Energy expenditure in critically ill children

Veldscholte, Karlien; Joosten, Koen; Chaparro, Corinne Jotterand

doi:10.21037/pm-20-62

Cited by 7 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The fact that in both models, CRP was selected as a meaningful variable is consistent with the notion that CRP is an indicator of illness severity and acute metabolic response and inflammation. The selection of body temperature among the variables for the model with no gas values is an important notion as body temperature importantly influences REE, with studies showing a positive correlation with factors per degree Celsius ranging from 6% to 8% [24]. Finally, the inclusion of blood glucose among the variables selected by TWIST for the VCO 2 model could be relevant, given that glucose abnormalities (especially hyperglycemia) are common adverse events in the critical care setting, which can result from the endogenous response to acute illness, regulated by different hormones (e.g., glucagon, cortisol, growth hormone, catecholamine, insulin), but could also be influenced by many medications [25].…”

Section: Discussionmentioning

confidence: 99%

Artificial Neural Network Algorithms to Predict Resting Energy Expenditure in Critically Ill Children

et al. 2021

View full text Add to dashboard Cite

Introduction: Accurate assessment of resting energy expenditure (REE) can guide optimal nutritional prescription in critically ill children. Indirect calorimetry (IC) is the gold standard for REE measurement, but its use is limited. Alternatively, REE estimates by predictive equations/formulae are often inaccurate. Recently, predicting REE with artificial neural networks (ANN) was found to be accurate in healthy children. We aimed to investigate the role of ANN in predicting REE in critically ill children and to compare the accuracy with common equations/formulae. Study methods: We enrolled 257 critically ill children. Nutritional status/vital signs/biochemical values were recorded. We used IC to measure REE. Commonly employed equations/formulae and the VCO2-based Mehta equation were estimated. ANN analysis to predict REE was conducted, employing the TWIST system. Results: ANN considered demographic/anthropometric data to model REE. The predictive model was good (accuracy 75.6%; R2 = 0.71) but not better than Talbot tables for weight. After adding vital signs/biochemical values, the model became superior to all equations/formulae (accuracy 82.3%, R2 = 0.80) and comparable to the Mehta equation. Including IC-measured VCO2 increased the accuracy to 89.6%, superior to the Mehta equation. Conclusions: We described the accuracy of REE prediction using models that include demographic/anthropometric/clinical/metabolic variables. ANN may represent a reliable option for REE estimation, overcoming the inaccuracies of traditional predictive equations/formulae.

show abstract

Section: Discussionmentioning

confidence: 99%

Artificial Neural Network Algorithms to Predict Resting Energy Expenditure in Critically Ill Children

et al. 2021

View full text Add to dashboard Cite

show abstract

“…We did not assess physical activity (PA), which is highly variable. In hospitalized patients with illness, REE may mirror TEE due to minimal physical activity 3 , 33 . However, during the outpatient phase when children return to their home environment, TEE increases due to heightened physical activity.…”

Section: Discussionmentioning

confidence: 99%

“…In resource-limited settings, malnutrition is common in children hospitalized for an acute illness 1 and is associated with case fatality ratios of 8–25%, despite antibiotic treatment and protocolized feeding 2 . Children commonly enter a downward cycle where illness worsens their malnutrition and vice versa due to enhanced metabolic needs, which negatively impacts clinical outcomes 3 . Acutely ill children with the most severe forms of malnutrition are managed according to guidelines developed by the World Health Organization (WHO) 4 , 5 .…”

Section: Introductionmentioning

confidence: 99%

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

Afroze,

Khoshnevisan,

Harawa

et al. 2024

Sci Rep

View full text Add to dashboard Cite

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.

show abstract

“…Data files comprising initial configurations of fully stretched Kremer–Grest polymer brushes in rectangular boxes at various grafting densities are generated using a Python script, made available online …”

Section: Model and Methodsmentioning

confidence: 99%

Pressure Anisotropy in Polymer Brushes and Its Effects on Wetting

Veldscholte,

Snoeijer,

den Otter

et al. 2024

Langmuir

View full text Add to dashboard Cite

Polymer brushes, coatings consisting of densely grafted macromolecules, experience an intrinsic lateral compressive pressure, originating from chain elasticity and excluded volume interactions. This lateral pressure complicates a proper definition of the interface and, thereby, the determination and interpretation of the interfacial tension and its relation to the wetting behavior of brushes. Here, we study the link among grafting-induced compressive lateral pressure in polymer brushes, interfacial tension, and brush wettability using coarse-grained molecular dynamics simulations. We focus on grafting densities and polymer−liquid affinities such that the polymer and liquid do not tend to mix. For these systems, a central result is that the liquid contact angle is independent of the grafting density, which implies that the graftinginduced lateral compressive pressure in the brush does not influence its wettability. Although the definition of brush interfacial tensions is complicated by the grafting-induced pressure, the difference in the interfacial tension between wet and dry brushes is perfectly well-defined. We confirm explicitly from Young's law that this difference offers an accurate description of the brush wettability. We then explore a method to isolate the grafting-induced contribution to the lateral pressure, assuming the interfacial tension is independent of grafting density. This scenario indeed allows disentanglement of interfacial and grafting effects for a broad range of parameters, except close to the mixing point. We separately discuss the latter case in light of autophobic dewetting.

show abstract

Energy expenditure in critically ill children

Cited by 7 publications

References 0 publications

Artificial Neural Network Algorithms to Predict Resting Energy Expenditure in Critically Ill Children

Artificial Neural Network Algorithms to Predict Resting Energy Expenditure in Critically Ill Children

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

Pressure Anisotropy in Polymer Brushes and Its Effects on Wetting

Contact Info

Product

Resources

About