ArticleNutrition & metabolism2020
Proposal of a new equation for estimating resting energy expenditure of acute kidney injury patients on dialysis: a machine learning approach.
Article in Nutrition & metabolism, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Nutrition support for patients with renal dysfunction in the intensive care unit: A narrative review.Nutrition in clinical practice : official publication of the American Society for Parenteral and Enteral Nutrition · 2025Review
- Evaluating the effects of continuous veno-venous hemodiafiltration on OAnnals of intensive care · 2025Article
- Resting energy expenditure and kidney disease: a narrative review.Frontiers in nutrition · 2025Review
- Machine learning models using non-linear techniques improve the prediction of resting energy expenditure in individuals receiving hemodialysis.Annals of medicine · 2023Article
- The Validity of the Energy Expenditure Criteria Based on Open Source Code through two Inertial Sensors.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
3 authors.
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Abstract
backgroundThe objective of this study was to develop a new predictive equation of resting energy expenditure (REE) for acute kidney injury patients (AKI) on dialysis. MATERIALS AND
methodsA cross-sectional descriptive study was carried out of 114 AKI patients, consecutively selected, on dialysis and mechanical ventilation, aged between 19 and 95 years. For construction of the predictive model, 80% of cases were randomly separated to training and 20% of unused cases to validation. Several machine learning models were tested in the training data: linear regression with stepwise, rpart, support vector machine with radial kernel, generalised boosting machine and random forest. The models were selected by ten-fold cross-validation and the performances evaluated by the root mean square error.
resultsThere were 364 indirect calorimetry measurements in 114 patients, mean age of 60.65 ± 16.9 years and 68.4% were males. The average REE was 2081 ± 645 kcal. REE was positively correlated with C-reactive protein, minute volume (MV), expiratory positive airway pressure, serum urea, body mass index and inversely with age. The principal variables included in the selected model were age, body mass index, use of vasopressors, expiratory positive airway pressure, MV, C-reactive protein, temperature and serum urea. The final r-value in the validation set was 0.69.
conclusionWe propose a new predictive equation for estimating the REE of AKI patients on dialysis that uses a non-linear approach with better performance than actual models.
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