Estimating Energy Expenditure in Critically Ill Children: Still Shooting in the Dark?

Smallwood, Craig D; Mehta, Nilesh M.

doi:10.1016/j.jpeds.2017.01.061

Cited by 5 publications

(4 citation statements)

References 7 publications

(6 reference statements)

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“…We have described for the first time an ANN-based REE predictive model to offer insight into the potential of machine learning to provide a valid and accurate REE prediction in critically ill children. Machine learning may offer a new opportunity to explore the complexity of metabolic changes in different physiologic and pathologic conditions [22]. The use of ANN models to predict REE has been found to be reliable in healthy children and adults, including obese patients [7,23].…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2021

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“…First, a new validated device is needed to measure REE by IC. In the pediatric population with low lung volumes and flow rates, accurate measurement of flow is crucial . In addition to IC, other methods, such as the doubly labelled water method and direct calorimetry, have been rarely used.…”

Section: Discussionmentioning

confidence: 99%

“…In parallel, because IC is not practical in many patients, more precise predictive equations for REE need to be developed. As recommended recently, validation methodology for new predictive models must be robust and consistent . When developing future predictive equations, the specific metabolism of ventilated critically ill children must be taken into account.…”

Section: Discussionmentioning

confidence: 99%

“…However, this method is time‐consuming and requires trained staff and specialized equipment. One practical limitation is the lack of an adequately validated indirect calorimeter for the PICU population . A recent worldwide survey conducted in 156 PICUs among 52 countries as well as European surveys showed that only 14%–17% of PICUs had access to this device .…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Resting Energy Expenditure Using Predictive Equations in Critically Ill Children: Results of a Systematic Review

Chaparro

Moullet

Taffé

et al. 2018

J Parenter Enteral Nutr

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Provision of adequate energy intake to critically ill children is associated with improved prognosis, but resting energy expenditure (REE) is rarely determined by indirect calorimetry (IC) due to practical constraints. Some studies have tested the validity of various predictive equations that are routinely used for this purpose, but no systematic evaluation has been made. Therefore, we performed a systematic review of the literature to assess predictive equations of REE in critically ill children. We systematically searched the literature for eligible studies, and then we extracted data and assigned a quality grade to each article according to guidelines of the Academy of Nutrition and Dietetics. Accuracy was defined as the percentage of predicted REE values to fall within ±10% or ±15% of the measured energy expenditure (MEE) values, computed based on individual participant data. Of the 993 identified studies, 22 studies testing 21 equations using 2326 IC measurements in 1102 children were included in this review. Only 6 equations were evaluated by at least 3 studies in critically ill children. No equation predicted REE within ±10% of MEE in >50% of observations. The Harris-Benedict equation overestimated REE in two-thirds of patients, whereas the Schofield equations and Talbot tables predicted REE within ±15% of MEE in approximately 50% of observations. In summary, the Schofield equations and Talbot tables were the least inaccurate of the predictive equations. We conclude that a new validated indirect calorimeter is urgently needed in the critically ill pediatric population.).

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Personalized nutrition approach in pediatrics: a narrative review

et al. 2020

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Estimating Energy Expenditure in Critically Ill Children: Still Shooting in the Dark?

Abstract: Serum tenascin-C as a novel predictor for risk of coronary artery lesion and resistance to intravenous immunoglobulin in Kawasaki disease: a multicenter retrospective study.

Cited by 5 publications

References 7 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

Estimation of Resting Energy Expenditure Using Predictive Equations in Critically Ill Children: Results of a Systematic Review

Personalized nutrition approach in pediatrics: a narrative review

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