An artificial neural network to predict resting energy expenditure in obesity. Issue 5 (October 2018)

• Record Type: Journal Article
• Authors: Disse, Emmanuel; Ledoux, Séverine; Bétry, Cécile; Caussy, Cyrielle; Maitrepierre, Christine; Coupaye, Muriel; Laville, Martine; Simon, Chantal
• Is Part Of: Clinical nutrition. Volume 37:Issue 5(2018)
• Page Start: 1661
• Page End: 1669
• Publication Date: 2018-10
• Subjects: Obesity -- Resting energy expenditure -- Indirect calorimetry -- Artificial neural network; ANN artificial neuronal network -- FFM fat-free mass -- MAPE mean absolute percentage error -- PP predictive precision -- MLP multi-layer perceptron -- REE resting energy expenditure -- RMSPE root mean squared prediction error -- USA DRI USA dietary reference intakes; Critically ill -- Nutrition -- Periodicals; Diet therapy -- Periodicals; Parenteral feeding -- Periodicals; Enteral feeding -- Periodicals; Enteral Nutrition -- Periodicals; Parenteral Nutrition -- Periodicals; Metabolism -- Periodicals; Diétothérapie -- Périodiques; Alimentation parentérale -- Périodiques; Alimentation entérale -- Périodiques; Nutrition -- Périodiques; Diet therapy; Enteral feeding; Nutrition; Parenteral feeding; Electronic journals; Periodicals; Electronic journals; 615.854
• DOI: 10.1016/j.clnu.2017.07.017
• Languages: English
• ISSNs: 0261-5614

Abstract

Summary: Background & aims: The resting energy expenditure (REE) determination is important in nutrition for adequate dietary prescription. The gold standard i.e. indirect calorimetry is not available in clinical settings. Thus, several predictive equations have been developed, but they lack of accuracy in subjects with extreme weight including obese populations. Artificial neural networks (ANN) are useful predictive tools in the area of artificial intelligence, used in numerous clinical fields. The aim of this study was to determine the relevance of ANN in predicting REE in obesity. Methods: A Multi-Layer Perceptron (MLP) feed-forward neural network with a back propagation algorithm was created and cross-validated in a cohort of 565 obese subjects (BMI within 30–50 kg m −2 ) with weight, height, sex and age as clinical inputs and REE measured by indirect calorimetry as output. The predictive performances of ANN were compared to those of 23 predictive REE equations in the training set and in two independent sets of 100 and 237 obese subjects for external validation. Results: Among the 23 established prediction equations for REE evaluated, the Harris & Benedict equations recalculated by Roza were the most accurate for the obese population, followed by the USA DRI, Müller and the original Harris & Benedict equations. The final 5-fold cross-validated three-layer 4-3-1 feed-forward back propagation ANN model developed in that study improved precision and accuracy of REE prediction over linear equations (precision = 68.1%, MAPE = 8.6% and RMSPE = 210 kcal/d), independently from BMI subgroups within 30–50 kg m −2 . External validation confirmed the better predictive performances of ANN model (precision = 73% and 65%, MAPE = 7.7% and 8.6%, RMSPE = 187 kcal/d and 200 kcal/d in the 2 independent datasets) for the prediction of REE in obese subjects. Conclusions: We developed and validated an ANN model for the prediction of REE in obese subjects that is more precise and accurate than established REE predictive equations independent from BMI subgroups. For convenient use in clinical settings, we provide a simple ANN-REE calculator available at:https://www.crnh-rhone-alpes.fr/fr/ANN-REE-Calculator . Highlights: To determine REE is of great interest to manage obesity. Established prediction equations are poorly reliable to predict REE in obese subjects. Artificial neural network is more accurate to predict REE. We provide a simple calculator available online.