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.

Prediction of Resting Energy Expenditure in Children: May Artificial Neural Networks Improve Our Accuracy? / V.D. Cosmi, A. Mazzocchi, G.P. Milani, E. Calderini, S. Scaglioni, S. Bettocchi, V. D'Oria, T. Langer, G.C.I. Spolidoro, L. Leone, A. Battezzati, S. Bertoli, A. Leone, R.S.D. Amicis, A. Foppiani, C. Agostoni, E. Grossi. - In: JOURNAL OF CLINICAL MEDICINE. - ISSN 2077-0383. - 9:4(2020 Apr 05). [10.3390/jcm9041026]

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

V.D. Cosmi
Primo
;
A. Mazzocchi
Secondo
;
G.P. Milani;T. Langer;G.C.I. Spolidoro;L. Leone;A. Battezzati;S. Bertoli;A. Leone;R.S.D. Amicis;A. Foppiani;C. Agostoni
Penultimo
;
2020-04-05

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.
children; energy expenditure; metabolism; neural networks; nutrition
Settore MED/38 - Pediatria Generale e Specialistica
Settore MED/49 - Scienze Tecniche Dietetiche Applicate
Settore BIO/09 - Fisiologia
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/732555
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