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


INTRODUCTION
2][3] Estimation of energy and protein requirements in healthy children is difficult because these requirements change with age, growth velocity, modifications in body composition, and physical activity. 4The task is even more complex in pediatric intensive care units (PICUs) and neonatal ICUs (NICUs), in which nutrition needs may be influenced by dynamic clinical factors, treatments, and hospital conditions. 58][9] Worldwide and European surveys [10][11][12] showed that only a minority of PICUs (7%-17%) had access to an indirect calorimeter, and as a result, clinicians estimated REE in critically ill children using a variety of predictive equations that were developed using data from healthy children. 13Furthermore, the recommended use of a validated calorimeter is challenging, as the Deltatrac II (Datex-Ohmeda, Helsinki, Finland), the reference device to determine REE, which has been validated in critically ill adults and children, 14,15 is no longer manufactured.7][18][19] In addition, these devices can only be used in children weighing >10-15 kg, which excludes much of the PICU population.Because of these constraints, there is a paucity of trials that have examined the impact of indirect calorimetry-guided energy prescription on clinical outcomes.
The doubly labeled water method measures total EE, which includes EE during activity, for ≥5 consecutive days. 4his method requires the use of isotopes and strict data collection, which may not be available in most centers and has been rarely used clinically in critically ill children. 20,21o determine protein needs, there is no simple method that can be used in daily practice.The classical method is the assessment of nitrogen balance, which requires the measurement of daily urinary nitrogen 22 and is used for research purposes.Urine collected over a shorter time may provide an estimate of 24-h nitrogen excretion but lacks accuracy.8][9] Clinical trials describing the optimal amount of protein that improves clinical outcomes have not yet been reported.
The determination of energy and protein needs in critically ill pediatric patients requires specific knowledge of the methods available to determine REE, total EE, and protein needs in terms of their accuracy and limitations.A preliminary search conducted in MEDLINE, Cochrane Database of Systematic Reviews, and Joanna Briggs Institute (JBI) Evidence Synthesis provided two systematic reviews in this field.One systematic review, published in 2018, assessed the accuracy of predictive equations for estimating REE in critically ill children. 13The second published in 2019 reviewed the patient and clinical factors associated with REE in critically ill patients, including children and neonates. 5In addition to these works, we are not aware of systematic or scoping reviews that have synthetized data on both the determination of energy and protein needs in critically ill pediatric patients.
Therefore, the objective of our 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, including medical and surgical patients.We included the available evidence and discussed the following specific topics: (1) indirect calorimetry to measure EE or other measurement methods, (2)  predictive equations to estimate REE, (3) clinical factors that may affect EE determination, and (4) methods used to determine protein needs in critically ill pediatric patients.

Review questions
The two main review questions addressed in this scoping review were as follows: 1. "Which methods used to determine energy needs have been assessed in critically ill pediatric patients, including medical and surgical patients, and what are the findings?"2. "Which methods used to determine protein needs have been assessed in critically ill pediatric patients, including medical and surgical patients, and what are the findings?" Considering the amount of scientific data on energy needs in critically ill pediatric patients, three subquestions were defined to clarify the presentation of data: a. Which  Inclusion and exclusion criteria mental and quasi-experimental study designs, including randomized controlled trials, nonrandomized controlled trials, before-and-after studies, and interrupted time-series studies.In addition, analytical observational studies, including prospective and retrospective cohort, casecontrol, and analytical cross-sectional studies, were considered for inclusion.This review also considered descriptive observational study designs, including descriptive cross-sectional studies.To provide an overview of available data, systematic reviews that met the inclusion criteria were also considered depending on the research question, including the two identified systematic reviews. 5,13Editorial abstracts from conferences, case reports, qualitative studies, text and opinion articles, reviews, and guidelines were also excluded.

METHODS
An international expert group of six members was established, composed of pediatric intensivists, dietitians, a nurse, and a methodologist, to conduct this scoping review based on the JBI methodology for scoping reviews. 25The study protocol was registered in the Open Science Framework (doi:10.17605/OSF.IO/7SVXM).

Search strategy
The search strategy focused on the identification of published studies.A 3-step search strategy was used.An initial limited search of MEDLINE PubMed was performed to identify articles published within the selected time period.We adapted full-search strategies for MEDLINE, previously developed for a systematic review on the accuracy of predictive equations in critically ill children 13 and European guidelines on nutrition support in critically ill children. 9The final full-search strategy for MEDLINE, developed in collaboration with a specialized librarian from the University of Applied Sciences and Arts of Western Switzerland Institution, Geneva, is shown in Appendix A. The search strategy, including all identified keywords and index terms, was adapted for Web of Science.In addition, the reference lists of all included sources of evidence were screened for additional studies, including references from existing systematic reviews on the topic.Studies published between January 2008 and the present (the last 15 years) in English and French were included.This limitation date was selected to focus on the most recent data, considering the addition of at least two major guidelines on this topic in the early 21st century. 26,27

Study selection
After the search, we collated and uploaded all identified citations to the reference manager software Zotero and removed duplicates.After a pilot test, two independent reviewers screened the titles and abstracts for assessment against the inclusion criteria using the Rayyan software. 28ny disagreements between the reviewers at each stage of the selection process were resolved through discussion or through an additional reviewer.Two independent reviewers assessed the full text of the selected citations based on the inclusion criteria.Reasons for exclusion of sources of evidence in the full text that did not meet the inclusion criteria were recorded.The results of the search and the study inclusion process were reported in a Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for scoping review (PRISMA-ScR) flow diagram. 29

Data extraction
Data were extracted from the studies included in the scoping review by one reviewer using a data extraction tool developed by the researchers and pretested before embarking on full data.The data extracted included specific details about the participants (number, sex, age, nutrition status, etc), concept (aim of the study, test method, reference method used for the comparison), context (PICU or NICU), study methods, and key findings relevant to the review questions and subquestions.

Data analysis and presentation
Data were presented with a narrative summary, accompanied by tabulated and/or charted results, and how the results relate to the review's objective and subquestions on energy were described.The presentation of results was organized under the main research questions and subresearch questions and included details from the data extraction table.As we included two systematic reviews 5,13 identified for subquestions 1b and 1c, we narratively compared their results with those of subsequently published studies to avoid redundant results.The results of the studies included in these two systematic reviews were presented in tables to provide an overview of the available evidence for each subquestion.A diagrammatic synthesis was provided to show the data available on the main research question and subquestions.

Included studies for the main questions and subquestions
The literature search identified 472 articles and 398 after removing duplicates.Of these, 229 were published after December 2007 and eligible for screening (Figure 1).After abstract and full-text screening, a total of 39 articles were included.The PRISMA-ScR 29 is shown in Figure 1.Most studies assessed the accuracy of predictive equations to estimate REE (n = 16; 41%) and the impact of clinical factors on EE (n = 22; 56%) (Figure 2).Tables 1-4 summarize the study characteristics and findings for the three subquestions on energy and the main question on protein.Five studies were included for both subquestions 1b and 1c on energy needs.

Methods of measurement of EE (subquestion 1a)
Only two studies were included in which REE measurement methods were assessed in critically ill children in the PICU 30,31 (Table 1).The first study compared the indirect calorimeter E-COVX (GE Healthcare, Waukesha, WI) with the Vmax Encore Metabolic Cart (Carefusion, San Diego, CA) in 19 children undergoing mechanical ventilation (mean age: 6.9 ± 5.8 years). 30he authors did not consider any of these devices as reference methods.The Bland-Altman limits of agreement were outside the clinically acceptable range and were on either side of the mean bias, with no discernible pattern.The authors concluded that these devices cannot be used interchangeably in this population.
A second study assessed the influence of different ventilator modes on carbon dioxide production (VCO 2 ), oxygen consumption (VO 2 ), respiratory quotient (RQ), and REE, measured by the E-COVX, in 11 critically ill children (mean age: 7.8 ± 4.9 years). 31No reference method has been used for the determination of REE.The VCO 2 , VO 2 , and REE measurements did not differ among the three ventilator modes; however, their SDs varied widely.There were no significant differences between repeated measurements.This group concluded that the influence of ventilator modes was not significant and that E-COVX was suitable for repeated measurements in children undergoing ventilation with stable respiratory patterns.

Methods of estimation of REE (subquestion 1b)
In total, 16 studies assessing the estimation methods of REE in critically ill pediatric patients were included: one systematic review published in 2018, 13 six studies already included in the systematic review, and six published thereafter, as shown in Table 2.They assessed either standard predictive equations, equations developed specifically for critically ill children, or a novel simplified metabolic equation that uses a measured VCO 2 value and a fixed RQ of 0.89. 41he systematic review included 1102 critically ill children from 22 studies, most of whom measured REE using Deltatrac II. 13 The Schofield equations 6 and Talbot tables, 65 which were the least inaccurate, predicted REE within ±15% of the measured REE in ~50% of the observations, and the Schofield equations predicted REE within ±10% in 34%-38% of the observations.The Harris-Benedict (HB) 66 equation overestimated REE in two-thirds of the patients.The authors concluded that a newly validated indirect calorimeter and more precise predictive equations were urgently needed in this population.
Studies published after 2018 have reported similar findings, although none measured REE using the Deltatrac II but used other devices, including the Vmax 29, the E-COVX, the Quark RMR, and the AMIS 2000 (Table 2).In 40 children with severe sepsis (median age: 7 years), the FAO/WHO/UNU 22 and Schofield equations estimated REE within ±10% of the measured REE in 30% of observations and HB in 25%, with underestimation or overestimation of REE. 37In 107 children undergoing cardiopulmonary bypass (median age: 5 months), all tested equations overestimated REE by adding a stress factor of 1.2 to the predicted values. 36The FAO/WHO/ UNU and Schofield equations and an allometric equation with an RQ of 0.8 had the least discrepancy, and the HB equation and dietary reference intake had the largest overestimation.In 95 children undergoing ventilation (median age: 17 months), all tested equations underestimated REE except for the FAO/WHO/UNU equation. 35The Schofield equation, with weight and height, showed the best performance for the entire group and subgroups according to age, sex, nutrition status, and organ failure.Among 16 tested equations in 153 children (median age: 7.5 years), 32 the majority, especially the Recommended Dietary Allowances, overestimated REE measured by E-COVX.In contrast to the above findings, the Schofield weight and height equation overestimated REE with one of the highest bias, and the HB equations had a smaller bias but still had wide limits of agreement.
Several studies have assessed the equations of White et al. 67 and Meyer et al. 44 developed specifically for critically ill children undergoing ventilation and observed that they did not perform better than standard predictive equations, or worse. 32,39,45Among the three equations developed by Meyer et al., one (equation A) performed better in two studies 35,44 but still did not predict REE within a clinically accepted range.
Several studies 13,33,34,[38][39][40] assessed the simplified metabolic equation, which was developed by Mehta et al. in 2015, in a derivation group of critically ill children undergoing ventilation, followed by a validation study. 41Around 50% of estimations fell within ±10% of measured REE, and the remaining observations were an underestimation of REE. 33,39,40This metabolic equation was the most accurate among several tested equations in critically ill children based on 475 and 275 indirect calorimetry measurements, respectively. 34,39The most important determinant of bias was the RQ, with a smaller error in patients with an RQ between 0.8 and 1. 40 In terms of factors that may affect the accuracy of predictive equations, two recent studies, involving one-third of overweight or obese patients, showed that apart from age, inaccuracy of the equations was not correlated to factors such as the presence of overweight or obesity, diagnostic categories including trauma, or disease severity. 32,33ctors impacting EE (subquestion 1c) In total, 22 studies assessing clinical factors that may have a role in EE determination in critically ill pediatric patients were identified.It includes one systematic review conducted in critically ill adults, children, and neonates published in 2019, 5 eight studies already included in this systematic review, and 11 studies published thereafter, as described in Table 3.
The systematic review found 95 clinical factors evaluated, with 352 evaluations performed mostly in adults. 5Based on at least two evaluations, factors in children that were positively correlated with EE included F I G U R E 2 Literature search and main findings for the main research questions and subquestions.NICU, neonatal intensive care unit; PICU, pediatric intensive care unit; SR, systematic review.
T A B L E 1 Findings related to subquestion 1a: Measurement methods of EE in critically ill pediatric patients.Similar to this systematic review, three recent studies 34,35,52 observed a positive association between weight and EE in children.One study found a positive association between age and EE. 34 recent study also observed a positive association between EE and the presence of multiorgan failure, but the causes of PICU admission and underlying disease were not correlated with EE. 35 The systematic review 5 and three recent studies 32,35,37 showed no correlation between the disease severity and EE.Most studies have reported a positive association between body temperature and EE.Day in the PICU was not correlated with EE in the systematic review, 5 similar to a recent study, 32 whereas a positive correlation was observed in another study. 52mong recent studies, one study 52 also showed that neuromuscular blockers and sedatives had a negative association with EE in critically ill pediatric patients, and three studies did not find an association. 32,35,53

Methods of determination of protein needs (main question 2)
Four studies on the methods of determining protein needs were included, [61][62][63][64] all testing various methods in different populations (Table 4).
In 2010, one study investigated the blood urea nitrogen method in 86 low-birth-weight preterm infants, and no significant association with protein intake was observed throughout time on parenteral nutrition. 64In 2011, a study evaluated the balance of phosphate and rate of creatinine excretion as a method to monitor cell catabolism in 11 children undergoing ventilation after cardiac surgery. 63Balance of phosphate was a useful and early tool for monitoring cell breakdown, whereas creatinine excretion rate is not a good marker of catabolism in children with major changes in glomerular filtration rate.
More recently, a study assessed the variability in the 24-h appearance of amino acids, representing protein and arginine metabolism, using stable isotopes in eight critically ill children. 62The 24-h protein and arginine metabolism showed high intraindividual variability in the continuously fed children.The last study published showed that a single-dose oral administration of N-glycine stable isotope with measurement of urinary end-product enrichment was a feasible and noninvasive method to investigate whole-body protein turnover. 61The protein balance obtained using the isotope and urinary urea nitrogen methods was significantly correlated.

DISCUSSION
This scoping review aimed to understand the extent and type of evidence in relation to the methods used to determine energy and protein needs in critically ill pediatric patients.Studies on the accuracy of predictive equations to estimate REE and the roles of clinical factors on EE were considerable, whereas only a few studies assessed methods of measurements of EE and of determination of protein needs.

Measurement of EE
We could only include two studies on the measurement methods of EE, which used indirect calorimetry in 30 critically ill children undergoing ventilation. 30,31The first compared two indirect calorimeters, that is, E-COVX (GE Healthcare, Datex-Ohmeda) and Vmax Encore, 30 and the second assessed the impact of ventilator modes on the values obtained with E-COVX. 31As no reference method was used, we could not draw conclusions regarding their accuracy.
The E-COVX is a gas exchange module developed to replace the Deltratrac II, which measures gas flow and concentrations via an adapter connected to the endotracheal or tracheostomy tube.The Vmax Encore is a breathby-breath method similar to the mode of action of the Deltatrac II.Gas is sampled at the humidifier and ventilator exhaust to measure O 2 and CO 2 concentrations, and flow is measured at the ventilator exhaust.
In vitro pediatric studies and adult clinical studies have assessed the accuracy of these devices.The E-COVX and another new gas exchange module, the E-sCAiOVX-00 (GE Healthcare, Datex-Ohmeda), were tested for VCO 2 and VO 2 between 20 and 100 ml/min, which corresponded to patients weighing ~5-16 kg. 39The E-COVX had wide bias and limits of agreement for VO 2 and VCO 2 that were not clinically acceptable, in contrast to the E-sCAiOVX, except for VCO 2 and VO 2 at 20 ml/ min.Similarly, studies that compared the E-COVX or M-COVX (similar to the E-COVX) with the Deltatrac II in adults undergoing ventilation showed large limits of agreements. 19,68,69These findings show the risk of inaccuracy when measuring REE with the E-COVX in critically ill children undergoing ventilation.
The Vmax Encore was tested in vitro for EE between 50 and 2000 kcal/day. 70The predicted and actual VO 2 and VCO 2 data were strongly correlated; however, the simulation model represented spontaneously breathing critically ill patients.In another pediatric simulation, the CCM Express (MGC Diagnostics, Saint Paul, MN) and the volumetric capnography device NM3 (Philips Healthcare, Eindhoven, Netherlands), which measures VCO 2 , showed a small mean bias for VCO 2 and acceptable limits of agreement. 71The simulation values of VO 2 and VCO 2 , corresponded to children older than 8 years and weighing >35 kg 72 ; therefore, future research is needed for younger children, as they are highly represented in the PICU.

Estimation of REE
For this subquestion, we found 16 eligible studies performed in the PICU, including a systematic review, 13 concluding that none of the predictive equations developed in healthy or critically ill children were satisfactory for estimating REE in the PICU.
Most studies 13,[35][36][37]39 observed that the Schofield equation, which predicted REE within ±10% of the measured REE in 30%-38% of observations only, 13,37 was one of the least inaccurate. Th direction of accuracy differed among studies, with underestimation and overestimation of REE, 13,37 mainly an underestimation, 35,46 or an overestimation.31,34,36 One study 39 showed that the bias of the Schofield equation differed along the REE range of values, resulting in the underestimation of REE in young children and the overestimation in older children, which may explain these differences.It is difficult to extrapolate the error introduced when estimating REE using this equation in critically ill children.
The FAO/WHO/UNU equation was also one of the least inaccurate equations in recent studies, but still had large limits of agreement. 32,36,37Equations specifically developed for critically ill children did not perform better than the standard equations, especially the White equation. 678][9] If not feasible, the guidelines suggest the use of the Schofield or FAO/WHO/ UNU equations, using an accurate weight.
A recent study confirmed overestimation of REE when adding a stress factor of 1.2 to the predicted REE 36 ; similarly, most included studies revealed an overestimation of REE using the HB equation and the Dietary Reference Intakes.Again, these are in line with nutrition guidelines, 7,9 which recommend not using the HB equation, not using Dietary Reference Intakes, and not adding a stress factor.Our findings do not provide guidance on which predictive equations should be used in specific subgroups of patients, including those with burns or trauma, or the increasing number of overweight or obese patients.Apart from age, no correlation was observed between these factors and the accuracy (or inaccuracy) of the predictive equations tested. 32,33he simplified metabolic equation, which uses a fixed RQ of 0.89 and a measured VCO 2, developed for critically ill children, was assessed by several studies, 13,33,34,[38][39][40] which observed that ~50% of measurements fell within ±10% of measured REE. 32,40This method, which is neither a simple predictive equation of REE nor a classical measurement of REE, performed better than the other equations. 34,39This is promising for the future; however, this still requires an accurate measurement of VCO 2 , especially in children weighting <15 kg. 38

Clinical factors impacting EE determination
Studies that assessed the impact of clinical factors on EE are quite frequent, including a recent systematic review. 5hey assessed various factors related to the patients (age, weight, sex, respiratory rate, heart rate, etc), treatment, diseases, and PICU days.However, each of these factors has been evaluated in a few studies, and the findings were not consistent except for weight and age.
As expected, the available data showed positive correlations between weight and EE and age and EE. 5,32,34,35,44These variables are included in most, if not all, of the standard predictive equations of REE for children and adults.Among the dynamic clinical factors, there was good agreement for the severity of illness among the included studies, concluding in the absence of an association with EE. 5,35,37,44,57 Similarly, most studies did not show a correlation between PICU days and EE.The relationship between EE and diagnosis remains indeterminate, but several studies do not show an association. 32,35,57These findings may explain why predictive equations specifically developed for critically ill children, that is, the equations of Meyer et al. 45 and White et al., 67  Findings regarding sedation, neuromuscular blockade, inotropes, vasopressors, and caloric intake were not completely consistent.Most studies have observed a positive correlation between body temperature and EE and a negative association for neuromuscular blockade and sedation.These factors deserve further consideration for inclusion in predictive equations for estimating REE in critically ill pediatric patients.
These findings confirm that when estimating REE with a predictive equation in critically ill children, additional factors are not needed during the acute phase of disease on sedation and mechanical ventilation.This is in agreement with the guidelines, which recommend providing energy intake not exceeding REE during the acute phase and taking into account energy debt, physical activity, rehabilitation, and growth thereafter.

Determination methods of protein needs
We could only include four studies on the methods used to determine protein needs that assessed four different methods in various populations.These included the blood urea nitrogen method, balance of phosphate and the rate of creatinine excretion, use of stable isotopes, and administration of N-glycine stable isotopes with measurement of urinary end-product enrichment.The latter was promising in 19 older critically ill children to assess whole-body protein turnover 61 and needs further investigation in larger samples of younger children.
The classical method to determine protein needs in healthy and sick patients is the nitrogen balance method, 22 which requires an accurate assessment of both nitrogen intake and all nitrogen losses, especially urinary nitrogen losses, over several consecutive days.Only a few studies have used this method in critically ill children, 23,73 and an easier method is necessary for clinical practice and research.The recommendations for protein intake are still based on studies that measured urinary nitrogen losses and showed higher losses than reference values in healthy children.Based on these data, it is recommended to provide ≥1.5 g/kg per day and 57 kcal/kg per day to equilibrate the nitrogen balance in critically ill children. 7,9,73

Limitations of the scoping review
This scoping review has some limitations.The measurement of EE differed among the included studies.Although the studies included in the systematic reviews mainly used the Deltatrac II, more recent studies used different devices, that is, the E-COVX, E-sCAiOVX, Quark RMR, Vmax Encore, the AMIS 2000, or respiratory mass spectrometry.As none of these indirect calorimeters has been validated in critically ill children, this may affect the findings of this scoping review and explain some observed discrepancies.Nevertheless, the global findings were quite similar among studies, except for one study, which used the E-COVX and had findings contrasting with previous studies regarding the Schofield and HB equations. 32Other factors such as the included population may also explain this discrepancy.Thus, the device used should also be considered when interpreting the findings of each included study.In addition, the population of the included studies was quite heterogeneous, especially with respect to various ages and diagnoses.Finally, various studies performed in neonates on the topics were excluded from this scoping review, as they were conducted outside the NICU.As a result, most of the eligible studies were performed in the PICU and not in the NICU.

CONCLUSION
This scoping review revealed a considerable amount of data on predictive equations to estimate REE and factors that may affect EE in critically ill children, whereas data on methods to measure EE and determine protein needs were limited.No indirect calorimeters have been validated in critically ill children undergoing ventilation.Research in this area is urgently needed, but there are many challenges, including which reference method to use.Predictive equations frequently underestimate and/ or overestimate REE.The Schofield equation was the least inaccurate of the traditional equations, and the metabolic equation, which used a fixed RQ and measured VCO 2 , performed better.Apart from weight and age, no clinical factors were clearly correlated with EE and should be included in the new predictive equations without further investigation.
measurement methods of EE (either total EE or REE), including various indirect calorimeters, have been assessed in critically ill pediatric patients, and what are the findings?b.Which estimation methods of REE, including various predictive equations, have been assessed in critically ill pediatric patients and what are the findings?c.Which clinical factors play a role in EE determination in critically ill pediatric patients?
Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) flowchart showing the inclusion and exclusion of studies.Downloaded from https://aspenjournals.onlinelibrary.wiley.com/doi/10.1002/ncp.11060by Test, Wiley Online Library on [22/09/2023].See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions)on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Downloaded from https://aspenjournals.onlinelibrary.wiley.com/doi/10.1002/ncp.11060by Test, Wiley Online Library on [22/09/2023].See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions)on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License , age, anthropometrics, percentage of body surface area burned, respiratory disease, and multiorgan failure.Additional factors, including temperature, cardiopulmonary bypass, neuromuscular blockade, inotropes, vasopressors, and cytokine levels, were significantly correlated with EE in the majority but not in all measurements.In neonates, age, heart rate, and caloric intake positively correlated with EE.
Abbreviations: PICU, pediatric intensive care unit; REE, resting energy expenditure; RQ, respiratory quotient; VCO 2 , carbon dioxide production; VO 2 , oxygen consumption.NUTRITION IN CLINICAL PRACTICE | S109 19412452, 2023, S2, Downloaded from https://aspenjournals.onlinelibrary.wiley.com/doi/10.1002/ncp.11060by Test, Wiley Online Library on [22/09/2023].See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions)on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License T A B L E 2 Findings related to subquestion 1b: Estimating methods of REE in critically ill pediatric patients.weight Downloaded from https://aspenjournals.onlinelibrary.wiley.com/doi/10.1002/ncp.11060by Test, Wiley Online Library on [22/09/2023].See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions)on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Findings related to main question 2: Methods used to determine protein needs in critically ill pediatric patients.Downloaded from https://aspenjournals.onlinelibrary.wiley.com/doi/10.1002/ncp.11060by Test, Wiley Online Library on [22/09/2023].See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions)on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License in 2018 in 19 critically ill children after thoracic surgery (median age, 14 years) Abbreviations: BMI, body mass index; CRP, C-reactive protein; FAO/WHO/UNU, Food and Agriculture Organization of the United Nations/World Health Organization/United National University; HB, Harris-Benedict; Henry-HW, Henry equation using height and weight; Henry-W, Henry equation using weight; IC, indirect calorimetry; Oxford-HW, Oxford equation using height and weight; Oxford-W, Oxford equation using weight; PICU, pediatric intensive care unit; REE, resting energy expenditure; RQ, respiratory quotient; Schofield-HW, Schofield equation using height and weight; Schofield-W, Schofield equation using weight; VCO 2 , carbon dioxide production; VO T A B L E 3 Findings related to subquestion 1c: Factors that may impact EE in critically ill pediatric patients.Abbreviations: BMI, body mass index; CRP, C-reactive protein; EE, energy expenditure; GLP-1, glucagon-like peptide 1; NICU, neonatal intensive care T A B L E 4 which integrate diagnosis categories and/ or PICU days, are often more inaccurate than traditional predictive equations.Other factors, such as minute ventilation or heart rate, assessed in recent studies, could better represent the physiological state at different times of illness and be more useful in predicting EE.
OR neonatology OR neonate* OR Child or Infant or Infant, Newborn or Adolescent or Pediatrics or child* or infant* or newborn* or pediatr* or paediatr*) 5,251,666 2 (Intensive Care Units or Critical Care or Critical Illness or Critical Care Nursing or Intensive Care Units, Pediatric or Intensive Care, Neonatal or ICU* or PICU* or NICU*) OR neonatology OR neonate* OR Child or Infant or Infant, Newborn or Adolescent or Pediatrics or child* or infant* or newborn* or pediatr* or paediatr*) AND (Intensive Care Units or Critical Care or Critical Illness or Critical Care Nursing or Intensive Care Units, Pediatric or Intensive Care, Neonatal or ICU* or PICU* or NICU*) AND ((Proteins or Amino Acids or protein* or amino acid or amino acids) OR (Energy Metabolism OR Basal Metabolism OR energy expenditure OR Energy OR calori*)) AND (calorimetry OR Calorimetry, Indirect OR Calorimetry, methods* OR indirect calorimetry OR calorimeter OR equation OR doubly labelled water OR nitrogen OR urinary loss* OR chemoluminescence OR Kjeldahl OR isotope OR isotop*) NOT (review[Publication Type]) NOT (editorial[Publication Type]) Limit: Last 15 years (1 January 2008 to 30 January 2023) JOTTERAND CHAPARRO ET AL. 19412452, 2023, S2, Downloaded from https://aspenjournals.onlinelibrary.wiley.com/doi/10.1002/ncp.11060by Test, Wiley Online Library on [22/09/2023].See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions)on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License S124 |