Artificial Neural Network Algorithms to Predict Resting Energy Expenditure in Critically Ill Children

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 VCO₂-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%; R² = 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%, R² = 0.80) and comparable to the Mehta equation. Including IC-measured VCO₂ 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.