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

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 o...

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Autores principales:	Giulia C. I. Spolidoro, Veronica D’Oria, Valentina De Cosmi, Gregorio Paolo Milani, Alessandra Mazzocchi, Alireza Akhondi-Asl, Nilesh M. Mehta, Carlo Agostoni, Edoardo Calderini, Enzo Grossi
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	energy expenditure metabolism nutrition children pediatrics critical care Nutrition. Foods and food supply TX341-641
Acceso en línea:	https://doaj.org/article/dea44dbd8ee1489487fc373d4b760e2e
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spelling	oai:doaj.org-article:dea44dbd8ee1489487fc373d4b760e2e2021-11-25T18:34:10ZArtificial Neural Network Algorithms to Predict Resting Energy Expenditure in Critically Ill Children10.3390/nu131137972072-6643https://doaj.org/article/dea44dbd8ee1489487fc373d4b760e2e2021-10-01T00:00:00Zhttps://www.mdpi.com/2072-6643/13/11/3797https://doaj.org/toc/2072-6643Introduction: 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 VCO<sub>2</sub>-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%; R<sup>2</sup> = 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%, R<sup>2</sup> = 0.80) and comparable to the Mehta equation. Including IC-measured VCO<sub>2</sub> 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.Giulia C. I. SpolidoroVeronica D’OriaValentina De CosmiGregorio Paolo MilaniAlessandra MazzocchiAlireza Akhondi-AslNilesh M. MehtaCarlo AgostoniEdoardo CalderiniEnzo GrossiMDPI AGarticleenergy expendituremetabolismnutritionchildrenpediatricscritical careNutrition. Foods and food supplyTX341-641ENNutrients, Vol 13, Iss 3797, p 3797 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	energy expenditure metabolism nutrition children pediatrics critical care Nutrition. Foods and food supply TX341-641
spellingShingle	energy expenditure metabolism nutrition children pediatrics critical care Nutrition. Foods and food supply TX341-641 Giulia C. I. Spolidoro Veronica D’Oria Valentina De Cosmi Gregorio Paolo Milani Alessandra Mazzocchi Alireza Akhondi-Asl Nilesh M. Mehta Carlo Agostoni Edoardo Calderini Enzo Grossi Artificial Neural Network Algorithms to Predict Resting Energy Expenditure in Critically Ill Children
description	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 VCO<sub>2</sub>-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%; R<sup>2</sup> = 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%, R<sup>2</sup> = 0.80) and comparable to the Mehta equation. Including IC-measured VCO<sub>2</sub> 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.
format	article
author	Giulia C. I. Spolidoro Veronica D’Oria Valentina De Cosmi Gregorio Paolo Milani Alessandra Mazzocchi Alireza Akhondi-Asl Nilesh M. Mehta Carlo Agostoni Edoardo Calderini Enzo Grossi
author_facet	Giulia C. I. Spolidoro Veronica D’Oria Valentina De Cosmi Gregorio Paolo Milani Alessandra Mazzocchi Alireza Akhondi-Asl Nilesh M. Mehta Carlo Agostoni Edoardo Calderini Enzo Grossi
author_sort	Giulia C. I. Spolidoro
title	Artificial Neural Network Algorithms to Predict Resting Energy Expenditure in Critically Ill Children
title_short	Artificial Neural Network Algorithms to Predict Resting Energy Expenditure in Critically Ill Children
title_full	Artificial Neural Network Algorithms to Predict Resting Energy Expenditure in Critically Ill Children
title_fullStr	Artificial Neural Network Algorithms to Predict Resting Energy Expenditure in Critically Ill Children
title_full_unstemmed	Artificial Neural Network Algorithms to Predict Resting Energy Expenditure in Critically Ill Children
title_sort	artificial neural network algorithms to predict resting energy expenditure in critically ill children
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/dea44dbd8ee1489487fc373d4b760e2e
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Artificial Neural Network Algorithms to Predict Resting Energy Expenditure in Critically Ill Children

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