ArticleNutrients2021
Artificial Neural Network Algorithms to Predict Resting Energy Expenditure in Critically Ill Children.
Article in Nutrients, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 8 citations in OpenAlex.
- Comparative machine learning modeling of resting energy expenditure estimation in mechanically ventilated children after cardiac surgery.European journal of clinical nutrition · 2026Article
- Article
- Artificial Intelligence-Guided Artificial Nutrition in Critical Illness: Integrating Indirect Calorimetry and BIVA for Metabolic Precision.Nutrients · 2026Review
- Validation of Indirect Calorimetry in Children Undergoing Single-Limb Non-Invasive Ventilation: A Proof of Concept, Cross-Over Study.Nutrients · 2024Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 2 countries.
Funding
Abstract
introductionAccurate 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
methodsWe 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
resultsANN considered demographic/anthropometric data to model REE. The predictive model was good (accuracy 75.6%; R
conclusionsWe 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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Registered trials
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