Predictive equations versus measured energy expenditure by indirect calorimetry: A retrospective validation

Zusman, Oren; Kagan, Ilya; Bendavid, Itai; Theilla, Miriam; Cohen, Jonathan; Singer, Pierre

doi:10.1016/j.clnu.2018.04.020

Cited by 87 publications

(84 citation statements)

References 31 publications

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“…Our study found that the REE estimated by the Harris and Benedict formula [19] was signi cantly lower than that measured by IC. This nding corroborates the indication not to use this formula in critically ill patients and in patients with AKI [11,14,20,21] and the need to propose a new equation for those AKI on dialysis [21][22][23][24][25].…”

Section: Discussionsupporting

confidence: 74%

Proposal of a new equation for estimating resting energy expenditure for acute kidney injury patients on dialysis. A Machine Learning Approach.

Ponce

Góes

Andrade

2020

Preprint

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The objective of this study was to develop a new predictive equation of resting energy expenditure (REE) for acute kidney injury patients (AKI) on dialysis. Material and methods: A 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 (RMSE). Results: There were 364 indirect calorimetry (IC) 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 (BMI), use of vasopressors, expiratory positive airway pressure, minute volume (MV), C-reactive protein, temperature and serum urea. The final r-value in the validation set was 0.69. Conclusion: We 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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Section: Discussionsupporting

confidence: 74%

Proposal of a new equation for estimating resting energy expenditure for acute kidney injury patients on dialysis. A Machine Learning Approach.

Ponce

Góes

Andrade

2020

Preprint

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“…The weakness of predictive equations and the use of indirect calorimetry have been subject to multiple evaluations and recommendations from ESPEN [2] and ASPEN [41], both preferring the use of indirect calorimetry to evaluate ICU patient needs (rated a very weak recommendation by ASPEN). The predictive equations are associated with significant inaccuracy (up to 60%), leading to over or under evaluation of the needs and inducing over or underfeeding [118]. Numerous meta-analyses have demonstrated the poor value of predictive equations [119,120], variability that is increased because body weight remains a value difficult to accurately assess [121].…”

Section: Statementmentioning

confidence: 99%

ESPEN guideline on clinical nutrition in the intensive care unit

et al. 2019

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s u m m a r yFollowing the new ESPEN Standard Operating Procedures, the previous guidelines to provide best medical nutritional therapy to critically ill patients have been updated. These guidelines define who are the patients at risk, how to assess nutritional status of an ICU patient, how to define the amount of energy to provide, the route to choose and how to adapt according to various clinical conditions. When to start and how to progress in the administration of adequate provision of nutrients is also described. The best determination of amount and nature of carbohydrates, fat and protein are suggested. Special attention is given to glutamine and omega-3 fatty acids. Particular conditions frequently observed in intensive care such as patients with dysphagia, frail patients, multiple trauma patients, abdominal surgery, sepsis, and obesity are discussed to guide the practitioner toward the best evidence based therapy. Monitoring of this nutritional therapy is discussed in a separate document.

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“…Caloric targeting in critically ill patients most often depends on predictive equations. However, a formula-based nutritional approach has no more value than flipping a coin and inevitably leads to under-or overestimation of calorie needs [10,11]. A worldwide audit in 10,000 patients from 880 ICUs showed poor adherence to nutrition guidelines.…”

Section: Caloriesmentioning

confidence: 99%

Nutrition in Sepsis: A Bench-to-Bedside Review

2020

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Nutrition therapy in sepsis is challenging and differs from the standard feeding approach in critically ill patients. The dysregulated host response caused by infection induces progressive physiologic alterations, which may limit metabolic capacity by impairing mitochondrial function. Hence, early artificial nutrition should be ramped-up and emphasis laid on the post-acute phase of critical illness. Caloric dosing is ideally guided by indirect calorimetry, and endogenous energy production should be considered. Proteins should initially be delivered at low volume and progressively increased to 1.3 g/kg/day following shock symptoms wane. Both the enteral and parenteral route can be (simultaneously) used to cover caloric and protein targets. Regarding pharmaconutrition, a low dose glutamine seems appropriate in patients receiving parenteral nutrition. Supplementing arginine or selenium is not recommended. High-dose vitamin C administration may offer substantial benefit, but actual evidence is too limited for advocating its routine use in sepsis. Omega-3 polyunsaturated fatty acids to modulate metabolic processes can be safely used, but non-inferiority to other intravenous lipid emulsions remains unproven in septic patients. Nutrition stewardship, defined as the whole of interventions to optimize nutritional approach and treatment, should be pursued in all septic patients but may be difficult to accomplish within a context of profoundly altered cellular metabolic processes and organ dysfunction caused by time-bound excessive inflammation and/or immune suppression. This review aims to provide an overview and practical recommendations of all aspects of nutritional therapy in the setting of sepsis.

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Predictive equations versus measured energy expenditure by indirect calorimetry: A retrospective validation

Cited by 87 publications

References 31 publications

Proposal of a new equation for estimating resting energy expenditure for acute kidney injury patients on dialysis. A Machine Learning Approach.

Proposal of a new equation for estimating resting energy expenditure for acute kidney injury patients on dialysis. A Machine Learning Approach.

ESPEN guideline on clinical nutrition in the intensive care unit

Nutrition in Sepsis: A Bench-to-Bedside Review

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