Clinical Guide for the Use of Metabolic Carts

Singer, Pierre; Singer, Joelle

doi:10.1177/0884533615622536

Cited by 50 publications

(29 citation statements)

References 89 publications

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“…11 Poor reimbursement by insurance for tests performed has also been considered a barrier to using IC. 8 As a result, predictive equations have been recommended as surrogates for IC, despite accuracies which range from 0% to 77%. 15 Unfortunately, those healthcare professionals who do not receive a strong education in nutrition or are not familiar with critical care medicine do not recognize these formulae were created based on healthy individuals.…”

Section: Discussionmentioning

confidence: 99%

“…These patients cannot undergo IC as these variables may cause inaccuracies in the measurements. 8 The clinical information used in this study originated from patients' electronic medical record (EMR) and consisted of age (years), gender (female, male), height, weight, body mass index (BMI), minute ventilation, temperature (in °C), and heart rate (beats per minute). The patient's height used in the study was either recumbent length or reported height.…”

Section: Methodsmentioning

confidence: 99%

“…However, its use in daily clinical application is not common because indirect calorimeters are not widely available. 1 As a result, several predictive equations have been developed to estimate energy requirements, such as the Harris-Benedict equation (HBE), the Mifflin-St Jeor equation (MSJ), the Penn State University equation (PSU), and the American Society for Parenteral and Enteral Nutrition-Society of Critical Care Medicine (ASPEN-SCCM) Guidelines weight-based equation (WBE) [2][3][4][5][6][7][8] (table 1).…”

Section: Introductionmentioning

confidence: 99%

“…However, the accuracy rates of these equations are variable at best since overestimation or underestimation of caloric needs may be off by 40%, leading to overfeeding or underfeeding. 8 The purpose of this study is to determine the accuracy of predictive equations (ie, HBE, MSJ, PSU, and WBE) that are commonly used for calculating energy needs for critically ill, ventilated patients as compared with the mREE that is measured through IC, to demonstrate the unpredictable inaccuracy of predictive equations for those who may be less familiar with the use of indirect calorimeters. This study is unique in that it compares IC directly with the HBE as well as MSJ, PSU, and WBE formulae.…”

Section: Introductionmentioning

confidence: 99%

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Throwing darts in ICU: how close are we in estimating energy requirements?

Grguric¹,

Musillo

et al. 2020

Trauma Surg Acute Care Open

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BackgroundIndirect calorimetry (IC) is the gold standard for determining energy requirement. Due to lack of availability in many institutions, predictive equations are used to estimate energy requirements. The purpose of this study is to determine the accuracy of predictive equations (ie, Harris-Benedict equation (HBE), Mifflin-St Jeor equation (MSJ), and Penn State University equation (PSU)) used to determine energy needs for critically ill, ventilated patients compared with measured resting energy expenditure (mREE).MethodsThe researchers examined data routinely collected as part of clinical care for patients within intensive care units (ICUs). The final sample consisted of 68 patients. All studies were recorded during a single inpatient stay within an ICU.ResultsPatients, on average, had an mREE of 33.9 kcal/kg using IC. The estimated energy requirement when using predictive equations was 24.8 kcal/kg (HBE×1.25), 24.0 kcal/kg (MSJ×1.25), and 26.8 kcal/kg (PSU).DiscussionThis study identified significant differences between mREE and commonly used predictive equations in the ICU.Level of evidenceIII.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Throwing darts in ICU: how close are we in estimating energy requirements?

Grguric¹,

Musillo

et al. 2020

Trauma Surg Acute Care Open

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“…9 However, in this study, IC was performed in patients during RRT and IC guidelines contraindicate this moment for the measurement of EE, due to possible interferences of the dialysis procedure in gas exchange, which would lead to errors in the measurement of EE using IC. 5,10,11 The present study aims to evaluate the daily variability of REE measured by IC in patients with AKI and dialytic indication and to identify the clinical variables associated with REE.…”

Section: Introductionmentioning

confidence: 99%

Daily variability of resting energy expenditure in acute kidney injury patients on dialysis

Góes¹,

Sanches²,

Balbi³

et al. 2017

Jornal Brasileiro De Nefrologia

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Variabilidade diária do gasto energético de repouso em pacientes com lesão renal aguda em tratamento dialítico Introdução: É imprescindível a correta estimativa do gasto energético de repouso (GER), que pode apresentar considerável variação diária no paciente crítico com lesão renal aguda (LRA). Objetivo: Avaliar a variabilidade diária do GER medido por calorimetria indireta (CI) em pacientes com LRA e indicação dialítica e identificar as variáveis clínicas associadas ao GER. Métodos: O GER foi medido no dia da indicação do procedimento dialítico e nos quatro dias subsequentes. Também foram avaliados parâmetros que podem influenciar o GER. As diferenças diárias foram analisadas pelo modelo linear generalizado para medidas repetidas, com distribuição gama, além da correlação de Spearman e regressão linear múltipla. Resultados: Foram 301 medidas de CI realizadas em 114 pacientes, com idade de 60,65 ± 16,9 anos e 68,4% do sexo masculino. O GER médio foi de 2081 ± 645 Kcal, com aumento no dia 5 (2270 ± 556 Kcal), quando comparado aos dias 2 e 3 (2022 ± 754; 2022 ± 660 kcal, respectivamente, p = 0,04); quando normalizado para peso, não houve diferença significante no GER (kcal/kg/dia) durante o acompanhamento. GER correlacionou-se positivamente com temperatura corporal, contagem total de leucócitos, proteína C reativa, volume minuto (VM), fração inspirada de oxigênio (FiO 2 ), aparecimento de nitrogênio ureico (UNA), peso corporal e estatura e inversamente com idade. Após a regressão linear múltipla, somente VM, FiO 2 e peso corporal e idade se correlacionaram independentemente. Conclusão: Pacientes com LRA dialíticos apresentam GER está-vel. O GER foi associado independentemente com FiO 2 , VM, peso e idade. Assim, requisitos ventilatórios precisam ser avaliados diariamente para que alterações necessárias na prescrição dietética sejam feitas. ResumoPalavras-chave: consumo de energia; lesão renal aguda; metabolismo energético. Introduction:It is needed for nutrition prescription correct estimate of resting energy expenditure (REE), which is a challenge given the possible daily variation in critically ill patients with acute kidney injury (AKI). Objective: To evaluate the daily variability of REE measured by indirect calorimetry (IC) in patients with AKI and dialysis indication and identify clinical variables associated with REE. Methods: The REE was measured on the time of dialysis indication and the subsequent four days. We also evaluated parameters that can influence the REE. The daily differences were analyzed by generalized linear model for repeated measures. We also used Spearman correlation and multiple linear regression. Results: There were 301 IC measurements in 114 patients, mean age of 60.65 ± 16.9 years and 68.4% were male. The average REE was 2081 ± 645 kcal, rising on day 5 (2270 ± 556 kcal) compared to the days 2 and 3 (2022 ± 754; 2022 ± 660 kcal, respectively, p = 0,04). When normalized to weight, there was no significant difference in REE (kcal/kg/day) during follow-up. REE was positively correlate...

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Factors Related to the Assessment of Resting Metabolic Rate in Critically Ill Patients

Frankenfield

2018

J Parenter Enteral Nutr

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Background Predicting resting metabolic rate (RMR) in mechanically ventilated, critically ill patients is an important part of the nutrition care in such patients. Methods RMR and associated clinical data from various studies of mechanically ventilated, critically ill patients were combined, and the impact of body size, age, reason for admission, and sedation level were analyzed along with prediction methods of RMR (the American Society for Parenteral and Enteral Nutrition [ASPEN] standards and the Penn State equation). Results Among 826 measurements, trauma patients had a higher RMR than surgical and medical patients (2077 ± 290 vs 1987 ± 282 kcal/d; P < .0001). RMR was not different in sedated vs unsedated patients. Wide ranges of weight (27–374 kg) and age (18–95 years) were captured. The relationships between weight and RMR and RMR and age were curvilinear. For weight‐based ratio methods of RMR prediction, <50% of predictions were within the range in which they were designed to work. The accuracy of the Penn State equation was better in some weight categories than others. New equations based on a wider range of body weights and ages are presented. Conclusions Curvilinear functions exist for weight and age in relation to RMR, but extraordinary levels of each are required for the curve to become apparent. The ASPEN energy standards (kcal/kg body weight) fail to predict RMR because the relationship is more complex than a simple ratio. The Penn State equations are better able to model these relationships. The new versions of the equation presented here await validation.

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Clinical Guide for the Use of Metabolic Carts

Cited by 50 publications

References 89 publications

Throwing darts in ICU: how close are we in estimating energy requirements?

Throwing darts in ICU: how close are we in estimating energy requirements?

Daily variability of resting energy expenditure in acute kidney injury patients on dialysis

Factors Related to the Assessment of Resting Metabolic Rate in Critically Ill Patients

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