De Cosmi Valentina, Mazzocchi Alessandra, Milani Gregorio Paolo, Calderini Edoardo, Scaglioni Silvia, Bettocchi Silvia, D'Oria Veronica, Langer Thomas, Spolidoro Giulia C I, Leone Ludovica, Battezzati Alberto, Bertoli Simona, Leone Alessandro, De Amicis Ramona Silvana, Foppiani Andrea, Agostoni Carlo, Grossi Enzo
Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Pediatric Intermediate Care Unit, 20122, Milan, Italy.
Department of Clinical Sciences and Community Health, University of Milan, 20122, Milan, Italy.
J Clin Med. 2020 Apr 5;9(4):1026. doi: 10.3390/jcm9041026.
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 ( = 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.
用于计算儿童能量代谢的静息能量消耗（REE）预测公式的不准确性，可能导致实际热量需求被低估或高估，从而产生临床后果。本文的目的是将人工神经网络算法（ANNs）应用于REE预测。我们招募了561名健康儿童（2至17岁）。根据世界卫生组织（WHO）标准对营养状况进行分类，其中113名儿童为肥胖。使用间接测热法测量REE，并通过WHO、哈里斯-本尼迪克特、斯科菲尔德和牛津公式进行估算。人工神经网络考虑特定人体测量数据来建立REE模型。预测的平均绝对误差（均值±标准差）为95.8±80.8，并且与REE值高度相关（=0.88）。在肥胖儿童亚组中，人工神经网络的表现更好（101±91.8），不精确程度较低（5.4%）。作为一种新方法，人工神经网络可能为儿童的能量需求和体重管理提供有价值的信息。
