Department of Clinical Sciences and Community Health, University of Milan, 20122 Milan, Italy.
Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Anestesia e Terapia Intensiva Donna-Bambino, 20122 Milan, Italy.
Nutrients. 2021 Oct 26;13(11):3797. doi: 10.3390/nu13113797.
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.
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-based Mehta equation were estimated. ANN analysis to predict REE was conducted, employing the TWIST system.
ANN considered demographic/anthropometric data to model REE. The predictive model was good (accuracy 75.6%; R = 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 = 0.80) and comparable to the Mehta equation. Including IC-measured VCO increased the accuracy to 89.6%, superior to the Mehta equation.
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.
准确评估静息能量消耗（REE）可以指导危重症儿童的最佳营养处方。间接热量测定法（IC）是测量 REE 的金标准，但应用受限。替代方法是，预测方程/公式估算的 REE 往往不够准确。最近，人工神经网络（ANN）预测 REE 被发现对健康儿童准确。我们旨在研究 ANN 在预测危重症儿童 REE 中的作用，并与常用方程/公式比较准确性。
我们纳入了 257 名危重症儿童。记录营养状况/生命体征/生化值。我们使用 IC 测量 REE。估计了常用的方程/公式和基于 VCO 的 Mehta 方程。采用 TWIST 系统进行 ANN 分析以预测 REE。
ANN 考虑了人口统计学/人体测量数据来模拟 REE。预测模型良好（准确性 75.6%；R = 0.71），但优于体重的 Talbot 表。添加生命体征/生化值后，该模型优于所有方程/公式（准确性 82.3%，R = 0.80），与 Mehta 方程相当。包含 IC 测量的 VCO 可将准确性提高到 89.6%，优于 Mehta 方程。
我们描述了使用包含人口统计学/人体测量/临床/代谢变量的模型预测 REE 的准确性。ANN 可能是 REE 估计的可靠选择，克服了传统预测方程/公式的不准确性。
