Prediction of Resting Energy Expenditure in Children: May Artificial Neural Networks Improve Our Accuracy?

Valentina De Cosmi,Ludovica Leone,Thomas Langer,Silvia Bettocchi,Alessandra Mazzocchi,Ramona Silvana De Amicis,Carlo Agostoni,Silvia Scaglioni,Alberto Battezzati,Giulia C I Spolidoro,Enzo Grossi,Andrea Foppiani,Gregorio Paolo Milani,Simona Bertoli,Veronica D’Oria,Alessandro Leone,Edoardo Calderini

doi:10.3390/jcm9041026

Abstract

The inaccuracy of resting energy expenditure (REE) prediction formulae to calculate energy metabolism in children may lead to either under- or overestimated real caloric needs with clinical consequences. The aim of this paper was to apply artificial neural networks algorithms (ANNs) to REE prediction. We enrolled 561 healthy children (2–17 years). Nutritional status was classified according to World Health Organization (WHO) criteria, and 113 were obese. REE was measured using indirect calorimetry and estimated with WHO, Harris–Benedict, Schofield, and Oxford formulae. The ANNs considered specific anthropometric data to model REE. The mean absolute error (mean ± SD) of the prediction was 95.8 ± 80.8 and was strongly correlated with REE values (R2 = 0.88). The performance of ANNs was higher in the subgroup of obese children (101 ± 91.8) with a lower grade of imprecision (5.4%). ANNs as a novel approach may give valuable information regarding energy requirements and weight management in children.

Highlights

The mainstay of malnutrition management is lifestyle modification beginning in childhood [1]
Indirect calorimetry (IC) is currently considered the gold standard for resting energy expenditure (REE) measurement, its clinical use is limited across the world
Many reports found that the disagreements between formulas and indirect calorimetry (IC) method on an individual level were of such a degree that their accuracy appeared unpredictable in day-to-day practice [3]

Summary

Introduction

The mainstay of malnutrition management is lifestyle modification beginning in childhood [1]. The accurate estimate of energy requirements is the first step to achieve this aim, and in children, it is mainly based on the assessment of resting energy expenditure (REE) For this purpose, indirect calorimetry (IC) is currently considered the gold standard for REE measurement, its clinical use is limited across the world. Paucity of available calorimeters due to their costs and the related manpower, the lack of expertise in results interpretation, and of patient compliance to the exam performance are limiting factors for the application of IC in clinical practice [2]. To overcome these difficulties, several predictive equations were proposed for the estimation of REE. Many reports found that the disagreements between formulas and IC method on an individual level were of such a degree that their accuracy appeared unpredictable in day-to-day practice [3]

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Discussion

Conclusion