Measured and predicted resting metabolic rate in patients with inflammatory bowel disease.

OBJECTIVE: The present study aimed to compare measured and estimated resting metabolic rate (RMR) predicted by selected equations in patients with nonactive inflammatory bowel disease (IBD) on an outpatient university clinic regimen. RESEARCH METHODS & PROCEDURES: Seventy-two adult (≥20 years) IBD patients (45 with Crohn's disease-CD) had RMR measured (mRMR) by indirect calorimetry and also estimated by predictive equations (Cunningham, Henry, Anjos et al., and Marra et al.). Body composition was assessed by DXA. Absolute Bias (estimated - mRMR) and % Bias (Bias/mRMR) were calculated. Agreement was assessed as the limit of agreement (LoA) in the Bland & Altman approach. RESULTS: There was no difference in age, body composition and mRMR between individuals with CD (5414.2 ± 1023.7 kJ/day) and ulcerative colitis (5443.9 ± 1008.9 kJ/day). Among the equations, only the Anjos et al.'s population-specific equation (-52.1 [642.0] kJ/day, P = 0.493; LoA: -1311; 1206 kJ/d) accurately estimated RMR. The equations of Marra et al. produced the highest % Bias (24.1 ± 14.8%). The Bland & Altman plots showed that the range of the LoA was relatively similar for all equations. In the simple regression analysis, the model with FFM resulted in a higher coefficient of determination (R2 = 0.51 for DC 0.74 for UC) compared to the model that included BM (R2 = 0.35 for DC and 0.65 for UC). CONCLUSIONS: Among the equations analyzed, only Anjos et al.'s accurately estimated RMR in outpatients with nonactive IBD. However, caution is advised when applying it at the individual level, due to the wide observed LoA.

Composite activity type and stride-specific energy expenditure estimation model for thigh-worn accelerometry.

BACKGROUND: Accurately measuring energy expenditure during physical activity outside of the laboratory is challenging, especially on a large scale. Thigh-worn accelerometers have gained popularity due to the possibility to accurately detect physical activity types. The use of machine learning techniques for activity classification and energy expenditure prediction may improve accuracy over current methods. Here, we developed a novel composite energy expenditure estimation model by combining an activity classification model with a stride specific energy expenditure model for walking, running, and cycling. METHODS: We first trained a supervised deep learning activity classification model using pooled data from available adult accelerometer datasets. The composite energy expenditure model was then developed and validated using additional data based on a sample of 69 healthy adult participants (49% female; age = 25.2 ± 5.8 years) who completed a standardised activity protocol with indirect calorimetry as the reference measure. RESULTS: The activity classification model showed an overall accuracy of 99.7% across all five activity types during validation. The composite model for estimating energy expenditure achieved a mean absolute percentage error of 10.9%. For running, walking, and cycling, the composite model achieved a mean absolute percentage error of 6.6%, 7.9% and 16.1%, respectively. CONCLUSIONS: The integration of thigh-worn accelerometers with machine learning models provides a highly accurate method for classifying physical activity types and estimating energy expenditure. Our novel composite model approach improves the accuracy of energy expenditure measurements and supports better monitoring and assessment methods in non-laboratory settings.

Resting energy expenditure in patients with liver cirrhosis: Indirect calorimetry vs. predictive equations.

BACKGROUND AND OBJECTIVES: The objective of our study was to explore the accuracy of previously published prediction equations in predicting resting energy expenditure (REE) in patients with liver cirrhosis (LC). We also aimed to develop a novel equation to estimate REE for Chinese patients with LC. METHODS AND STUDY DESIGN: In 90 patients with LC, the agreement between REE measured by Indirect calorimetry (IC) and predictive equations was quantified using paired T-test and visualized using a Bland-Altman Plot. Pearson correlation coefficient (R) was used to measure a linear correlation between REE measured by IC and different predictive equations. Stepwise multiple regression analysis was used to create a new REE equation. RESULTS: The estimated REEs of previous equations were underestimated against REE measured by IC (1610 ± 334 kcal). Lean body mass (LBM) was positively correlated with REE measured by IC (r = 0.723, p < 0.01). The newly derived estimation equation for REE (kcal) was 1274.3 - 209.0 * sex - 5.73 * age + 3.69 * waist circumference + 22.89 * LBM. The newly derived estimation equation was found to have a Pearson-r value of 0.765 compared with REE measured by IC. CONCLUSIONS: REE in liver cirrhosis was underestimated by using predictive equations. The new predictive equation developed by using age, sex, waist circumference, and LBM may help estimate REE in Chinese patients with LC accurately and easily.

Evaluating predictive equations for energy requirements throughout breast cancer trajectory: A comparative study.

BACKGROUND & AIMS: Accurately estimating resting energy requirements is crucial for optimizing energy intake, particularly in the context of patients with varying energy needs, such as individuals with cancer. We sought to evaluate the agreement between resting energy expenditure (REE) predicted by 40 equations and that measured by reference methods in women undergoing active breast cancer treatment stage (I-IV) and post-completion (i.e., survivors). METHODS: Data from 4 studies were combined. REE values estimated from 40 predictive equations identified by a systematic search were compared with REE assessed by indirect calorimetry (IC) using a metabolic cart (MC-REE N = 46) or a whole-room indirect calorimeter (WRIC-REE N = 44). Agreement between methods was evaluated using Bland-Altman and Lin's concordance coefficient correlation (Lin's CCC). RESULTS: Ninety participants (24 % survivors, 61.1% had early-stage breast cancer I or II, mean age: 56.8 ± 11 years; body mass index: 28.7 ± 6.4 kg/m2) were included in this analysis. Mean MC-REE and WRIC-REE values were 1389 ± 199 kcal/day and 1506 ± 247 kcal/day, respectively. Limits of agreement were wide for all equations compared to both MC and WRIC (∼300 kcal for both methods), including the most commonly used ones, such as Harris-Benedict and Mifflin ST. Jeor equations; none had a bias within ±10% of measured REE, and all had low agreement per Lin's CCC analysis (<0.90). The Korth equation exhibited the best performance against WRIC and the Lvingston-Kohlstadt equation against MC. Similar patterns of bias were observed between survivors and patients and between patients with stages I-III versus IV cancer. CONCLUSION: Most equations failed to accurately predict REE at the group level, and none were effective at the individual level. This inaccuracy has significant implications for women with or surviving breast cancer, who may experience weight gain, maintenance, or loss due to inaccurate energy needs estimations. Therefore, our research underscores the need for further efforts to improve REE estimation.

Comparative analysis of basal metabolic rate measurement methods in overweight and obese individuals: A retrospective study.

The global prevalence of overweight and obesity is on the rise, presenting significant health challenges worldwide. Obesity is associated with various chronic diseases and imposes substantial economic burdens on society. Accurate assessment of basal metabolic rate (BMR) is crucial for effective weight management strategies. This retrospective study, conducted at Baskent University Hospital between October 2019 and October 2023, analyzed data from 133 overweight and obese individuals. Various methods including indirect calorimetry (IC), predictive equations (Harris-Benedict and Mifflin-St Jeor), and bioelectrical impedance analysis (BIA) were used to estimate BMR. Additionally, demographic, clinical, and body composition data were recorded. The mean BMR measured using IC was 1581 ±â€…322 kcal/day, which was significantly lower than the BMR estimated by other methods such as BIA (1765.8 ±â€…344.09 kcal/day), Harris-Benedict (1787.64 ±â€…341.4 kcal/day), and Mifflin-St Jeor equations (1690.08 ±â€…296.36 kcal/day) (P < .001). Among the predictive equations, the Mifflin-St Jeor method provided BMR estimates closest to the gold standard IC. When BMR measurement methods were compared to IC, 36.8% of measurements with the Harris-Benedict equation method, 50.4% with the Mifflin-St Jeor equation method, and 36.1% with the BIA method were within ±â€…10% agreement with IC measurements. Significant correlations were found between BMR and body composition parameters such as fat-free mass, muscle mass, and fat mass (R = 0.681, P < .001; R = 0.699, P < .001; R = 0.595, P < .001, respectively). Regression analysis identified that variables such as weight, height, body mass index, and muscle mass significantly predicted BMR measured by IC, accounting for 69.1% of the variance. This study underscores the challenges in assessing BMR in overweight and obese individuals. While IC remains the gold standard, predictive equations and BIA offer alternative methods. The Mifflin-St Jeor equation emerged as a practical option, closely aligning with IC results. However, discrepancies between methods and the influence of body composition highlight the importance of individualized approaches to BMR assessment in weight management strategies.

Bioelectrical Impedance Analysis to Assess Energy Expenditure in Critically Ill Patients: A Cross-Sectional Study.

BACKGROUND: Evaluating energy expenditure is important for establishing optimal goals for nutrition treatment. However, indirect calorimetry, the reference standard for measuring energy expenditure, is difficult to apply widely in clinical practice. OBJECTIVE: To test the consistency of bioelectrical impedance analysis (BIA) relative to indirect calorimetry for evaluating energy expenditure in critically ill patients. METHODS: A cross-sectional study of 140 critically ill adult patients was conducted. Within 24 hours of a patient being transferred to the intensive care unit, trained researchers assessed the patient's energy expenditure by use of BIA and indirect calorimetry simultaneously. Consistency of the 2 measurements was detected by intraclass correlation coefficients with a 2-way random-effects model. Factors affecting consistency were analyzed. RESULTS: Median energy expenditure measured by indirect calorimetry was 1430.0 kcal/d (IQR, 1190.5-1650.8 kcal/d). Median energy expenditure measured by BIA was 1407.0 kcal/d (IQR, 1248.5-1563.5 kcal/d). The correlation coefficient between indirect calorimetry and BIA was 0.813 (95% CI, 0.748-0.862; P < .001). The consistency of the 2 measurements was lower in patients with comorbidities than in those without (P = .004). CONCLUSIONS: Results of BIA were highly consistent with indirect calorimetry assessments of energy expenditure in critically ill patients. Few factors except comorbidity affect the accuracy of BIA when assessing energy expenditure. Therefore, as a low-cost, easy-to-use, and noninvasive method, BIA is a valuable clinical tool for assessing energy expenditure in critically ill patients.

Perioperative estimations of oxygen consumption from LiDCO™plus-derived cardiac output and Ca-cvO2 difference: Relationship with measurements by indirect calorimetry in elderly patients undergoing major abdominal surgery.

BACKGROUND: Feasible estimations of perioperative changes in oxygen consumption (VO2) could enable larger studies of its role in postoperative outcomes. Current methods, either by reverse Fick calculations using pulmonary artery catheterisation or metabolic by breathing gas analysis, are often deemed too invasive or technically requiring. In addition, reverse Fick calculations report generally lower values of oxygen consumption. METHODS: We investigated the relationship between perioperative estimations of VO2 (EVO2), from LiDCO™plus-derived (LiDCO Ltd, Cambridge, UK) cardiac output and arterial-central venous oxygen content difference (Ca-cvO2), with indirect calorimetry (GVO2) by QuarkRMR (COSMED srl. Italy), using data collected 2017-2018 during a prospective observational study on perioperative oxygen transport in 20 patients >65 years during epidural and general anaesthesia for open pancreatic or liver resection surgery. Eighty-five simultaneous intra- and postoperative measurements at different perioperative stages were analysed for prediction, parallelity and by traditional agreement assessment. RESULTS: Unadjusted bias between GVO2 and EVO2 indexed for body surface area was 26 (95% CI 20 to 32) with limits of agreement (1.96SD) of -32 to 85 ml min-1m-2. Correlation adjusted for the bias was moderate, intraclass coefficient(A,1) 0.51(95% CI 0.34 to 0.65) [F (84,84) = 3.07, P<0.001]. There was an overall association between GVO2 and EVO2, in a random coefficient model [GVO2 = 73(95% CI 62 to 83) + 0.45(95% CI 0.29 to 0.61) EVO2 ml min-1m-2, P<0.0001]. GVO2 and EVO2 changed in parallel intra- and postoperatively when normalised to their respective overall means. CONCLUSION: Based on this data, estimations from LiDCO™plus-derived cardiac output and Ca-cvO2 are not reliable as a surrogate for perioperative VO2. Results were in line with previous studies comparing Fick-based and metabolic measurements but limited by variability of data and possible underpowering. The parallelity at different perioperative stages and the prediction model can provide useful guidance and methodological tools for future studies on similar methods in larger samples.

Validity and reliability of a new whole room indirect calorimeter to assess metabolic response to small calorie loads.

We overview of our whole room indirect calorimeter (WRIC), demonstrate validity and reliability of our WRIC, and explore a novel application of Bayesian hierarchical modeling to assess responses to small carbohydrate loads. To assess WRIC validity seven gas infusion studies were performed using a gas blender and profiles designed to mimic resting and postprandial metabolic events. Sixteen participants underwent fasting and postprandial measurements, during which they consumed a 75-kcal drink containing sucrose, dextrose, or fructose in a crossover design. Linear mixed effects models were used to compare resting and postprandial metabolic rate (MR) and carbohydrate oxidation. Postprandial carbohydrate oxidation trajectories for each participant and condition were modeled using Bayesian Hierarchical Modeling. Mean total error in infusions were 1.27 ± 0.67% and 0.42 ± 0.70% for VO2 and VCO2 respectively, indicating a high level of validity. Mean resting MR was similar across conditions ([Formula: see text] = 1.05 ± 0.03 kcal/min, p = 0.82, ICC: 0.91). While MR increased similarly among all conditions (~13%, p = 0.29), postprandial carbohydrate oxidation parameters were significantly lower for dextrose compared with sucrose or fructose. We provide evidence validating our WRIC and a novel application of statistical methods useful for research using WRIC.

The use of accelerometers to improve estimation of the thermic effect of food in whole room calorimetry studies.

We tested whether spontaneous physical activity (SPA) from accelerometers could be used in a whole room calorimeter to estimate thermic effect of food (TEF). Eleven healthy participants (n = 7 females; age: 27 ± 4 yr; body mass index: 22.8 ± 2.6 kg/m2) completed two 23-h visits in randomized order: one "fed" with meals provided and one "fasted" with no food. SPA was measured by ActivPAL and Actigraph accelerometers. Criterion TEF was calculated as the difference in total daily energy expenditure (TDEE) between fed and fasted visits and compared with three methods of estimating TEF: 1) SPA-adjusted TEF (adjTEF)-difference in TDEE without SPA between visits, 2) Wakeful TEF-difference in energy expenditure obtained from linear regression and basal metabolic rate during waking hours, 3) 24-h TEF-increase in TDEE above SPA and sleeping metabolic rate. Criterion TEF was 9.4 ± 4.5% of TDEE. AdjTEF (difference in estimated vs. criterion TEF: activPAL: -0.3 ± 3.3%; Actigraph: -1.8 ± 8.0%) and wakeful TEF (activPAL: -0.9 ± 6.1%; Actigraph: -2.8 ± 7.6%) derived from both accelerometers did not differ from criterion TEF (all P > 0.05). ActivPAL-derived 24-h TEF overestimated TEF (6.8 ± 5.4%, P = 0.002), whereas Actigraph-derived 24-h TEF was not significantly different (4.3 ± 9.4%, P = 0.156). TEF estimations using activPAL tended to show better individual-level agreement (i.e., smaller coefficients of variation). Both accelerometers can be used to estimate TEF in a whole room calorimeter; wakeful TEF using activPAL is the most viable option given strong group-level accuracy and reasonable individual agreement.NEW & NOTEWORTHY Two research-grade accelerometers can effectively estimate spontaneous physical activity and improve the estimation of thermic effect of food (TEF) in whole room calorimeters. The activPAL demonstrates strong group-level accuracy and reasonable individual-level agreement in estimating wakeful TEF, whereas a hip-worn Actigraph is an acceptable approach for estimating 24-h TEF. These results highlight the promising potential of accelerometers in advancing energy balance research by improving the assessment of TEF within whole room calorimeters.

A CNN Model for Physical Activity Recognition and Energy Expenditure Estimation from an Eyeglass-Mounted Wearable Sensor.

Metabolic syndrome poses a significant health challenge worldwide, prompting the need for comprehensive strategies integrating physical activity monitoring and energy expenditure. Wearable sensor devices have been used both for energy intake and energy expenditure (EE) estimation. Traditionally, sensors are attached to the hip or wrist. The primary aim of this research is to investigate the use of an eyeglass-mounted wearable energy intake sensor (Automatic Ingestion Monitor v2, AIM-2) for simultaneous recognition of physical activity (PAR) and estimation of steady-state EE as compared to a traditional hip-worn device. Study data were collected from six participants performing six structured activities, with the reference EE measured using indirect calorimetry (COSMED K5) and reported as metabolic equivalents of tasks (METs). Next, a novel deep convolutional neural network-based multitasking model (Multitasking-CNN) was developed for PAR and EE estimation. The Multitasking-CNN was trained with a two-step progressive training approach for higher accuracy, where in the first step the model for PAR was trained, and in the second step the model was fine-tuned for EE estimation. Finally, the performance of Multitasking-CNN on AIM-2 attached to eyeglasses was compared to the ActiGraph GT9X (AG) attached to the right hip. On the AIM-2 data, Multitasking-CNN achieved a maximum of 95% testing accuracy of PAR, a minimum of 0.59 METs mean square error (MSE), and 11% mean absolute percentage error (MAPE) in EE estimation. Conversely, on AG data, the Multitasking-CNN model achieved a maximum of 82% testing accuracy in PAR, a minimum of 0.73 METs MSE, and 13% MAPE in EE estimation. These results suggest the feasibility of using an eyeglass-mounted sensor for both PAR and EE estimation.

Indirect calorimetry directed feeding and cycling in the older ICU population: a pilot randomised controlled trial.

BACKGROUND: Older critically ill patients experience rapid muscle loss during stay in an intensive care unit (ICU) due to physiological stress and increased catabolism. This may lead to increased ICU length of stay, delayed weaning from ventilation and persistent functional limitations. We hypothesized that with optimal nutrition and early physical therapy acting in synergism, we can reduce muscle mass loss and improve functional outcomes. METHODS: This was a prospective, single blinded randomized, controlled single-center pilot study to compare the lean muscle mass (measured at bilateral quadriceps femoris using ultrasound) of older ICU patients at 4 time points over 14 days between the control and intervention groups. The control group received standard weight-based empiric feeding and standard ICU physiotherapy. The intervention group received indirect calorimetry directed feeding adjusted daily and 60 min per day of cycle ergometry. 21 patients were recruited and randomized with 11 patients in the control arm and 10 patients in the intervention arm. Secondary outcome measures included ICU and hospital mortality, length of stay, functional assessments of mobility and assessment of strength. RESULTS: Median age was 64 in the control group and 66 in the intervention group. Median calories achieved was 24.5 kcal/kg per day in the control group and 23.3 kcal/kg per day in the intervention group. Cycle ergometry was applied to patients in the intervention group for a median of 60 min a day and a patient had a median of 8.5 sessions in 14 days. Muscle mass decreased by a median of 4.7cm2 in the right quadriceps femoris in the control group and 1.8cm2 in the intervention group (p = 0.19), while the left quadriceps femoris decreased by 1.9cm2 in the control group and 0.1cm2 in the intervention group (p = 0.51). CONCLUSION: In this pilot study, we found a trend towards decrease muscle loss in bilateral quadriceps femoris with our combined interventions. However, it did not reach statistical significance likely due to small number of patients recruited in the study. However, we conclude that the intervention is feasible and potentially beneficial and may warrant a larger scale study to achieve statistical significance. TRIAL REGISTRATION: This study was registered on Clinicaltrials.gov on 30th May 2018 with identifier NCT03540732.

Energy Expenditure Validation of an Exergame Platform: Ring Fit Adventure Use in Adults with Overweight and Obesity.

Objective: This study aims at comparing the energy expenditure (EE) and heart rate (HR) data from Ring Fit Adventure (RFA) with those from indirect calorimetry (COSMED) and a heart rate monitor (Polar FT7). A secondary goal is to evaluate self-reported enjoyment and perceived effort levels. Materials and Methods: Thirty participants (age = 21.8 ± 2.2; body mass index = 31.8 ± 4.4) were recruited for two laboratory visits. The first visit involved baseline measurements. In the second visit, participants performed a 55-minute ad libitum exercise session with the RFA in adventure mode with moderate difficulty. During this session, EE, HR, perceived effort, and enjoyment of physical activity were recorded. Results: Although no statistically significant overestimation of EE was found between the RFA and the metabolic cart, two-way analysis of variance results show a main effect of condition (RFA vs. Polar FT7) on HR (122.8 ± 20.1 bpm and 129.0 ± 18.6 bpm; P = 0.007; ᶯp2 = 0.235). Based on comprehensive statistical evaluations, including the mean absolute percent error, intraclass correlations, typical error of measurement, and limits of agreement, the data suggest that the RFA provides reliable estimates for EE and HR. Overall, participants enjoyed the game considerably (71.3 ± 5.9/80 arbitrary units), and their reported perceived exertion was low. Conclusion: This study underscores that the RFA values are relatively accurate and precise, and thus it can be safely suggested for individuals with overweight and obesity to adopt an active lifestyle.

Development and validation of a smartwatch algorithm for differentiating physical activity intensity in health monitoring.

To develop and validate a machine learning based algorithm to estimate physical activity (PA) intensity using the smartwatch with the capacity to record PA and determine outdoor state. Two groups of participants, including 24 adults (13 males) and 18 children (9 boys), completed a sequential activity trial. During each trial, participants wore a smartwatch, and energy expenditure was measured using indirect calorimetry as gold standard. The support vector machine algorithm and the least squares regression model were applied for the metabolic equivalent (MET) estimation using raw data derived from the smartwatch. Exercise intensity was categorized based on MET values into sedentary activity (SED), light activity (LPA), moderate activity (MPA), and vigorous activity (VPA). The classification accuracy was evaluated using area under the ROC curve (AUC). The METs estimation accuracy were assessed via the mean absolute error (MAE), the correlation coefficient, Bland-Altman plots, and intraclass correlation (ICC). A total of 24 adults aged 21-34 years and 18 children aged 9-13 years participated in the study, yielding 1790 and 1246 data points for adults and children respectively for model building and validation. For adults, the AUC for classifying SED, MVPA, and VPA were 0.96, 0.88, and 0.86, respectively. The MAE between true METs and estimated METs was 0.75 METs. The correlation coefficient and ICC were 0.87 (p < 0.001) and 0.89, respectively. For children, comparable levels of accuracy were demonstrated, with the AUC for SED, MVPA, and VPA being 0.98, 0.89, and 0.85, respectively. The MAE between true METs and estimated METs was 0.80 METs. The correlation coefficient and ICC were 0.79 (p < 0.001) and 0.84, respectively. The developed model successfully estimated PA intensity with high accuracy in both adults and children. The application of this model enables independent investigation of PA intensity, facilitating research in health monitoring and potentially in areas such as myopia prevention and control.

Measurement and estimation of energy in the critically ill.

PURPOSE OF REVIEW: Recent changes in guidelines recommendation during early phase of critical illness and use of indirect calorimetry. The aim of this review is to discuss methods of determining energy requirements in the critically ill and highlight factors impacting resting energy expenditure. RECENT FINDING: An appraisal of recent literature discussing indirect calorimetry guided-nutrition potential benefits or pitfalls. Recent attempts to devise strategy and pilot indirect calorimetry use in the critically ill patients requiring continuous renal replacement therapy or extracorporeal membrane oxygenation are also discussed. Additionally, we briefly touched on variability between guidelines recommended energy target and measured energy expenditure for adult critically ill patients with obesity. SUMMARY: While energy requirement in the critically ill continues to be an area of controversy, recent guidelines recommendations shift toward providing less aggressive calories during acute phase of illness in the first week of ICU.Use of indirect calorimetry may provide more accurate energy target compared to the use of predictive equations. Despite the absence of literature to support long term mortality benefits, there are many potential benefits for the use of indirect calorimetry when available.

Describing Energy Expenditure in Children with a Chronic Disease: A Systematic Review.

Understanding energy expenditure in children with chronic disease is critical due to the impact on energy homeostasis and growth. This systematic review aimed to describe available literature of resting (REE) and total energy expenditure (TEE) in children with chronic disease measured by gold-standard methods of indirect calorimetry (IC) and doubly labeled water (DLW), respectively. A literature search was conducted using OVID Medline, Embase, CINAHL Plus, Cochrane, and Scopus until July 2023. Studies were included if the mean age of the participants was ≤18 y, participants had a chronic disease, and measurement of REE or TEE was conducted using IC or DLW, respectively. Studies investigating energy expenditure in premature infants, patients with acute illness, and intensive care patients were excluded. The primary outcomes were the type of data (REE, TEE) obtained and REE/TEE stratified by disease group. In total, 271 studies across 24 chronic conditions were identified. Over 60% of retrieved studies were published >10 y ago and conducted on relatively small population sizes (n range = 1-398). Most studies obtained REE samples (82%) rather than that of TEE (8%), with very few exploring both samples (10%). There was variability in the difference in energy expenditure in children with chronic disease compared with that of healthy control group across and within disease groups. Eighteen predictive energy equations were generated across the included studies. Quality assessment of the studies identified poor reporting of energy expenditure protocols, which may limit the validity of results. Current literature on energy expenditure in children with chronic disease, although extensive, reveals key future research opportunities. International collaboration and robust measurement of energy expenditure should be conducted to generate meaningful predictive energy equations to provide updated evidence that is reflective of emerging disease-modifying therapies. This study was registered in PROSPERO as CRD42020204690.

Low respiratory quotient correlates with high mortality in patients undergoing mechanical ventilation.

OBJECTIVE: Oxygen consumption (VO2), carbon dioxide generation (VCO2), and respiratory quotient (RQ), which is the ratio of VO2 to VCO2, are critical indicators of human metabolism. To seek a link between the patient's metabolism and pathophysiology of critical illness, we investigated the correlation of these values with mortality in critical care patients. METHODS: This was a prospective, observational study conducted at a suburban, quaternary care teaching hospital. Age 18 years or older healthy volunteers and patients who underwent mechanical ventilation were enrolled. A high-fidelity automation device, which accuracy is equivalent to the gold standard Douglas Bag technique, was used to measure VO2, VCO2, and RQ at a wide range of fraction of inspired oxygen (FIO2). RESULTS: We included a total of 21 subjects including 8 post-cardiothoracic surgery patients, 7 intensive care patients, 3 patients from the emergency room, and 3 healthy volunteers. This study included 10 critical care patients, whose metabolic measurements were performed in the ER and ICU, and 6 died. VO2, VCO2, and RQ of survivors were 282 +/- 95 mL/min, 202 +/- 81 mL/min, and 0.70 +/- 0.10, and those of non-survivors were 240 +/- 87 mL/min, 140 +/- 66 mL/min, and 0.57 +/- 0.08 (p = 0.34, p = 0.10, and p < 0.01), respectively. The difference of RQ was statistically significant (p < 0.01) and it remained significant when the subjects with FIO2 < 0.5 were excluded (p < 0.05). CONCLUSIONS: Low RQ correlated with high mortality, which may potentially indicate a decompensation of the oxygen metabolism in critically ill patients.

Impact of Equation Choice on Resting Metabolic Rate Ratio in High-Level Men and Women Athletes.

OBJECTIVE: To examine the impact of the RMR ratio cutoff point selected on the categorization of prevalence/absence of low energy availability among predictive equations in high-level athletes (n = 241 [99 women]; 52% competed at the World Championship and Olympic Games), and whether this categorization is influenced by sex and the predictive equation used. METHODS: We assessed RMR using indirect calorimetry, predicted the RMR using the equations proposed by Harris-Benedict, FAO/WHO/UNU, de Lorenzo, ten Haaf and Wejis, Wong, Jagim, Cunningham, and Freire, and computed the RMR ratio for each equation. RESULTS: We observed that the cumulative percentage of RMR ratio values increased at a faster rate using Jagim, ten Haaf and Wejis, and Cunningham equations compared to the other equations. At the 0.90 value (the most used cutoff point in literature), the Jagim equation categorized ≥ 50% of the athletes into "low energy availability". No Sex × Equation × Sport interaction effect was observed (F = 0.10, p = 1.0). There was a significant main effect to Sex (F = 11.7, p < 0.001, ES = 0.05), Sport (F = 16.4, p < 0.001, ES = 0.01), and Equation (F = 64.1, p < 0.001, ES = 0.19). Wong and FAO/WHO/UNU equations yielded the largest errors (assessed vs. predicted RMR) in men and women, respectively. CONCLUSION: The selected RMR ratio cutoff point influences the prevalence/absence of low energy availability characterization in high-level athletes and suggests that certain equations could bias its assessment.

An evaluation of intensive care nurses' performance of indirect calorimetry measurements.

BACKGROUND: Indirect calorimetry (IC) is the gold standard to monitor energy expenditure in critically ill patients. In several intensive care units (ICUs), nurses are responsible for carrying out the measurements. AIM: The aim of this study was to assess nurses' perception of their involvement in IC. STUDY DESIGN: This was a prospective survey conducted in the surgical ICU of a French university hospital after 18 months of use of the Q-NRG + ® calorimeter (COSMED©, Italy). All nurses who have used the calorimeter in the previous 6 months in this ICU were questioned through a questionnaire about their theoretical and practical knowledge and experience in using it. RESULTS: The participation rate was 93% (28/30 surveyed). All the respondents understood the objectives of performing an IC and 23 of them (82%) had used the device at least once in the previous 6 months. All the users thought it was pertinent that ICU nurses were in charge of the IC measurements, 16 of them (70%) reported having been formally trained, mostly by a colleague, and 17 (77%) felt comfortable with the device after 2 to 5 uses. The five non-users (8%) did not have the opportunity to do so. Theoretical and practical knowledge could be improved as only 5 of the users (22%) declared to know the main criteria of reliability of the IC measurement and 4 of them (18%) declared to know the maintenance and cleaning protocol of the device. CONCLUSION: Nurses quickly felt comfortable with the Q-NRG + ® in this ICU. Formal initial and ongoing training of all staff completing IC is essential to perform IC measurements safely and to obtain reliable and interpretable results in practice. RELEVANCE TO CLINICAL PRACTICE: Involving the nursing team in nutritional care, even if it is technical, seems to bring satisfaction in terms of overall patient care.

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 in critically ill children with acute kidney injury on continuous kidney replacement therapy: a 2023 executive summary.

OBJECTIVES: Nutrition plays a vital role in the outcome of critical illness in children, particularly those with acute kidney injury. Currently, there are no established guidelines for children with acute kidney injury treated with continuous kidney replacement therapy. Our objective was to create clinical practice points for nutritional assessment and management in critically ill children with acute kidney injury receiving continuous kidney replacement therapy. METHODS: An electronic search using PubMed and an inclusive academic library search (including MEDLINE, Cochrane, and Embase databases) was conducted to find relevant English-language articles on nutrition therapy for children (<18 y of age) receiving continuous kidney replacement therapy. RESULTS: The existing literature was reviewed by our work group, comprising pediatric nephrologists and experts in nutrition. The modified Delphi method was then used to develop a total of 45 clinical practice points. The best methods for nutritional assessment are discussed. Indirect calorimetry is the most reliable method of predicting resting energy expenditure in children on continuous kidney replacement therapy. Schofield equations can be used when indirect calorimetry is not available. The non-intentional calories contributed by continuous kidney replacement therapy should also be accounted for during caloric dosing. Protein supplementation should be increased to account for the proteins, peptides, and amino acids lost with continuous kidney replacement therapy. CONCLUSIONS: Clinical practice points are provided on nutrition assessment, determining energy needs, and nutrient intake in children with acute kidney injury and on continuous kidney replacement therapy based on the existing literature and expert opinions of a multidisciplinary panel.