A necessidade básica de glicose dos tecidos é da ordem de 300 g/dia, enquanto a capacidade de armazenamento na forma de glicogênio se limita a 100 g/dia. Já as reservas orgânicas de lipídios são bem maiores, constituindo, portanto, a fonte energética principal do organismo, especialmente em situações de jejum e em condições basais. A glicose fica, dessa forma, reservada aos órgãos mais nobres que dela dependem.

Assim, também durante o exercício físico de intensidade leve a moderada, os ácidos graxos são a fonte principal de energia, evitando-se a depleção dos estoques de glicogênio, o que comprometeria a performance.

Por outro lado, no exercício de alta intensidade, há aumento da disponibilidade e da oxidação de glicose, com diminuição da oxidação de lipídios. No exercício de intensidade leve a moderada, os ácidos graxos são mobilizados do tecido adiposo (periférico e intramuscular) pela lipólise e utilizados pelo sistema musculoesquelético.

min)x3,941+(VCO2 min)x1,11-(NU g/L)x2,17]x1440 min/dia

Equação de Weir modificada

0,71 ÁCIDOS GRAXOS

0,81 PROTEINAS

1,00 CARBOHIDRATOS

0,69 METABOLISMO CETÔNICO

1,30 LIPOGÊNEGE DERIVADA DE CARBOHIDRATOS OU HIPERVENTILAÇÃO

<0,69 SUGERE VAZAMENTO

UM AUMENTO NO QR PODE SIGNIFICAR APENAS QUANTIDADE EXCESSIVA DE ALIMENTO LEVANDO A DESCONFORTO RESPIRATÓRIO

Abstract

PURPOSE OF REVIEW:

This review evaluates whether improvements have occurred in the value of predictive equations for use in designing nutritional therapeutic regimens in the ICU. The report also seeks to determine whether emerging strategies for nutrition therapy in the ICU change the need for an accurate measurement of energy requirements by indirect calorimetry.

RECENT FINDINGS:

Predictive equations remain problematic for use in the critically ill patient. Inaccuracy of predictive equations introduces error in the design of a nutritional therapy regimen. The epidemic of obesity renders the calculations of requirements by predictive equations increasingly inaccurate at extremes of BMI. Certain patient populations appear to be hypometabolic, contradicting the traditional notion that critical illness increases energy expenditure. More recent data indicates that determination of which patients benefit from nutritional therapy may be based both on assessment of nutrition risk and delivery of sufficient nutrition therapy.

SUMMARY:

The role of indirect calorimetry in the ICU should be expected to increase in the near future, as predictive equations may be too inaccurate to identify the appropriate goals of nutrition therapy.

Abstract

Nutrition support has been shown to have a positive impact on critically ill patients who meet their defined goals of nutrition therapy. However, inappropriate energy assessment can contribute to under- or overfeeding resulting in deleterious effects. Thus, assessment of energy expenditure in critically ill patients is crucial to prevent negative impacts from inappropriate feeding. Currently, the optimal energy requirement and appropriate energy assessment in these patients is controversial. Indirect calorimetry or predictive equations have been suggested to evaluate energy expenditure in critically ill patients. Indirect calorimetry is a gold standard for evaluating energy expenditure, but it is not always available and has some limitations. Many predictive equations, therefore, have been developed to predict energy expenditure in critically ill patients. However, these equations cannot be used generally in these patients since they were developed in a unique patient population. Many studies compared measured energy expenditure with predictive energy expenditure, but the data regarding accuracy is not robust. Therefore, clinicians should consider using these equations carefully based on the current supporting data. Indirect calorimetry is recommended for use in evaluating energy expenditure in critically ill patients if it is available.

Background & aims: Optimal nutritional care for intensive care unit (ICU) patients requires precise

determination of energy expenditure (EE) to avoid deleterious under- or overfeeding. The reference

method, indirect calorimetry (IC), is rarely accessible and inconstantly feasible. Various equations for

predicting EE based on body weight (BW) are available. This study aims at determining the best prediction

strategy unless IC is available.

Methods: Mechanically ventilated patients staying 72 h in the ICU were included, except those with

contraindications for IC measurements. IC and BW measurements were routinely performed. EE was

measured (MES), adjusted for cumulated water balance (ADJ), calculated for a body mass index (BMI) of

22.5). Comparisons were made using Pearson correlation and Bland & Altman plots.

Results: 85 patients (57 ± 19 y, 61 men, SAPS II 43 ± 16) were included. Correlations between IC and

predicted EE using the ESPEN formula with different BW (BWAN, BWMES, BWADJ, and BWBMI22.5) were

0.44, 0.40, 0.36, and 0.47, respectively. Bland & Altman plots showed wide and inconsistent variations.

Predictive equations including body temperature and minute ventilation showed the best correlations,

but when using various BWs, differences in predicted EE were observed.

Conclusion: No EE predictive equation, regardless of the BWused, gives statistically identical results to IC.

If IC cannot be performed, predictive equations including minute ventilation and body temperature

should be preferred. BW has a significant impact on estimated EE and the use of measured BWMES or BW

BMI 22.5 is associated with the best EE prediction.

Background & aims: The resting energy expenditure (REE) predictive formulas are often used in clinical

practice to adapt the nutritional intake of patients or to compare to REE measured by indirect calorimetry.

We aimed to evaluate which predictive equations was the best alternative to REE measurements

according to the BMI.

Methods: 28 REE prediction equations were studied in a population of 1726 patients without acute or

chronic high-grade inflammatory diseases followed in a Nutrition Unit for malnutrition, eating disorders

or obesity. REE was measured by indirect calorimetry for 30 min after a fasting period of 12 h. Some

formulas requiring fat mass and free-fat mass, body composition was measured by bioelectrical

impedance analysis. The percentage of accurate prediction (±10%/REE measured) and Pearson r correlations

were calculated.

Results: Original Harris & Benedict equation provided 73.0% of accurate predictions in normal BMI group

but only 39.3% and 62.4% in patients with BMI < 16 kg m2 and BMI 40 kg m2, respectively. In

particularly, this equation overestimated the REE in 51.74% of patients with BMI < 16 kg m2. Huang

equation involving body composition provided the highest percent of accurate prediction, 42.7% and

66.0% in patients with BMI < 16 and >40 kg m2, respectively.

Conclusion: Usual predictive equations of REE are not suitable for predicting REE in patients with

extreme BMI, in particularly in patients with BMI <16 kg m2. Indirect Calorimetry may still be recommended

for an accurate assessment of REE in this population until the development of an adapted

predictive equation.

Methods This was a prospective observational cohort study in

a mixed medical-surgical, 28-bed ICU in an academic hospital.

243 sequential mixed medical-surgical patients were enrolled

on day 3–5 after admission if they had an expected stay of at

least another 5–7 days. They underwent indirect calorimetry as

part of routine care. Nutrition was guided by the result of indirect

calorimetry and we aimed to provide at least 1.2 g of protein/kg/

day. Cumulative balances were calculated for the period of

mechanical ventilation. Outcome parameters were ICU, 28-day

and hospital mortality.

No difference in outcome related to optimal

feeding was found for men.

Conclusions Optimal nutritional therapy improves ICU, 28-day

and hospital survival in female ICU patients. Female patients

reaching both energy and protein goals have better outcomes

than those reaching only the energy goal. In the present study

men did not benefit from optimal nutrition.

Reaching nutritional goals, in this study defined as energy

delivery with a minimum of 90% of the measured REE plus

10% and protein provision of at least 1.2 g/kg pre-admission

body weight during the period of mechanical ventilation,

results in an 80% decreased chance of dying in the ICU and

a 92% decreased 28-day mortality, while hospital mortality is

67% lower when compared with patients who do not reach

the above mentioned nutritional goals.

Reaching only the energy target and not attaining 1.2 g protein/

day in females results in less favorable outcomes than

when both energy and protein goals are reached. The chance

of dying in the ICU is not affected by reaching only the energy

target but there is still a decreased chance of dying of 88% at

28 days and a 68% decrease of hospital mortality.

Guidelines for nutrition therapy in critical illness: are not they all the same?

R. G. MARTINDALE 1, M. S. MCCARTHY 2, S. A. MCCLAVE 3

1Division of General Surgery, Oregon Health and Science University, Portland, OR, USA; 2Madigan Army Medical Center, Tacoma, WA, USA; 3Division of Gastroenterology/Hepatology, University of Louisville School of Medicine, Louisville, KY, USA

Minerva Anestesiol 2011;77:463-7

The major controversies of the currently published guidelines are in four areas. Variations in the population of patients examined results in major differences. For example, the ESPEN, ASPEN, and SCCM guidelines all include critical care recommendations for surgical patients admitted to an ICU during their hospital stay. The Canadian Critical Care Clinical Practice Guidelines do not include elective surgical patients. This in effect limits recommendations for patients undergoing major surgical procedures such as esophagectomy, pancreaticoduodenectomy, and major liver resections, all of whom routinely spend several days in the intensive care unit postoperatively.

Another major difference between the various guidelines is in the sample size of studies accepted as large studies. The ASPEN and SCCM Guidelines required a study of more than 100 patients to be classified as a “large study”, whereas the Canadian Critical Care Practice Guidelines did not have a lower limit for sample size. The ESPEN Guidelines do not state sample size requirements. The study design used to assign grades and make recommendations was also quite variable. SCCM, ASPEN, and ESPEN all used randomized clinical trials, observational studies, and expert opinion, while the CCCPG used only randomized clinical trials and included meta-analyses, unlike the other Societies. However, meta-analysis methodology was used to organize data by both ESPEN and SCCM. The use of expert opinion and observational studies remains controversial. The SCCM Guidelines committee felt strongly that to make the guidelines more useful at the bedside the recommendations of expert opinion were acceptable as long as they were clearly stated and given a low grade of (E). This lets the reader be aware that while this recommendation is only expert opinion it still provides some guidance on the clinical situation in question. The CCCPG Committee elected not to cite expert opinion, and instead, state there is insufficient data to make a recommendation.