons revealed significant differences in VO₂ between baseline and 50% NIF and baseline and 80% NIF (p = 0.048 and p = 0.001, respectively). VO₂ increased by 9.3 ml/min for every 1 cmH₂O increase in inspiratory load from IMT. Every increase in P/F ratio of 1 decreased the intercept VO₂ by 0.41 ml/min (CI - 0.58 to - 0.24 p < 0.001). NIF had a significant effect on the intercept and slope, with every 1 cmH₂O increase in NIF increasing intercept VO₂ by 3.28 ml/min (CI 1.98-4.59 p < 0.001) and decreasing the dose-response slope by 0.15 ml/min/cmH₂O (CI - 0.24 to - 0.05 p = 0.002).

CONCLUSIONS

IMT causes a significant load-dependent increase in VO₂. P/F ratio and NIF impact baseline VO₂. The dose-response relationship of the applied respiratory load during IMT is modulated by respiratory strength. These data may offer a novel approach to prescription of IMT.

TAKE HOME MESSAGE

The optimal approach to IMT in ICU is uncertain; we measured VO₂ at different applied respiratory loads to assess whether VO₂ increased proportionally with load and found VO₂ increased by 9.3 ml/min for every 1 cmH₂O increase in inspiratory load from IMT. Baseline NIF has a significant effect on the intercept and slope, participants with a higher baseline NIF have a higher resting VO₂ but a less pronounced increase in VO₂ as the inspiratory load increases; this may offer a novel approach to IMT prescription. Trial registration ClinicalTrials.gov, registration number: NCT05101850. Registered on 28 September 2021, https://clinicaltrials.gov/ct2/show/NCT05101850.

Energy Expenditure Under General Anesthesia: An Observational Study Using Indirect Calorimetry in Patients Having Noncardiac Surgery

BACKGROUND

Perioperative hemodynamic management aims to optimize organ perfusion pressure and blood flow-assuming this ensures that oxygen delivery meets cellular metabolic needs. Cellular metabolic needs are reflected by energy expenditure. A better understanding of energy expenditure under general anesthesia could help tailor perioperative hemodynamic management to actual demands. We thus sought to assess energy expenditure under general anesthesia. Our primary hypothesis was that energy expenditure under general anesthesia is lower than preoperative awake resting energy expenditure.

METHODS

We conducted an observational study on patients having elective noncardiac surgery at the University Medical Center Hamburg-Eppendorf (Germany) between September 2019 and March 2020. We assessed preoperative awake resting energy expenditure, energy expenditure under general anesthesia, and energy expenditure after surgery using indirect calorimetry. We compared energy expenditure under general anesthesia at incision to preoperative awake resting energy expenditure using a Wilcoxon signed-rank test for paired measurements.

RESULTS

We analyzed 60 patients. Median (95% confidence interval [CI]) preoperative awake resting energy expenditure was 953 (95% CI, 906-962) kcal d -1 m -2 . Median energy expenditure under general anesthesia was 680 (95% CI, 642-711) kcal d -1 m -2 -and thus 263 (95% CI, 223-307) kcal d -1 m -2 or 27% (95% CI, 23%-30%) lower than preoperative awake resting energy expenditure ( P < .001).

CONCLUSIONS

Median energy expenditure under general anesthesia is about one-quarter lower than preoperative awake resting energy expenditure in patients having noncardiac surgery.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Medical nutrition therapy in patients receiving ECMO: Evidence-based guidance for clinical practice

Patients receiving extracorporeal membrane oxygenation (ECMO) inherit substantial disease-associated metabolic, endocrinologic, and immunologic modifications. Along with the technical components of ECMO, the aforementioned alterations may affect patients' needs and feasibility of adequate macronutrient and micronutrient supply and intake. Thus, patients receiving ECMO are at increased risk for iatrogenic malnutrition and require targeted individual medical nutrition therapy (MNT). However, specific recommendations for MNT in patients receiving ECMO are limited and, with some exceptions, based on an evidence base encompassing general patients who are critically ill. Consequently, clinician decision-making for MNT in patients receiving ECMO is unguided, which may further increase nutrition risk, culminating in iatrogenic malnutrition and ultimately affecting patient outcomes. The purpose of this article is to provide educational background and highlight specific points for MNT in adult patients receiving ECMO, which might serve as evidence-based guidance to develop institutional standard operating procedures and nutrition protocols for daily clinical practice.

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 support practices for critically ill patients with severe acute respiratory syndrome coronavirus-2: A multicentre observational study in Singapore

INTRODUCTION

To improve the nutritional care and resource allocation of critically ill patients with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), we described their characteristics, treatment modalities and clinical outcomes, and compared their nutrition interventions against the American Society for Parenteral and Enteral Nutrition (ASPEN) recommendations.

METHODS

This was a retrospective observational study conducted in 5 tertiary hospitals in Singapore. Characteristics, treatment modalities, clinical outcomes and nutrition interventions of critically ill patients with SARS-CoV-2 who received enteral and parenteral nutrition were collected between January and May 2020.

RESULTS

Among the 83 critically ill patients with SARS-CoV-2, 22 (28%) were obese, 45 (54%) had hypertension, and 21 (25%) had diabetes. Neuromuscular blockade, prone therapy and dialysis were applied in 70% (58), 47% (39) and 35% (29) of the patients, respectively. Refeeding hypophosphataemia and hospital mortality occurred respectively in 6% (5) and 18% (15) of the critically ill patients with SARS-CoV-2. Late enteral nutrition and cardiovascular comorbidities were associated with higher hospital mortality (adjusted relative risk 9.00, 95% confidence interval [CI] 2.25-35.99; 6.30, 95% CI 1.15-34.40, respectively). Prone therapy was not associated with a higher incidence of high gastric residual volume (≥250mL). The minimum caloric (15kcal/kg) and protein (1.2g/kg) recommendations of ASPEN were achieved in 54% (39) and 0% of the patients, respectively.

CONCLUSION

The high obesity prevalence and frequent usage of neuromuscular blockade, prone therapy, and dialysis had considerable implications for the nutritional care of critically ill patients with SARS-CoV-2. They also did not receive adequate calories and protein. More audits should be conducted to refine nutritional interventions and guidelines for this ever-evolving disease.

Methodological Aspects of Indirect Calorimetry in Patients with Sepsis-Possibilities and Limitations

The aim of the review was to analyse the challenges of using indirect calorimetry in patients with sepsis, including the limitations of this method. A systematic review of the literature was carried out. The analysis concerned the methodology and presentation of research results. In most studies assessing energy expenditure, energy expenditure was expressed in kcal per day (n = 9) and as the mean and standard deviation (n = 7). Most authors provided a detailed measurement protocol, including measurement duration (n = 10) and device calibration information (n = 7). Ten papers provided information on the day of hospitalisation when the measurements were obtained, nine on patient nutrition, and twelve on the criteria for inclusion and exclusion of participants from the study. Small study group sizes and study at a single centre were among the most cited limitations. Studies assessing energy expenditure in patients with sepsis by indirect calorimetry differ in the methodology and presentation of results, and their collective analysis is difficult. A meta-analysis of the results could enable multi-site and large patient evaluation. Standardisation of protocols and presentation of all collected data would enable their meta-analysis, which would help to achieve greater knowledge about metabolism in sepsis.

Do we need different predictive equations for the acute and late phases of critical illness? A prospective observational study with repeated indirect calorimetry measurements

BACKGROUND

Predictive equations (PEs) for estimating resting energy expenditure (REE) that have been developed from acute phase data may not be applicable in the late phase and vice versa. This study aimed to assess whether separate PEs are needed for acute and late phases of critical illness and to develop and validate PE(s) based on the results of this assessment.

METHODS

Using indirect calorimetry, REE was measured at acute (≤5 days; n = 294) and late (≥6 days; n = 180) phases of intensive care unit admission. PEs were developed by multiple linear regression. A multi-fold cross-validation approach was used to validate the PEs. The best PEs were selected based on the highest coefficient of determination (R²), the lowest root mean square error (RMSE) and the lowest standard error of estimate (SEE). Two PEs developed from paired 168-patient data were compared with measured REE using mean absolute percentage difference.

RESULTS

Mean absolute percentage difference between predicted and measured REE was <20%, which is not clinically significant. Thus, a single PE was developed and validated from data of the larger sample size measured in the acute phase. The best PE for REE (kcal/day) was 891.6(Height) + 9.0(Weight) + 39.7(Minute Ventilation)-5.6(Age) - 354, with R² = 0.442, RMSE = 348.3, SEE = 325.6 and mean absolute percentage difference with measured REE was: 15.1 ± 14.2% [acute], 15.0 ± 13.1% [late].

CONCLUSIONS

Separate PEs for acute and late phases may not be necessary. Thus, we have developed and validated a PE from acute phase data and demonstrated that it can provide optimal estimates of REE for patients in both acute and late phases.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03319329.

Indirect Calorimetry as an Instrument of Research to Identify the Effect of Hypermetabolism in Critical Patients' Prognosis

Background: Energy expenditure (EE) evaluation in Intensive Care Unit (ICU) patients can be very challenging. Critical illness is characterized by great variability in EE, which is influenced by the disease itself and the effects of treatment. Indirect calorimetry (IC) is currently the gold standard to measure EE in Intensive Care Unit (ICU) patients. However, calorimeters are not widely available, and predictive formulas (PF) are still commonly used, leading to under or overfeeding and deleterious consequences.Important metabolic changes occur and catabolism becomes prominent in critically ill patients.Both hyper and hypometabolism can be observed, but hypermetabolic patients appear to have higher mortality rates compared to metabolically normal patients. This study aimed to assess hypermetabolism incidence and compare clinical outcomes between hypermetabolic and normometabolic patients in ICU.

Methods: A single-center, retrospective, and observational study was conducted in the ICU of the Hospital do Divino Espírito Santo in Ponta Delgada, between August 2018 and February 2021. Only invasively mechanically ventilated patients were included. Resting energy expenditure (REE) was predicted by 25 kcal/kg/day formula to obtain predicted resting energy expenditure (PREE), and REE was measured by IC to obtain measured resting energy expenditure (MREE). According to their metabolic state (PREE/MREE), patients were divided into hypermetabolic (≥1.3) and normometabolic (<1.3). To determine the limits of agreement between PREE and MREE, we performed a Bland-Altman (BA) analysis. Baseline characteristics, severity criteria, nutritional status, and main diagnosis on admission were compared. The primary outcome considered was 30-day mortality. Other outcomes such as the ICU length of stay (LOS), in-hospital LOS, and length of invasive ventilation were also evaluated.

Results: Among the 80 ICU patients included in the final analysis, 67 patients were normometabolic (83.4%). Patients admitted due to pneumonia were more hypermetabolic, 8 (61.5%) vs. 10 (14.9%); p<0.001. Hypermetabolism was found also in patients admitted due to sepsis/septic shock, 7 (53.8%) vs. 16 (23.9%); p=0.029. Hypermetabolic patients had lower body mass index (22.5 [interquartile range (IQR): 21.5-24.9] vs. 27.7 [IQR: 25.0-32.4] kg/m²; p=0.001) and higher MREE (2715.0 [2399.0-3090.0] vs. 1690.0 [1410.0-2190.0] kcal/day; p<0.001). Bland-Altman analysis showed a mean difference of -5.6 ± 744.7 Kcal/day between the PREE and MREE by IC. No statistically significant difference was found between the two groups, neither in 30-day mortality nor in the other outcomes considered.

Conclusions: Hypermetabolism was not seen to present a greater risk of death in mechanically ventilated patients in ICU. Lower BMI, sepsis/septic shock, and pneumonia appear to be associated with a hypermetabolic state.

Indirect calorimetry in critical illness: a new standard of care?

PURPOSE OF REVIEW

Review recent literature on the role of indirect calorimetry in critical care nutrition management.

RECENT FINDINGS

Critical illness demands objective, targeted nutritional therapy to prevent adverse effects of underfeeding/over feeding. Thus, all recent societal guidelines recommend indirect calorimetry use to determine energy needs. Very recently, indirect calorimetry technology has finally evolved to allow for accurate, simple, and routine utilization in a wider range of ICU patients. Recent data continues to confirm poor correlation between measured and equation-predicted energy expenditure emphasizing need for indirect calorimetry to be standard of care. This may be particularly true in COVID-19, where significant progressive hypermetabolism and variability in energy expenditure has been shown. Metabolic physiology can change frequently during ICU stay in response to changes in clinical condition or care. Thus, repeated longitudinal indirect calorimetry measures are needed throughout ICU stay to optimize care, with initial data showing improved clinical outcomes when indirect calorimetry targets are utilized.

SUMMARY

Personalized ICU care demands objective data to guide therapy. This includes use of indirect calorimetry to determine energy expenditure and guide ICU nutrition therapy. Long-awaited new innovations in indirect calorimetry technology should finally lead to indirect calorimetry to becoming a fundamental component of modern ICU standard of care and clinical research moving forward.

Energy expenditure and indirect calorimetry in critical illness and convalescence: current evidence and practical considerations

The use of indirect calorimetry is strongly recommended to guide nutrition therapy in critically ill patients, preventing the detrimental effects of under- and overfeeding. However, the course of energy expenditure is complex, and clinical studies on indirect calorimetry during critical illness and convalescence are scarce. Energy expenditure is influenced by many individual and iatrogenic factors and different metabolic phases of critical illness and convalescence. In the first days, energy production from endogenous sources appears to be increased due to a catabolic state and is likely near-sufficient to meet energy requirements. Full nutrition support in this phase may lead to overfeeding as exogenous nutrition cannot abolish this endogenous energy production, and mitochondria are unable to process the excess substrate. However, energy expenditure is reported to increase hereafter and is still shown to be elevated 3 weeks after ICU admission, when endogenous energy production is reduced, and exogenous nutrition support is indispensable. Indirect calorimetry is the gold standard for bedside calculation of energy expenditure. However, the superiority of IC-guided nutritional therapy has not yet been unequivocally proven in clinical trials and many practical aspects and pitfalls should be taken into account when measuring energy expenditure in critically ill patients. Furthermore, the contribution of endogenously produced energy cannot be measured. Nevertheless, routine use of indirect calorimetry to aid personalized nutrition has strong potential to improve nutritional status and consequently, the long-term outcome of critically ill patients.

The effect of age and body mass index on energy expenditure of critically ill medical patients

Background

Data on the influence of age and body mass index (BMI) on energy metabolism of the critically ill are heterogeneous. Due to the increasingly aging critically ill population, investigation on age- and BMI-specific energy metabolism is relevant.

Methods

A total of 394 indirect calorimetry measurements were conducted on 348 critically ill adult medical patients, including 46 repeat measurements after 3.6 ± 4.3 days. Measured resting energy expenditure (MREE) was compared for age groups, BMI, and gender. Predicted energy expenditure (PEE) using the Penn State, Swinamer, and Ireton-Jones equations and the ACCP recommendations was also compared with MREE.

Results

The patients were 65.6 ± 14.5 years old. Their mean Acute Physiology and Chronic Health Evaluation II score was 27.6 ± 7.8. Mean BMI was 27.8 ± 8.4 kg/m², and 25.6% were obese. MREE adjusted for ideal body weight decreased with increasing age, while it increased with increasing BMI. Age, BMI, and gender are independent determinants of MREE after adjusting for clinical factors (R² = 0.34). All four prediction equations showed a proportional bias, with the Penn State equation performing acceptably. In 46 patients with repeat indirect calorimetry, there was no significant difference between the first and second MREE (p = 0.62).

Conclusions

Age, BMI, and gender are independent determinants of resting energy expenditure in critically ill adults. Variations between measured and predicted energy expenditure are considerable. Should prediction equations be used, their performance in the specific population should be taken into consideration. Repeat indirect calorimetry may not always be necessary. However, this may depend on the length of stay and the extent of stress.

[Nutritional support for critically ill patients suffering from SARS-CoV-2 infection]

Patients with severe cases of COVID-19 are at high nutritional risk during their ICU stay. Prolonged immobilization associated with an exacerbated systemic inflammatory response is a major provider of ICU-acquired muscle weakness. Early enteral nutrition is recommended to gradually reach the energy target of 25 kcal/kg/day and protein target of 1.3 g/kg/day around D4. The occurrence of a Refeeding syndrome should be closely monitored. In case of feeding intolerance refractory to a prokinetic treatment, complementary or total parenteral nutrition is advised, favouring new generation mixed lipid emulsions (containing fish oil) and regular monitoring of triglyceridemia. Nutrition care of critically ill patients should be carried out with limited procedures that may pose a risk of contamination for the healthcare staff.