Nutrition Monitoring in the PICU

  • George BriassoulisEmail author


The ideal set of variables for nutritional monitoring that may correlate with patient outcomes has not been identified. This is particularly difficult in the PICU patient because many of the standard modes of nutritional monitoring, although well described and available, are fraught with difficulties. Thus, repeated anthropometric and laboratory markers must be jointly analyzed but individually interpreted according to disease and metabolic changes, in order to modify and monitor the nutritional treatment. In addition, isotope techniques are neither clinically feasible nor compatible with the multiple measurements needed to follow progression. On the other hand, indirect alternatives exist but may have pitfalls, of which the clinician must be aware. Risks exist for both overfeeding and underfeeding of PICU patients so that an accurate monitoring of energy expenditure, using targeted indirect calorimetry, is necessary to avoid either extreme. This is very important, since the monitoring of the nutritional status of the critically ill child serves as a guide to early and effective nutritional intervention.


Nutrition Monitoring PICU Energy expenditure Metabolic monitor Anthropometrics 


Metabolic demands of critical illness and underfeeding or overfeeding may expose seriously ill children to the threat of malnutrition or metabolic overload (acute metabolic syndrome). In addition, nutritional status itself affects every pediatric patient’s response to illness. Reports of poor provision of nutrition in intensive care units, as well as evidence of malnutrition [1] or overfeeding [2] in critically ill patients are still frequent. Recent studies, compared with similar surveys performed up to three decades ago, showed that there has been little improvement in nutritional status in pediatric populations in the interim [3].

Although caloric intake lower than the basic metabolic rate has been associated with higher mortality and morbidity rates [4], critically ill children have been reported to only receive a median 58.8 % of their energy requirements, which could not be optimized until the 10th intensive care day [5]. In another study, patients in the Pediatric Intensive Care Unit (PICU) received a median of 37.7 % (range, 0.2–130.2 %) of their estimated energy requirements [6]. Only 52 % achieved full estimated energy requirements at any time during their admission. Failure to estimate energy requirements accurately [7], barriers to bedside delivery of nutrients, and reluctance to perform regular nutritional assessments are responsible for the persistence and delayed detection of malnutrition in this cohort [8]. At the same time, using targeted indirect calorimetry, a high incidence of unintended overfeeding in critically ill children with a long stay has been recently detected [2]. The predominance of hypometabolism, failure of physicians to correctly predict metabolic state, use of stress factors, and inaccuracy of standard equations all contributed to cumulative energy excess in this cohort [8].

Nutritional monitoring should be an integral part of the care for every pediatric critically ill patient. Despite the realization of its importance and the high incidence of combination of malnutrition and low energy intake, most medical professionals seldom assess and monitor the nutritional status of hospitalized patients [9]. A survey in 111 European PICUs from 24 countries showed that a multidisciplinary nutritional team was available in 73 % of PICUs. Approximately 70 % of PICUs used dedicated software for nutritional support and acknowledged nutritional support as an important aspect of patient care, yet only 17 % of them regularly monitored energy expenditure by indirect calorimetry. In most PICUs daily energy requirements were estimated using weight, age, predictive equations with correction factors, and a wide range of biochemical blood parameters [9].

Assessing Nutritional Status

Subjective Global Nutritional Assessment (SGNA)

The ideal set of variables for nutritional monitoring that may correlate with patient outcomes has not been identified. This is particularly difficult in the PICU patient because many of the standard modes of nutritional monitoring, although well described and available, are fraught with difficulties. Thus, repeated anthropometric and laboratory markers must be jointly analyzed but individually interpreted according to disease and metabolic changes, in order to modify and monitor the nutritional treatment. In addition, isotope techniques are neither clinically feasible nor compatible with the multiple measurements needed to follow progression. On the other hand, indirect alternatives exist but may have pitfalls, of which the clinician must be aware. Overall, among assessment instruments, only 11 original instruments and three modified ones were published with enough information to allow appropriate usage [10]. This is very important, since the monitoring of the nutritional status of the critically ill child serves as a guide to appropriately modify nutritional intervention.

Because of the 24-h 7-day-a-week time requirement for the initial nutrition screen in a PICU, many units use staff nurses to complete the screening at the time of admission. These screens are generally shorter in length than more in depth screens that include laboratory values, but have the advantage that they can be done efficiently and in a timely fashion. Certain components of nutritional assessment have been combined into a clinical tool described as the subjective global nutritional assessment (SGNA) that physicians can use to systematically document and recognize nutritional problems in their hospitalized patients [11]. It provides a systematic method for obtaining essential information about nutritional status from the history and the physical exam, such as the history of weight loss, altered food consumption, gastrointestinal derangements, decreased functional capacity, subcutaneous tissue loss, muscle wasting, and the presence of edema. It demands training but is easily learned, adds little additional effort to a routine admission history and physical examination, and is a powerful predictor of adverse outcomes [12]. Thus, SGNA has been validated by anthropometry and albumin measurement, and predicted morbidity and mortality in severely ill patients [13]. Those patients classified as severely malnourished by the SGNA presented with a consistent worsening of the traditional objective markers, had significantly more complications, remained in the hospital longer and had a higher mortality rate. Additionally, it has been shown that SGNA is a sensitive and specific nutrition assessment tool for assessing nutritional status in children having major thoracic or abdominal surgery and identifying those at higher risk of nutrition-associated complications and prolonged hospitalizations [14]. Therefore, application of the protocol as a complement of standard anthropometric tool in a PICU setting should be considered.


In the initial evaluation of nutritional status, only updated national or regional standard growth curves, the most rudimentary of assessment tools, are necessary. The child who suddenly or progressively deviates from an established pattern is at high risk for depletion. The height indexed to the height of the 50th percentile is useful in assessing chronic changes, but its value in acute illness is not clear. Weight is more likely to be affected by acute changes, while deviation from the height curve perhaps reflects long-standing caloric deprivation. The current body weight is often compared with the ideal body weight for height in order to roughly estimate the patient’s body habitus versus norms. These measurements can be converted to growth velocities or to height-for-age and weight-for-height Z-scores or percent of expected values to provide a measure of the degree of under- or over-nutrition in the child [15]. There are several important caveats, however, in monitoring anthropometries in the critically ill. They suffer from the influence of intra-observer and inter-observer errors and are compared with tables derived from healthy populations. Furthermore, the edema and ecchymoses often encountered in the PICU setting interfere with accurate determinations. Monitoring the weight/height/age ratios, therefore, based on the National Center for Health Statistics [16] and the World Health Organization child growth charts [17] can be used as reference but there is a risk of over- and underestimation of malnutrition rates compared with country-specific growth references [18]. For children with specific medical conditions and syndromes, specific growth references should be used for appropriate interpretation of nutritional status [19].

Body mass index (BMI) may also be used to assess nutritional status. This weight-stature index is calculated as weight in kilograms divided by height in meters squared. Although there is no consensus on how to interpret BMI in relation to nutritional status, a BMI of <15 kg/m2 has been associated with significant increases in morbidity and mortality. In moderately malnourished children, percentage of standard BMI was the best predictor for serum leptin concentrations, which were low not only in severe acute malnutrition, but also in children with mild-to-moderate malnutrition without chronic disease [20]. In adults, it has been suggested that the relationship between BMI and patient outcomes is “U” shaped, with worse outcomes for both underweight (BMI <18.5 kg/m2) and morbidly obese (>40 kg/m2) patients [21, 22]. In evaluating more severe changes, chronic and acute nutritional status was defined by interpreting the Waterlow stages for chronic protein-energy malnutrition (CPEM) and acute protein-energy malnutrition (APEM) [23]. Patients classified by these criteria as CPEM or APEM are more than 2 SD from the median (Table 42.1). Another widely used anthropometric classification is the Z score [24]. It may be used for children of any age, and rates lower than −2 Z scores or less than average indicate undernutrition. Children whose rates are lower than −3 Z scores or less than 70 % in relation to average, or those who present with edema provenly due to nutrition, are considered severely undernourished.
Table 42.1

Criteria for relative risk for malnutrition

A. Acute protein energy malnutrition (APEM): weight for height = (actual weight)/(50th percentile weight for subject’s height and age)

Waterlow stagea






At risk

Greater risk

Protein-calorie malnutrition

>90 %

80–89 %

70–79 %

<69 %

B. Chronic protein energy malnutrition (CPEM): subject’s height/50th percentile height for subject’s age

Waterlow stagea






At risk

Greater risk

Growth retarded

>95 %

90–95 %

85–89 %

<85 %

aEach Waterlow stage represents approximately 1 SD from the population median; patients are classified by these criteria as APEM or CPEM, if they are 2 SD or more from the median (stages 2–3)

Skinfold thickness (TSF) and circumference measurements of the arms, legs and/or trunk may be useful to characterize the changes in peripheral fat depots and muscle mass, respectively. Upper arm TSF and mid-arm circumferences (MAC) represent body-compartment measurements of adipose tissue and muscle. Arm muscle size is been calculated from arm circumference and triceps skinfold and should be useful in monitoring the depletion of lean body mass [25]. In infants, 10 % of body weight should be fat; by 5–10 months of age, this should be up to 20 %. Even in adults, compared with the BMI, the MAC was a better mortality predictor in patients with chronic obstructive pulmonary disease [26]. MAC and cutaneous TSF are determined using Lange skin-fold callipers and a tape measure [27]. From these measurements, mid-arm muscle circumference (MMC), mid-arm muscle area (MMA), and mid-arm fat area (MFA) are calculated. Fat stores are assessed by measurements of TSF and MFA; somatic protein stores are assessed by MMC and MMA. Both, fat and protein stores are classified as normal, nutritionally at risk, or deficient, according to Frisancho [28, 29] and Ryan, Martinez [30] or the Standards of the Ten-State Nutritional Survey [31] tables (Table 42.2). Critically ill patients whose arm circumference values are below the fifth percentile have a higher mortality rate [32]. Acute fluid shifts and changes in circulating albumin, however, cannot only influence weight, but also arm circumference and skinfold thickness determinations. In a recent prospective study, however, in which 80 % of children received enteral nutrition, there was no statistically significant change in most anthropometric indicators evaluated in the PICU, suggesting that nutrition probably helped patients maintain their nutrition status [33]. Importantly, in children with cancer, although the weight-for-height values were normal, MAC and TSF values were significantly less than control values [34]. In a prospective PICU study, although weight and all arm anthropometrics decreased, only arm circumference and triceps skinfold thickness were significantly decreased at day 7 compared with initial measurements [33]. Thus, it has been suggested that arm anthropometry should replace the use of weight-related indices to identify malnutrition in children with co-morbidity [24].
Table 42.2

Anthropometric nutritional status assessment

Somatic protein stores:

 Midarm muscle circumference : MMC (mm) = MACa (mm) − (TSF [mm] × 3.14)

 Midarm muscle area : MMA (mm 2) = (MAC [mm] − [TSF [mm] × 3.14])2/4π

Fat stores:

Triceps skinfold thickness (TSF) [mm]

 Midarm fat area (MFA) (mm 2) = MAAb–MMA

Frisancho tables


Nutritionally at risk


>10 percentile

5–10 percentile

<5 percentile

aMidarm circumference = MAC

bMidarm area : MAA (mm 2) = π/4 × (MAC/π)2


The National Health and Nutrition Examination Survey and Pediatric Nutrition Surveillance System report a tripling of the prevalence of BMI at least 95 % (obesity) among US school-age children and adolescents over the past three decades [35]. International data confirm similar upward shifts in pediatric BMI distribution, especially in countries undergoing economic transitions favoring industrialized, western urban lifestyles [24]. Among adults, nearly one-third of ICU patients are obese and nearly 7 % are morbidly obese, frequencies that are predicted to increase as the prevalence of obesity in the general population rises [36]. However, there is a critical lack of research on how obesity may affect complications of critical illness and patient long-term outcomes. Data of 8,813 mechanically ventilated adults >18 years who remained in the ICU for >72 h (multicenter international observational study of ICU nutrition practices that occurred in 355 ICUs in 33 countries during 2007–2009) showed that during critical illness, extreme obesity is not associated with a worse survival advantage compared to normal weight [37]. It showed, however, that among survivors, BMI ≥40 kg/m2 is associated with longer time on mechanical ventilation and in the ICU.


Adequate nutrient intake is critical for optimal cellular and organ functions, protein synthesis, immunity, repair, and capacity of skeletal, cardiac, and respiratory muscles and tissue repair [38]. Disease related malnutrition, however, frequently occur in infants and children, often with more rapidly obvious and detrimental consequences than in adults. Other factors such as age, social background, congenital heart disease, burn injury, and length of hospital stay also negatively impact nutritional status. The consequences of hospital malnutrition are well described and recognized as causing skeletal-muscle weakness, increased rate of hospital-acquired infection, impaired wound healing, prolonged convalescence, length of hospital stay, increasing mortality [39] and, consequently, the costs of providing health care [40, 41] A number of studies have demonstrated that children with newly diagnosed diseases may already be malnourished [3]. It has also been indicated that malnutrition and nutrient store deficiencies commonly occur early in the course of critical illnesses in children [42]. In a recent study, 16.7 % of patients were already depleted of protein and 31 % of fat stores upon admission to the PICU [43]. Overall, 16.9 % were at risk for and 4.2 % had already CPEM and 21.1 % were at risk for and 5.6 % had already APEM. In addition, levels of many complement components are reduced and trace mineral and vitamin deficiencies are associated with profound effects on cell-mediated immunity such as impaired lymphocyte stimulation response, decreased CD4+:CD8+ cell number and function, decreased chemotaxis and function of phagocytes, and diminished secretory immunoglobulin A antibody response [44]. These changes are driven by a combination of the counter regulatory hormones and the direct and indirect action of the various inflammatory mediators such as prostaglandin and kallikreins [45], the balance of which may be crucial in regulating the ability to generate an anabolic response [46].

Monitoring Biochemical Markers

Plasma Proteins

Because of the problems associated with anthropometrics, a number of other measurements are used in conjunction both for establishing the initial nutritional status and monitoring changes. Hepatic proteins such as albumin, transthyretin (pre-albumin), transferrin, and retinol binding protein have been used as nutritional markers. Among them, the shorter half-life proteins correlate better with acute changes, and the longer-lived proteins are better for the evaluation of chronic problems. Thus, isolated starvation does not alter plasma protein concentrations reflecting the uniform loss of water and cellular mass until severe depletion is present. On the other hand, critical illness leads to a decrease of protein concentrations without severe loss of body cell mass, mainly reflecting reprioritization of liver protein synthesis.

Serum albumin represents equilibrium between hepatic synthesis and albumin degradation and losses from the body. It is also influenced by intravascular and extravascular albumin compartments and water distribution. About one-third of the albumin pool is in the intravascular compartment, and two-thirds is in the extravascular compartment. Once albumin is released into the plasma, its half-life is about 21 days. Levels of this visceral protein may decline in the setting of acute injury and illness as the liver reprioritizes protein synthesis from visceral proteins to acute-phase reactant proteins and as a consequence of increased degradation, transcapillary losses and fluid replacement [47, 48]. Albumin losses from plasma to the extravascular space increase threefold in patients with septic shock [49]. Albumin might be also altered because of factors other than malnutrition, such as in hepatic disorders, extra protein losses (nephrotic syndromes, in fistula, peritonitis), and in cases of acute infection or inflammation. However, it has been shown that serum albumin correlates very poorly with monitoring of nutritional status based on a patient’s history and physical exam [50].

Transthyretin, also referred to as prealbumin, is a transport protein for thyroid hormone. It is synthesized by the liver and partly catabolized by the kidneys. Normal serum transthyretin concentrations range from 16 to 40 mg/dL; values of <16 mg/dL are associated with malnutrition. Levels may be increased in the setting of renal dysfunction, corticosteroid therapy, or dehydration, whereas physiological stress, infection, liver dysfunction, and over-hydration can decrease transthyretin levels. The half-life of transthyretin (2–3 days) is much shorter than that of albumin, making it a more favorable marker of acute change in nutritional status [43].

Transferrin has also been used as a marker of nutritional status. This acute-phase reactant is a transport protein for iron; normal concentrations range from 200 to 360 mg/dL. Transferrin has a relatively long half-life (8–10 days) and is influenced by several factors, including liver disease, fluid status, stress, and illness. Levels decrease in the setting of severe malnutrition, but this marker is unreliable in the assessment of mild malnutrition, and its response to nutrition intervention is unpredictable. Transferrin has not been studied as extensively as albumin and transthyretin in relation to nutritional status, and the test can be expensive. It also suffers from the influence of other non-nutritional situations such as hepatic and renal failure and hormone infusion.

Despite these limitations, it has been shown that transferrin and prealbumin levels improved at the end of a period of early enteral feeding in critically ill children, while survivors had higher prealbumin levels than non-survivors (22.3 versus 15.5 mg/dL) [43]. Similarly, a greater positive trend in levels of prealbumin, transferrin, retinol-binding protein, and total protein has been shown in a protein-enriched diet group [51].

Nutritional Indices

Nutritional indices, such as the Prognostic Nutrition Index, are mathematically derived equations that combine measurements of albumin, triceps skinfold thickness, transferrin, and delayed hypersensitivity skin testing. Each measurement has its own restrictions, as previously mentioned, but, when combined, they have been shown to increase the sensitivity of prediction of major morbidity in surgical patients [52]. Two multi-parameter nutritional status indices, the Maastricht Index (MI) [53] and the Nutritional Risk Index (NRI) [54] were used to assess the nutritional status of patients. Also, acute phase reactants such as C-reactive protein have been used as a marker of metabolic state [55]. By combining nutritional and acute stress markers, a modified form of Prognostic Inflammatory and Nutritional Index (PINI) has been shown to be significantly correlated with protein intake by the end of early enteral nutrition (EN) and to be negatively correlated to myocardial contractility in critically ill children [43].

Nitrogen Balance

Significant negative nitrogen balance and somatic protein depletion develops in critically ill pediatric patients, especially when they are inadequately fed, develop Multiple Organ System Failure (MOSF), or have previous chronic illness. Thus, an alternative to specific protein determinations is measurement of nitrogen balance. In the human body, only protein is composed of nitrogen, thus measurement of nitrogen excretion is a method for assessing protein metabolism and indirectly assessing metabolic stress and following up nutrition repletion. Achievement of positive protein and energy balance in relation to the basic metabolic rate using an aggressive early EN protocol improved nitrogen balance during the acute phase of stress in two-thirds of critically ill children [56]. The breakdown of muscle proteins has been proved to be sensitive to alterations in nutrient and substrate supply [57]. It has been speculated that ATP is utilized in the process of peptide bond synthesis, for the formation of the tertiary structure of proteins, and for the synthesis of tRNA, mRNA, and the nucleotides from which they are, in turn, found [58]. On the contrary, the oxidation of amino acids in muscle is stimulated by fasting, sepsis, stress, hormonal influence, and other conditions associated with negative NB [59]. It was recently shown that caloric intake and MOSF independently affect substrate utilization [60]. In particular, the incidence of negative NB was 91 % when the caloric intake was less than REE and 9 % when it was equal to or greater than REE. Without MOSF there was a trend toward positive nitrogen balance by day 7 while with MOSF, negative nitrogen balance persisted even by day 7 [51].

Nitrogen balance, in the absence of exogenous support, can he estimated from urinary urea nitrogen excretion over a 24-h period. This will constitute about 93 % of total urinary nitrogen losses. Additionally, it has been suggested that 0.5 g per day of nitrogen be added to the output to account for nitrogen lost through skin [61]. Ideally, urine output should be collected for 24 h measurements of total urinary nitrogen and additional fecal, stoma, drainage fluid, and/or other body fluid losses should be obtained to determine concentrations of daily nitrogen excretion [49]. In critically ill children, nitrogen balance estimated by total urinary nitrogen and justified for other losses differed significantly from the estimated values using urine urea nitrogen or even unjustified total urinary nitrogen (Fig. 42.1). Nitrogen drainages are usually determined by manual micro-Kjeldahl digestion or by high-resolution liquid chromatography [62]. Nitrogen balance is then calculated by subtracting output (corrected for daily changes in total-body urea content assuming that urea is uniformly distributed in body water) from input (enteral, parenteral, and non-feeding protein), according to the following formula:
Fig. 42.1

Comparison of 24-h nitrogen balance estimations (g/day), using three different calculated methods (based on urine urea nitrogen, unjustified total urinary nitrogen or justified for other losses total urinary nitrogen) in the same population of critically ill patients (p <0.0001) (Courtesy of G. Briassoulis)

$$ \begin{array}{l} Justified\kern0.24em for\kern0.24em other\kern0.24em body\kern0.24em fluid\kern0.24em losses\kern0.24em nitrogen\kern0.24em balance\left(g/ day\right):\mathrm{N}\kern0.24em in take\\ {}-\left( Total\kern0.24em urinary\kern0.24em \mathrm{N}+ change\kern0.24em in\kern0.24em BU{N}^{\ast }+ faecal\kern0.24em losses+ other\kern0.24em body\kern0.24em fluid\kern0.24em losses+0.5\mathrm{g}\kern0.24em for\kern0.24em cutaneous\kern0.24em losses\right).\end{array} $$
$$ \ast Change\kern0.24em in\kern0.24em BUN\kern0.24em (g)=0.6\times weight\times BUN\kern0.24em in itial- BUN\kern0.24em final. $$

Creatinine Height Index- Body Protein Turnover

The creatinine height index may also be used to monitor nutritional status. It is derived from the measurement of 24 h urinary creatinine excretion as follows: (24 h excretion of creatinine/creatinine excretion of normal individuals of same height and sex) × 100. Thus the determined creatinine height index is compared with predicted values based on height and sex and then the somatic protein status may be calculated as follows: <80 % = moderate depletion of somatic protein status, <60 % = severe depletion of somatic protein status [53]. Any factor that might interfere with creatinine excretion, such as age, renal disease, stress or diet, might interfere with its interpretation. In a mixed critically ill pediatric population, 27 % had mild to moderate somatic protein depletion and 5.4 % had severe somatic protein depletion on day 1 [60]. Only the persistence of stress and co-morbidity were associated with the creatinine-height index. Monitoring this index in a prospective study of early EN in a PICU, children who had severe depletion of somatic protein status on stress day 1 reached the normal range of somatic protein status on post-stress day 5 (CHI >80 %) [56].

Although, 3-methylhistidine excretion has been proposed as an index of skeletal muscle degradation and, therefore, proteolysis associated with stress, at least 25 % of urinary 3-methylhistidine has been also attributed to extra-skeletal sources [63]. Tracer studies of whole body protein turnover underestimate actual protein turnover because intercellular recycling of amino acids occurs, and thus some amino acids may not be in equilibrium with a tracer such as nitrogen.

Monitoring Resting Energy Expenditure

Energy expenditure can be difficult to predict in PICU patients because of the effects of various disease states, therapeutic interventions, stress, and each patient’s own inherent metabolic requirements. It was recently shown that there was no relationship between resting energy expenditure (REE) and clinical severity evaluated using the PRISM, PIM2 and PELOD scales or with the anthropometric nutritional status or biochemical alterations [64]. Thus, neither nutritional status nor clinical severity was shown to relate to REE which should be measured individually in each critically ill child at risk, preferably using indirect calorimetry. Risks exist for both overfeeding and underfeeding of PICU patients so that an accurate assessment of energy needs is necessary to avoid either extreme.

Indirect Calorimetry

Indirect calorimetry continues to be the ‘gold standard’ for measuring energy expenditure in the critically ill child. Unfortunately, indirect calorimetry is expensive and is not available to the majority of PICU clinicians or registered dietitians. Obviously, when a metabolic monitor is available, critically ill children’s energy requirements should be based on measured REE as the Food and Agriculture Organization (FAO) and the World Health Organization (WHO) recommend. Traditional components of total energy expenditure (TEE) include REE, diet-induced thermogenesis and physical activity energy expenditure. The TEE is considered the REE plus a 5 % activity factor [65] and plus 15 % for day-to-day variability [66]. The magnitude of the diet-induced thermogenesis depends on the type of substrate provided, the amount of substrate ingested, and the way the body handles the substrate, ranging from 5 to 30 % [67].

Indirect calorimetry is based on the recognition that the body is a chemical furnace and, like all physical entities, must follow the basic laws of thermodynamics. In combustion, the use of energy involves the consumption of oxygen (VO2) and production of carbon dioxide (VCO2), nitrogenous waste, and water in a stoichiometric fashion. Further, according to the first law of thermodynamics, energy can be neither consumed nor created, but can merely change forms. When basic nutrients are converted to heat in cells, measurement of VO2 and VCO2 would indirectly reflect the basal metabolic expenditure involved. Mean REE and respiratory quotient (RQ) are automatically obtained using an integrated microprocessor. Respiratory quotient is calculated from gas fractions alone, according to the Haldane transformation and REE is calculated according to Weir’s equation [68] (Table 42.3).
Table 42.3

Measured resting energy expenditure (REE) and Respiratory Quotients (RQ)

According to Haldane transform, which states the relationship between the inspiratory (I) and expiratory (E) volume, the respiratory quotients (RQ) can be calculated from gas fractions alone:





Respiratory quotients


RQ = 1


RQ = 0.7


RQ = 0.8

REE can be derived from metabolic cart measurements, using the following formulae:

REE Weir formula:

REE = 5.68 VO2 + 1.59 VCO2 − 2.17 Urine N2

If urine N 2 is not entered, the REE is calculated as follows:

REE = 5.466 VO2 + 1.748 VCO2

The RQ reflects whole body substrate use, but is also affected by factors such as body habitus, acid-base disturbances, underlying metabolic conditions, or critical illness. Physiologically, RQ represents an instantaneous summary of the interplay between oxidation and synthesis of various metabolic substrates. The ratio can vary from 0.7 to 1.2. The non-protein RQ is calculated from the measured respiratory quotient (i.e. VO2/VCO2) after correction for protein oxidation as measured by urinary nitrogen excretion after correction for changes in serum urea levels. Thus, the non-protein RQ reflects the ‘net’ metabolism of glucose and lipids. A RQ value greater than 1 indicates that patients have been overfed, especially in excess of carbohydrate calories, which can result in net fat synthesis. Lipogenesis or net fat synthesis is also defined as a non-protein RQ >1. A high carbohydrate intake, defined as a continuous glucose infusion >8 mg*kg−1*min−1, to acutely ill patients may increase CO2 production by 50–66 %. Overfeeding a respiratory compromised child might increase the RQ to >1, producing unnecessary CO2 that he/she might be unable to eliminate creating difficulty in weaning from mechanical ventilation [69] and prolonging the length of hospitalization [70]. Thus, when there is a need for increasing energy intake, the energy source should be carefully chosen to avoid giving excess carbohydrate calories. In adults on parental nutrition, however, RQ >1.0 was not a specific marker of excessive caloric intake [71] whereas RQ >1.0 was reported in <30 % of all adults who were overfed [72]. Similarly, in critically ill children, a single measurement of RQ had poor sensitivity (21 % for overfeeding) when a specific cutoff of RQ >1.0 was used to classify the degree of overfeeding [73]. Therefore, the best indicator of overfeeding remains the difference between energy intake and energy expenditure as measured by metabolic monitors.

Indirect calorimetry circumvents many of the problems associated with other modes of nutritional assessment. Since the method directly measures the conversion of energy to heat, there is no need to apply age-related, population-based data to individual critically ill children. Alterations in tissue composition also will not obscure the meaning of the data. Patients may be classified as hypermetabolic (as defined by a REE >10 % of the predicted value), normometabolic (REE within the ±10 % prediction range), and hypometabolic (REE <10 % of the prediction range) [62]. Compared with a ‘normometabolic’ group, hypermetabolic patients show higher fat oxidation suggesting that fat is preferentially oxidized. A high carbohydrate intake is associated with lipogenesis and thus with an increase in thermogenesis [62]. RQ is also strongly affected by the ratio of energy intake/total daily REE and by the cumulative energy balance. Following a patient over time will allow recognition of the nature of the metabolic problem and tailoring of support to meet individual needs. Thus, during the week following PICU admission, REE was not shown to be different from Schofield’s Predicted Basal Metabolic Rate (PBMR), but it was 20 % lower than TEE [74]. During convalescence, for clinically stable patients, adding approximately 20–25 % to the REE might better approximate TEE requirements [75]. From the median nitrogen excretion, optimal daily protein intake has been calculated as 1.9 g/kg, whereas a high protein intake (about 2.8 g/kg per day) has been shown to result in a positive nitrogen balance [62].

Monitoring nutrition of critically ill children, subjects may be divided into three groups, based on the degree of feeding as previously described [76]. Underfed is defined as a subject’s actual average energy intake being less than 90 % of total energy requirements. Appropriately fed is defined as a subject’s actual average energy intake being within ± 10 % of TEE requirements. In overfed patients the actual average energy intake is larger than 110 % of the TEE requirements. Additionally, careful monitoring with indirect calorimetry and nitrogen balance studies should help prevent inadequate protein or excessive carbohydrate intake. The non-protein RQ and the net substrate (fat, carbohydrate, and protein) oxidation rates are calculated using the Weir formula modified by Frayn [77].

The Standard Metabolic Monitor

The metabolic monitors have been widely validated and tested for accuracy and reproducibility by both in vitro and in vivo means, which enables its clinical use in PICU [78]. They measure the VO2 and VCO2 from gas exchange measurements, and calculate the REE and RQ by using the Weir equation [68], ignoring the urinary nitrogen [79]. For most children a single measurement of total daily energy expenditure provides reasonable insight as to the daily energy needs [80]. The mean percentage of between day variations in energy expenditure has been estimated at 21 ± 16 %, [81] but the early phase of stress response is characterized by a greater variability in REE because of the fast biphasic metabolic response to injury in children [82]. In a previous study conducted for validation of the predictive equations in the early post-injury period, no differences with clinical relevance were found in the REE throughout the 24-h post-injury phase [83]. In another study, the within-day variation of REE in ventilated, critically ill children was only 7.2 % [81] also supporting the approach that a single 30-min indirect calorimeter measurement may provide an acceptable guide to set up the nutritional support. Thus, in critically ill, ventilated children energy expenditure can be measured with acceptable accuracy but daily measurements are necessary because of huge between-day variations.

Patients receiving continuous enteral or parenteral infusions are not interrupted during the measurement. The patient is connected via an endotracheal tube to a spirometer filled with 100 % O2 attached to a kymograph (closed circuit spirometry method). As the patient breathes, the oxygen is consumed and CO2 is exhaled. The water and CO2 vapor are mechanically absorbed, so that volume changes in the spirometer are only due to the consumption of oxygen. The oxygen uptake by the lungs is determined from the amount of oxygen consumed from the spirometer. Since the magnitude of tube leakage of mixed expiratory gases cannot be predicted from endotracheal tube diameter, ventilator settings, or infant activity or posture [84], lack of an air leak must be confirmed on clinical examination by absence of an audible air leak during mechanical inspiration, by determining the difference between inspired and expired tidal volumes (<15 %), and/or during testing by the presence of stable minute to minute RQ values. Patients with FiO2 >0.80, with incompetent endotracheal tube cuffs, leaking chest tubes, bronchopleural fistulas losing expired gases to the environment, on other ventilators (not suitable for REE measurement) [85], or on continuous nitric oxide or CO2 inhalation may not have reliable measurements. The device permits uninterrupted patient ventilation and provides non-invasive measurement of inspired and exhaled gases. The flowmeter and the CO2 and O2 analyzers are automatically calibrated before each measurement and oxygen consumption is measured for at least 30 min each time.

Recent Advances in Monitoring REE in the Critically Ill

With the advent of newer technology, continuous and accurate metabolic monitoring in critically ill children has been possible at the bedside. Two of the most well studied metabolic monitors; the Deltatrac II NMN-200 (Datex-Ohmeda, Helsinki) and the E-COVX (former M-COVX) from Datex-Ohmeda (Helsinki) are systems that use the open circuit technique. The Deltatrac II measures gas volume in a mixing chamber and the E-COVX uses a breath-by-breath method to analyze VO2 and VCO2 [86]. The Deltatrac II, validated in different patients in the past but no longer produced, used to require a high level of technical expertise and was time consuming to calibrate. The newer compact E-COVX is able to continuously analyzeVO2 and VCO2, is cheaper and simpler to use, performs calibration automatically and is much smaller in size [87]. It was suggested, however, that although in adult patients it compared more [87] or less [88] favorably to the Deltatrac II, it might not provide measurement within a clinically accepted range in certain ventilation modes and in non-sedated patients [89].

Extending the previous studies we have recently shown that, despite an immediate decrease in dynamic compliance and expired tidal volume that result in inadequate or inaccurate sidestream respiratory monitoring, pulmonary mechanics and continuous indirect calorimetry monitoring were not influenced after uneventful open endotracheal suctioning in well-sedated children [90]. Especially, when using a compact modular metabolic monitor (E-COVX), attending physicians may be able to reliably record spirometry and metabolic indices as early as 5 min after suctioning at different ventilator modes. In addition, we have shown that the influence of different ventilator modes on VO2 and VCO2 measurements in adequately sedated critically ill children is not significant [91]. Thus, new compact metabolic monitors, like the E-COVX metabolic module, are suitable for continuous REE monitoring in well-sedated mechanically ventilated children with stable respiratory patterns using the PRVC, SIMV, or BiVent modes of ventilation.

The Short Douglas Bag Protocol

The Douglas bag method uses the fraction of expiratory CO2 to determine VCO2 and calculate energy expenditure through a double in/outlet, separated by a valve, allowing airflow to and from the bag or to outside air by turning a tap. It compared favorably to the metabolic monitor in a study in which predictive equations failed to predict individual energy expenditure [92]. Intra-measurement variability was within 10 % for both methods, which showed significant bias compared to Schofield equations. Considering its low cost, these data render the short and simple Douglas bag method a possible robust measure and a routinely applicable instrument for tailored nutritional assessment in critically ill children.

Alternative Methods Used to Predict Energy Expenditure

Bicarbonate Dilution Kinetics

Energy expenditure can be determined over a period of several days [93] by bicarbonate dilution kinetics if the energy equivalents of carbon dioxide (food quotient) from the diet ingested are known. Accordingly, using this method it is necessary to know the fraction of carbon dioxide produced during the oxidative process but not excreted. By measuring the dilution of 13C infused by metabolically produced carbon dioxide (continuous tracer infusion of NaH13CO3) the rates of 13CO2 appearance were estimated in critically ill children [94]. Determining the energy expenditure by this method, it was shown that the 2001 World Health Organization and Schofield predictive equations overestimated and underestimated, respectively, energy requirements compared with those obtained by bicarbonate dilution kinetics [94].

Indirect Estimations of REE

Because protein oxidation represents 8–12 % of the total energy expenditure, it has been speculated that changes in nitrogen balance are associated with changes in REE [95]. Benotti et al. proposed that an estimation of REE could be made from measurement of urea excretion and nitrogen balance [96]. In a study, in which the patients received only 5 % dextrose infusion and no nitrogen, a week correlation between daily nitrogen excretion and REE, has been shown [97]. In patients receiving mixed nutritional regimens, however, we could not find any correlation or independent association of the nitrogen balance with REE [98]. Instead, the efficacy of increased protein and energy intakes to promote protein anabolism and the underlying mechanisms were studied by measuring whole body protein balance (WbPBal) using intravenous-enteral phenylalanine/tyrosine stable isotope method protocol [99].

Predictive Equations

Energy requirements can be predicted with some accuracy. Estimations are based on measurements of energy expenditure in greater reference populations. Conceptually, estimates of energy requirements refer to the mean of groups and not to individuals. Predictive equations based on measurements in a considerable number of individuals have been developed for REE and also for TEE. Most of these equations are valid for adults. Alternatively, since patients in an ICU environment are rarely in a basal state, Kinney et al. have defined REE as the measured energy expenditure in a quiet supine individual. PBMR of assumed (non-measured) REE in ventilated, critically ill children, has usually been calculated using the Talbot’s tables, Harris-Benedict, Caldwell-Kennedy, Schofield, Food and Agriculture/World Health Organization/United Nation Union, Maffeis, Fleisch, Kleiber, Dreyer, and Hunter equations, using the actual and ideal weight [100].

The formulae most frequently used in practice (e.g. the FAO/WHO/UNU prediction equations or Schofield’s formulae) are based on measurements in considerable numbers of participants (i.e. more than 7,500 3–18-year-old children in the case of the FAO/WHO/UNU who were investigated between 1910 and 1980 in different areas all over the world) [101]. For children and adolescents a sufficient database on REE has only recently been established [102]. Henry et al. developed new equations to estimate REE in children aged 10–15 years [103]. These equations were based on measurements of REE by indirect calorimetry in 195 school children (40 % boys, 60 % girls). Sex, weight, height, puberty stage and skinfolds were used to develop more specific regression equations. In adults, and probably in adolescences, the predicted basal metabolic rate (PBMR) is a function of sex, age, height, and weight and can be calculated using the Harris-Benedict equations [104]. It has been suggested that until more accurate prediction equations are developed, the following should be utilized: Schofield-HW equation [105] for field studies with a mixed population of obese and non-obese children and adolescents; the FAO/WHO/UNU equation in girls; and the Schofield-W equation in non-obese children [106]. Corresponding Predicted Energy Expenditure (PEE) is estimated by PBMR multiplied by stress-related correction factors (Table 42.4). In critically ill children PBMR and PEE are often obtained according to FAO/WHO/UNU, Schofield-HW, and Seashore [107] equations and in adolescents after the Harris-Benedict equations [108].
Table 42.4

Equations used to estimate PBMR and PEE

1a. Harris-Benedict equation: (kilocalories/day) (after age 15) [204]

Males : 66.473 + (13.7516 × Wt) + (5.0033 × Ht) − (6.755 × Age)

Females : 665.0955 + (9.5634 × Wt) + (1.8496 × Ht) − (4.6756 × Age)

1b. PEE = PBMR × correction factor for stress (1.4–2.0)

Stress components


Elevated body temperature (per °C above 37 °C)


Severe infection/sepsis


Recent extensive operation




Burn wounds




2a. FAO/WHO/UNU equation: (kilocalories/day)

<3 years:

Boy–(60.9 × Wt) − 54

Girl–(61 × Wt) − 51

3–10 years:

Boy–(22.7 × Wt) + 495

Girl–(22.5 × Wt) + 499

10–18 years:

Boy–(17.5 × Wt) + 651

Girl–(12.2 × Wt) + 746

3a. SchofieldWH [204] equations (MJ/day) (1 kcal = 4.186 kJ)

<3 years:

Boy–(0.0007 × Wt) + (6.349 × Ht) − 2.584

Girl–(0.068 × Wt) + (4.281 × Ht) − 1.730

3–10 years:

Boy–(0.082 × Wt) + (0.545 × Ht) + 1.736

Girl–(0.071 × Wt) + (0.677 × Ht) + 1.553

10–18 years:

Boy–(0.068 × Wt) + (0.574 × Ht) + 2.157

Girl–(0.035 × Wt) + (1.948 × Ht) + 0.837

2b–3b. Correction factors (% of PBMR added to PBMR)

 Elevated temperature

+12 % per °C above 37°


+20 %


+10–30 % depending on severity


+10–30 % depending on severity


+10–30 % depending on severity

4a–b. Seashores equation for PBMR and PEE (kilocalories/day) (infants and children up to age 15)

Estimation of energy requirements



[55 − (2 × Age in years)] × Weight in kg



PBMR + 20 %



PBMR + 0–25 %



PBMR + 13 % for each 1 °C above normal


Simple trauma:

PBMR + 20 %


Multiple injuries:

PBMR + 40 %



PBMR + 50–100 %


Growth and anabolism:c

PBMR + 50–100 %


5a. Henrys regression formulae for estimating REE (kilojoules per day):

Boy Wt × 66.9 + 2876

Girl Wt × 47.9 + 3230

Ht Height in cm, Wt weight in kilograms, Age age in years, ºC degree centigrade, PBMR predicted basal metabolic rate, PEE Predicted Energy Expenditure, WHO Food and Agriculture Organization/World Health Organization/United Nations University equation, Mj Megajoules, kj kilojoules, kcal kilocalorie, ARDS Acute Respiratory Distress Syndrome, F female, M male

aIncludes specific dynamic action and amount of energy needed for equilibrium in the resting but awake state with minimal muscular movements

b0 % for comatose state, 25 % for hospitalized child who ambulates two to three times a day, 50 % for active non-hospitalized child

c100 % for growth in infancy and adolescence; 50 % for the years in between

Lessons Learned From Adult Studies

As shown recently, seven prediction equations applied to critically ill patients were rarely within 10 % of the measured REE [109]. In 34 mechanically ventilated cancer adult patients the Harris-Benedict PBMR without added stress and activity factors correlated better with measured REE than did the clinically estimated PEE based on recommendations of the American Society for Parenteral and Enteral Nutrition [110]. Thus indirect calorimetry is now suggested as the method of choice to estimate caloric requirements in critically ill, mechanically ventilated adult patients [111].

Current clinical practice guidelines suggest that an adequate energy goal to be monitored for most ICU patients is approximately equivalent to the measured or estimated REE multiplied by 1.0–1.2 [112]. Although, an alternative method is to initially use 20–25 kcal per kilogram of body weight as the total caloric target range for most adults in the ICU [113], a REE monitoring period of about 12 min for patients on controlled ventilation and 21 min for those on assisted mode was found to be enough for a successful daily REE estimation in the majority of cases (difference between TDEE and REE <5 %) [45]. In addition, it has been shown that indirect calorimetry with 5-min steady state test correlated very well with the 30-min steady state test in both mechanically ventilated and spontaneously breathing patients [114]. A 5-min period of measurement, if variation in that measurement is less than 5 % [115], or multiple short measurement periods (twice 15 min) or a single 20-min measurement have also been shown accurate in approximating TDEE [116]. Later in the period of hospitalization multiple factors influence the REE and due to these factors, there can be a significant variability in the daily REE, ranging from 4 to 56 %. Occasionally, attention to the RQ has been considered important in roughly evaluating substrate utilization and/or nutritional support and in determining overfeeding and underfeeding [117].

The Pediatric Experience

The unreliability of prediction equations to determine caloric requirements in ventilated, critically ill children is well established. Obesity, malnutrition, dehydration, excess body water, or population differences may impose difficulty in accurately monitoring body weight, height or other variables used in the prediction equations [111]. In particular, most of the predictive equations overestimate REE in critically ill children during the early post-injury period [83]. Similarly, recommended daily allowances and energy expenditure predicted by using a stress-related correction to the PEE grossly overestimate REE [98, 100]. In fact, in critically ill mechanically ventilated children, REE is close to PBMR and in many patients it is lower than PBMR and associated with higher morbidity [98]. Since a proportion of children do not become hypermetabolic during the acute phase of critical illness [98], agreement between REE and PEE remains broad [118, 119], confirming therefore that PEE equations are inappropriate for use in critically ill children [120, 121]. Although none of the remaining methods stood out as being more precise, the recommended dietary allowance for energy has been shown to be the least accurate and differed significantly even from the other predictive methods, overestimating energy expenditure in 50 of 52 children [122]. In young children (birth to 3 years) with failure to thrive WHO, Schofield weight-based, and Schofield weight- and height-based equations were all within 10 % accuracy <50 % of the time [123]. Agreement between measured resting energy expenditure and equation-estimated energy expenditure was poor, with mean bias of 72.3 ± 446 kcal/day (limits of agreement −801.9 to +946.5 kcal/day).

Besides disparity between equation-estimated PEE, REE, and TDEE among critically ill children, a high incidence of underfeeding or overfeeding and a wide range of metabolic alterations were also recorded [124], strongly suggesting that nutritional repletion should be ideally based on indirect calorimetry. Hopefully, targeted indirect calorimetry on high-risk patients selected by a dedicated nutrition team may prevent cumulative excesses and deficits in energy balance [124].

Body-Composition Tests

Body-composition methods, such as nuclear magnetic resonance, whole-body conductance and impedance, neutron activation, hydrodensitometry and other techniques, have been evaluated as additional nutritional assessment tools in healthy populations and in athletes [125]. Except for a few studies from investigative centers, very little research has been done to prove the utility of these methods in sick patients. It is extremely difficult to perform these tests in bedridden children, those who are connected to ventilators, and those who have fluid imbalances. For example, hydrodensitometry, which some consider the ‘gold standard’ for body-composition analysis, would be impossible to perform in bedridden patients, as it requires total immersion of the child in water.

Dual-Energy X-Ray Absorptiometry

The dual-energy X-ray absorptiometry (DEXA) designed for the diagnosis of osteoporosis provides accurate information about the body compartments (fat, lean mass and bone) and is considered a referential method for this assessment [126]. DEXA has been used in conjunction with gamma in vivo neutron activation analysis, tritiated water dilution, total body potassium and calorimetry to assess body composition and energy expenditure in a small group of patients with blunt trauma [127]. During the first 25 days, a relationship was demonstrated between the changes in body compartments and metabolic requirements.

Bioelectrical Impedance: Magnetic Resonance Spectroscopy

Bioelectrical impedance is a simple, noninvasive, easy and low-cost technique; monitoring results of the body composition in adults and children are consistent [128]. Although it has been showed that bioelectrical impedance is good for clinical studies in patients in intensive-care units, it has not been proved very accurate in individual cases [129]. Especially, this method has been studied in patients on dialysis, because of the difficulty to perform anthropometric and laboratorial nutritional assessment of such patients [130]. However, although it was demonstrated that the electrical bioimpedance was more sensitive to body changes than the anthropometric measurements [131], for many PICU patients, bioelectrical impedance may not be useful as a nutritional monitoring tool because of fluctuation in fluid volumes and changing body weight [132].

Doubly Labeled Water Technique

Another approach to measure TEE is an isotope dilution, the so-called doubly labeled water (DLW) technique. DLW is based on the differences in turnover rates of 2H2O and H2 18O in body water. After equilibration, both 2H and 18O are lost as water whereas only 18O is lost by respiration as carbon dioxide. The difference in the rate of turnover of the two isotopes can be used to calculate the VCO2 . Assuming a mean respiratory quotient (i.e. VO2/VCO2) of 0.85, the VO2 and thus energy expenditure can then be calculated from VO2 and VCO2. The DLW technique is validated against indirect calorimetry and is now considered to be a gold standard for measurements of TEE under free-living conditions.

It is clear that 2H2O can now be used to address questions related to carbohydrate, lipid, protein and DNA synthesis. Using this novel tracer method, it is thus possible to elucidate new, highly relevant, knowledge regarding health and disease [133]. Especially, the DLW method is most convenient in children because it places low demands on the participant’s performance (only drinking a glass of water and the collection of some urine samples). Sources of error are analytical errors in the mass spectrometric determination of isotopic enrichment, biological variations in the isotope enrichment, isotopic fractionation during formation of carbon dioxide and during vaporization of water, the calculation of total body water and the assumption or calculation of the 24 h respiratory quotient.

Whole-Body Counting/Neutron Activation

The elements K, N, P, H, O, C, Na, Cl, and Ca can be measured with a group of techniques referred to as whole-body counting/in vivo neutron activation analysis. Whole-body counting neutron activation methods are important because they provide a means of estimating all major chemical components in vivo. These methods are considered the standard for evaluating the body-composition components of nutritional interest, including body-cell mass, fat, fat-free body mass, skeletal muscle mass, and various fluid volumes [134]. Shielded whole-body counters can count the γ-ray decay of naturally occurring 40K, but there is no experience in critically ill settings.

Miscellaneous Tests Indicating Nutritional Status/Stress Response

This section covers different tools that have been used to assess nutritional status but which have either had their capacity to monitor malnutrition questioned or have been found to be too difficult to perform routinely in a clinical setting [135].

Comparison with Recommended Dietary Allowances

The Recommended Dietary Allowances (RDA) is the most commonly used reference allowances in the pediatric population. These recommended levels for nutrient intake are estimated to meet the nutritional needs of practically all healthy children. Caloric allowances are estimated using the tables proposed by the Food and Nutrition Board of the Institute of Medicine [136], and Dietary Reference Intakes [137]. Although the upper intake level is the appropriate Dietary Reference Intake to use in assessing the proportion of a group at risk of adverse health effects [137], RDA is inappropriate to assess the nutrient adequacy of groups such as the critically ill [98].

Routine Laboratory Tests

The routine electrolyte, mineral (calcium, phosphorus and magnesium) and triglyceride laboratory tests monitoring are not related to anthropometric rates, although they are important to follow nutritional protocols, especially those of parenteral nutrition, and determine specific nutritional deficiencies [138]. Serum cholesterol levels lower than 160 mg/dl have been considered a reflection of low lipoprotein and thus of low visceral protein levels [139]. Hypocholesterolemia, however, seems to occur late in the course of malnutrition, limiting the value of cholesterol as a screening tool.

Selenium, Zinc, Chromium, Iodine, Iron, Copper and vitamin (A, B2, B6, B12, E) deficiencies, which are very common in patients belonging to low socioeconomic class in developing countries, may inhibit T4 to T3 conversion and lead to functional hypothyroidism and severe hypometabolism (extremely low REE) [140].

Glucose Control

Stress-induced hyperglycemia has been well described in the literature in the acutely ill patient population owing to insulin resistance and increase gluconeogenesis [141]. In fact, insulin resistance is an adaptive mechanism that prioritizes utilization of energy for immune response in the presence of infection or injury [142]. In the presence of fatty acids mitochondrial pathogen associated molecular patterns (PAMPs) and danger associated molecular patterns (DAMPs) receptors, acting as nutrient sensors, may induce an inflammatory cascade that affects insulin signaling with development of insulin resistance [142]. In a prospective, observational cohort study in children with meningococcal sepsis and septic shock [143] hyperglycemia (glucose >8.3 mmol/l) was present in 33 % of the children on admission whereas 62 % of the hyperglycemic children had overt insulin resistance (glucose >8.3 mmol/l and homeostasis model assessment (HOMA) [β-cell function <50 %), 17 % had β-cell dysfunction, and 21 % had both insulin resistance and β-cell dysfunction. Normalization of blood glucose levels occurred within 48 h, typically with normal glucose intake and without insulin treatment [143].

In the face of stress-induced hyperglycemia, the provision of dextrose infusion in the form of parenteral nutrition (PN) can further exacerbate hyperglycemia, which can lead to increased infectious complications and increased mortality [144]. Landmark trials in adults by Van den Berghe et al. suggested that targeting normoglycemia (a blood glucose concentration of 80–110 mg/dL [4.4–6.1 mmol/L]) reduced mortality and morbidity [145], but other investigators have not been able to replicate these findings. Recently, the international multicenter Normoglycemia in Intensive Care Evaluation-Survival Using Glucose Algorithm Regulation (NICE-SUGAR) study reported increased mortality with this approach, and recent meta-analyses do not support intensive glucose control for critically ill patients [146]. Although the initial trials in Leuven produced enthusiasm and recommendations for intensive blood glucose control, the results of the NICE-SUGAR study have resulted in the more moderate recommendation to target a blood glucose concentration between 144 and 180 mg/dL (8–10 mmol/L) [146]. Thus, it was recently shown that the incidence of hypoglycemia was significantly higher with intensive insulin therapy (absolute risk increase 23.5 %, number needed to harm 4) [147]. Studies in children also suggest that special consideration should be given to the safety of the youngest patients given their higher risk of hypoglycemia if an investigation of tight glycemic control is performed [148]. Especially high rates of hypo-/hyperglycemia are noted in sicker patients and in those requiring more therapeutic interventions. Adding to this skepticism, it has been recently shown that the current recommended parenteral amino acid intakes are insufficient to maintain protein balance in insulin-resistant patients during tight glucose control [149]. Concerns are raised that high amino acid intakes may exacerbate insulin resistance and favor gluconeogenesis, thereby offsetting their beneficial effects on protein balance by enhancing endogenous glucose production and lipolysis [149].

Cell-Mediated Immunity and Lymphopenia

Total lymphocyte counts and impaired cell-mediated immunity have been correlated with nutritional status. These may be difficult to interpret in children, given the variable response of an immature immune system. Additionally, numerous other factors, including sepsis, cancer, collagen vascular diseases, uremia, hepatic dysfunction, and drug administration may impair cell-mediated immunity. Quantification of T-lymphocyte subpopulations, with particular reference to killer cells, may be more specific [150]. However, in the critically ill child, many factors can alter delayed cutaneous hypersensitivity and render it useless in assessing the state of nutrition. Therefore, immunity is neither a specific indicator of malnutrition nor is it easily studied.

Delayed cutaneous hypersensitivity, which results from the inoculation of antigens such as Candida spp., Trichophyton spp., or the mumps virus, has been used to measure immunological competence and, indirectly, nutritional status. These tests are influenced by a number of other situations that cause anergy, such as various drugs (especially steroids and antirejection drugs), the presence of infection, malignancy, and burns, among others [151].

Functional Tests of Malnutrition

The use of exercise tolerance by ergometers and measurement of heart rate are useful for population studies but difficult for sick patients with cardiorespiratory impairment and for children in intensive care. Grip strength, respiratory muscle strength, and function by electrical stimulation all demonstrate changes with nutrition. Among them, relaxation rate of single twitches may be a simple, non-invasive, and reproducible way of studying function in sick patients [152].

Clinical Data Impacting Nutrition and Metabolic Response Monitoring


It is generally accepted that the degree of catabolism of the acutely ill child reflects the degree of stress of the individual, since with more stress there is more neurohumoral activation and more muscle proteolysis [153]. It is known that during stress, interleukin-6 increases plasma arginine vasopressin, indicating that this cytokine has a role in the inappropriate secretion of antidiuretic hormone that can occur in patients with infectious or inflammatory diseases or trauma [154].

Counter-Regulatory Stress Hormones

In addition to their short-term effects on the hypothalamus, the inflammatory cytokines can apparently stimulate pituitary corticotrophin and adrenal cortisol secretion directly by interacting with these tissues [155]. Accordingly, it has been shown that hormonal acute stress responses may explain the shift toward fat oxidation and either gluconeogenesis or impaired peripheral carbohydrate uptake, but does not quantitatively affect energy expenditure [156]. Similarly, glucocorticoids may increase nitrogen wasting in head-injured patients without increasing metabolic rate. Although counter-regulatory stress hormones do not cause hypoalbuminemia in healthy volunteers, they do produce protein catabolism [157]. It has been postulated that stress hormones alter the configuration of ribosomes in muscle, decreasing protein synthesis, inducing proteolysis, and fluxing essential amino acids for high priority use in other tissues [158]. Another study has also provided evidence that nutrition intervention may modulate cortisol-binding globulin and the concentration of free circulating cortisol after a severe stress [159]. On the contrary, supplemental insulin may have provided mild improvement in nitrogen utilization, probably related to the insulin effect on the skeletal muscle. This hormone is known to increase protein synthesis in skeletal muscle [160] and decrease degradation in liver and muscle [161].

Drugs Influencing Monitoring

Catecholamines are primary mediators of elevated energy expenditure and tissue catabolism in critically ill patients. Systemic corticosteroids also induce a hypermetabolic response and increase protein catabolism. Long-term beta receptor blockade was capable of decreasing REE and tissue catabolism [162]. This effect was associated with an improvement in both muscle protein balance as well as body cell mass conservation. It was also found that propranolol induced an increase in intracellular recycling of free amino acids. Opiates, muscle relaxants, and barbiturates variably significantly reduce energy expenditure [163, 164].

Critical Illness

Liver dysfunction is common in critically ill patients, caused by shock or hypodynamic circulatory states, intra and extra-abdominal infections, drugs, infectious hepatitis, as well as metabolic and nutritional causes. The metabolic changes induced by critical illness and inadequate nutritional supply foster the development of fatty liver. The increased release of stress hormones, proinflammatory cytokines and other inflammatory mediators, as well as insulin resistance, are hallmarks of the physiological response to injury. Initial assessment of these critically children would probably show characteristic metabolic changes, such as hyperglycemia, increased hepatic glucose production, increased lipolysis and stimulation of the de novo lipogenesis pathway.

Excessive fluid therapy has emerged as a new mechanism of gastrointestinal failure in critically ill patients during the past few years [165]. While timely administration of fluids is lifesaving, positive fluid balance after hemodynamic stabilization may affect the PICU course in children who do not receive renal replacement therapy by impacting organ function and negatively influencing important outcomes in critically ill patients [166]. More specifically, excessive fluid administration may harm the abdominal organs, because it increases intra-abdominal pressure and fosters the development of abdominal compartment syndrome. The latter is characterized by high intraabdominal pressure and decreased abdominal perfusion pressure, and is associated with signs of abdominal organ hypoperfusion, multiple organ failure, and decreased survival. Monitoring of patients with major burns or trauma shows that excessive fluid therapy exerts deleterious effects on the gut, delaying the return of gastrointestinal functions and preventing the use of early enteral feeding [167].

Nutrition Monitoring

Early Nutrition Monitoring

Among patients who have protein-energy malnutrition at the time of admission to the ICU and enteral feeding is not possible, the American clinical practice guidelines suggest that PN should be initiated without delay [168]. Although the time frame for initiation of PN to supplement patients who are receiving inadequate EN or no EN is not specified by the A.S.P.E.N. pediatric critical care nutrition guidelines [169], the European Society for Clinical Nutrition and Metabolism (ESPEN) recommends PN in 24–48 h if EN will be contraindicated for 3 days, or after 48 h to supplement insufficient EN in critically ill adults [112]. PN infusion protocols, therefore, should always be in place to assure safe administration and close monitoring for the metabolic complications of refeeding syndrome, tolerance of electrolytes and macronutrients, as well as glucose control is necessary; [170] monitoring of specific micronutrients is crucial in long-term PN usage. Since the use of EN as opposed to PN results in an important decrease in the incidence of infectious complications in the critically ill and is less costly, should be the first choice for nutritional support in the critically ill [171]. Fortunately, most critically ill patients who require specialized nutrition (85–90 %) can be fed enterally through gastric or intestinal tubes [166], whereas increases of caloric intake during the acute phase of a critical illness are well tolerated in children and may approach PBMR by the second day and PEE by the fourth day [4].

The refeeding syndrome is of particular importance to critically ill patients, who can be moved from the starved state to the fed state rapidly via enteral or parenteral nutrition, but is often under-appreciated [172]. There are a variety of risk factors for the development of the refeeding syndrome, but all are tied together by starvation physiology. Complications of the refeeding syndrome can include hypophosphatemia, hypokalemia, hypomagnesemia, rapid fluid shifts, peripheral edema, and sometimes thiamine deficiency, heart failure, respiratory failure, and death [173]. The most commonly seen abnormality is hypophosphatemia, which should be monitored very closely and replenished as needed to avoid heart failure, arrhythmia, and life-threatening respiratory failure [174]. An initial phosphate depleted state is further exacerbated by the introduction of dextrose infusion. Insulin leads to an increase in cellular uptake of phosphate, as well as increased synthesis of ATP, 2,3DPG, and creatine phosphokinase, all leading to decreased serum phosphorus levels. In addition, accelerated carbohydrate metabolism increases the body’s use of thiamine and can precipitate symptoms and signs of thiamine deficiency [175].

The Immunonutrition Question

It is not known if a low plasma glutamine or selenium concentration is an independent prognostic factor for an unfavorable outcome in the PICU, so that their monitoring is not currently recommended. Recently, a reduced adult ICU mortality was observed during intravenous glutamine supplementation in a broad range of ICU patients [176]. However, no change in the SOFA score was recorded and mortality did not differ at 6 months. Similarly, in a randomized, double blinded, factorial, controlled multicenter trial, the primary (intention to treat) analysis showed no effect on new infections or on mortality when PN was supplemented with glutamine or selenium [177]. Only patients who received PN supplemented with the antioxidant selenium for ≥5 days did show a reduction in new infections.

In a blinded, prospective, randomized, controlled clinical trial, nitrogen balance, nutritional indices, antioxidant catalysts, and outcome were compared in critically ill children given an immune-enhancing formula (IE) or conventional early EN (C) [178]. Although it had a favorable effect on nitrogen balance, nutritional indices and antioxidant catalysts, it did not influence outcome hard endpoints. In group IE nitrogen balance became positive by day 5 compared with group C in which the mean nitrogen balance remained negative (p < 0.001). Also, early IE nutrition was shown to modulate cytokines in children with septic shock, but again there was no evidence that this immunomodulation has any impact on short-term outcome [179]. On day 5 IL-6 levels were significantly lower and IL-8 significantly higher in the IE than in the C group, whereas after 5 days of nutritional support a significant decrease in IL-6 levels was recorded only in group IE. In another randomized study in children with severe head injury, nitrogen balance became positive in 30.8 % of patients in the C group and in 69.2 % of patients in the IE group by day 5 [180]. It was also shown however, that although it decreased interleukin-8 and gastric colonization, it was not associated with additional advantage over the one demonstrated by regular early enteral nutrition.

Protocols in the Role of Monitoring

To account for alterations in energy metabolism, caloric amounts equal to the measured REE [167, 181] or, if not available, to the PBMR should be provided during the acute metabolic stress period [4]. Especially, targeted indirect calorimetry may allow detection of an altered metabolic state energy imbalance in a subset of critically ill children at a high risk of overfeeding, such as those with existing malnutrition on admission, prolonged stay in the ICU, and those who are unable to wean from mechanical ventilatory support, having therefore a role in optimizing energy intake in the PICU [124]. On the other hand, inadequate nutritional intake in the PICU, often due to fluid restriction, further leads to protein and energy deficits, especially early after admission [182]. Other factors that hinder adequate nutrition are impaired intracellular insulin signaling [183], impaired glucose uptake [184] and reduced mitochondrial capacity during critical illness [185]. These factors are probably the reason why protein-energy malnutrition is observed in 16–24 % of critically ill children and is associated with adverse clinical outcome [43, 186].

Mechanically ventilated subjects are at the highest risk of EN interruptions. It was recently shown that avoidable EN interruption was associated with increased reliance on PN and impaired ability to reach caloric goal [187]. EN interruption, however, is frequently avoidable in critically ill children; knowledge of existing barriers to EN combined with institution of protocolized feeding approach may allow appropriate interventions to optimize nutrition provision in the PICU [184]. In a recent study aimed to assess the impact of enteral feeding protocols on nutritional support practices through a continuous auditing process over a defined period it was found that the time taken to initiate nutrition support was reduced from 15 to 4.5 h [188]. Simultaneously, an increase was documented in the percentage of patients receiving a daily energy provision of up to 70 % of the estimated average requirement, whereas the proportion of patients on parenteral feeds was reduced from 11 to 4 %. In a multicenter adult study, on average, protocolized sites used more EN alone (70.4 % of patients vs 63.6 %, p = 0.0036), started EN earlier (41.2 h from admission to ICU vs 57.1, p = 0.0003), and used more motility agents in patients with high gastric residual volumes (64.3 % of patients vs 49.0 %, p = 0.0028) compared with sites that did not use a feeding protocol [189]. Importantly, in a 7-day prospective before–after study, early EN without residual gastric volume monitoring in mechanically ventilated adult patients improved the delivery of enteral feeding and did not increase vomiting or ventilator associated pneumonia [190]. Awaiting confirmatory studies before removing the residual gastric volume assessment from their ICUs, however, clinicians are advised to take guidance from published evidence-based guidelines [191].

Of equal significance of this therapeutic protocols strategy is also to avoid the provision of calories and nutritional substrates which the patient cannot probably handle in order to maintain the metabolic homeostasis of the acute stress response [2]. An increase of caloric intake during the acute phase of a stress state has been shown to be feasible and well tolerated in non-cardiac critically ill children [5]. Also, increased protein and energy intakes have been recommended in critically ill infants with viral bronchiolitis [99]. However, future studies will need to examine the safety of such protocols and the impact of large cumulative energy excess on patient outcomes. In fact, many chronically ill children with malnutrition would be rather overfed (if their daily energy requirements were calculated based on PEE during acute illness) than underfed (Fig. 42.2 [192]). Monitoring of energy expenditure, therefore, has been used to characterize alterations in metabolism accompanying critical illness and to provide accurate information necessary for appropriate nutritional repletion, including the type and amount of macronutrient substrates that exactly meets the patient’s energy requirements and avoids the complications of overfeeding [193]. Accordingly, it has been shown that when caloric intake was less than REE, mean substrate utilization was 48.6 % from lipid, 37.1 % from carbohydrate but, when it was greater than REE, mean substrate utilization was 83.3 % from carbohydrate and 16.7 % from protein [60].
Fig. 42.2

Boxplots of energy intake and resting energy expenditure measured by indirect calorimetry (MREE) or basal metabolic rate (without stress factors) predicted by the SCHOHW or the White equations (Courtesy of G. Briassoulis)

Recent studies have shown that computerized information systems do improve nutritional monitoring (energy delivery and balance, protein and fat delivery), quality of nutrition, glucose control, and reduce nurse workload associated with the multiple balance calculations and ease visualization of events out of planned targets [194]. Overfeeding, particularly carbohydrate overfeeding, increases ventilatory work by increasing CO2 production, can potentially prolong the need for mechanical ventilation, may increase the risk of infection secondary to hyperglycemia, and can impair liver function by inducing hepatic steatosis and cholestasis [195]. Azotemia can result from overzealous protein infusion, whereas fat-overload syndrome can result from either overall total calorie overfeeding, overfeeding of lipids, or both [196]. Algorithms to control glucose using insulin therapy and alterations in formula administration are intended to prevent hyperglycemia, under close glucose control, and increase synthesis of fatty acids from glucose and other non-lipid precursors in the liver and in peripheral tissues [197]. Parenteral intakes of essential and nonessential amino acids supplied to critically ill children are supplied in lower or higher amounts than the content of mixed muscle proteins or breast milk and are not based on measured requirements to maintain nutrition and functional balance and on knowledge of toxicity [198]. Instead, protocol-driven implementation of nutrition therapy by the third day of admission to the PICU with goal intake achieved by the end of the first week was recently shown to help preserve lean body mass in a group of children with a high prevalence of baseline malnutrition [33].

Monitoring the Metabolic Response

Acute stress may result in a substantial decrease of energy needs. The acute stress may induce a catabolic response that is proportional to the magnitude, nature, and duration of the injury. Increased serum counter-regulatory hormone concentrations induce insulin and growth hormone resistance, resulting in the catabolism of endogenous stores of protein, carbohydrate, and fat to provide essential substrate intermediates and energy necessary to support the ongoing metabolic stress response. During this catabolic response, somatic growth cannot occur and, therefore, the caloric allotment for growth, which is substantial in infancy, should not be administered. The intensive care environment is temperature-controlled, and insensible energy losses are substantially reduced and most patients are ventilated with heated, humidified air, thus reducing insensible losses by one third. In addition, children treated in the intensive care setting are frequently sedated and mechanically ventilated, so that their work of breathing and activity level are markedly reduced further lowering energy needs [199]. Similarly, various pharmacologic agents and the capacity of the patient to respond to the metabolic demands imposed by the injury might further alter the metabolic response [200].

Comparing simultaneous REE and PBMR recordings, patients may be classified as hypermetabolic, normometabolic, and hypometabolic when REE is >110, 90–110 % and, <90 % of the PBMR, respectively. Although sustained hypermetabolism has been reported for weeks after burn injury, REE peak returns to baseline within 12 h after some surgical procedures [201]. More studies in children [2, 118] and adults [202] have now verified results of a pioneer indirect calorimetry study reporting lack of hypermetabolic response during critical illness [4]. Using various equations to predict acute phase energy expenditure in mechanically ventilated children whose REE was continuously monitored through an E-COVX metabolic monitor, the mean REE/PBMR ratio was <1 in all but one (Fig. 42.3, G. Briassoulis et al. University of Crete, unpublished work). In a study examining the metabolic patterns in pediatric patients with critical illness, it was shown that the initial predominance of the hypometabolic pattern (48.6 %) declined within 1 week of acute stress (20 %), and the hypermetabolic patterns dominated only after 2 weeks (60 %) [185]. High IL-10 levels and low measured REE were independently associated with mortality (11.7 %), which was higher in the hypometabolic compared to other metabolic patterns. However, although in SIRS or sepsis the cytokine response was reliably reflected by increases in Nutritional Index and triglycerides, it was different from the metabolic (VO2, VCO2) or glucose response [201].
Fig. 42.3

Boxplots of ratios of resting energy expenditure (REE) measured by E-COVX metabolic monitor/basal metabolic rates (PBMR) predicted by various equations without stress factors in children during critical illness. Equations used are shown by their names (Courtesy of G. Briassoulis)

The SIRS elicited by peritonitis in mice was accompanied by mitochondrial energetic metabolism deterioration and reduced peroxisome proliferator-activated receptor gamma coactivator (PGC)-1alpha protein expression [203]. Because ATP production by mitochondrial oxidative phosphorylation accounts for more than 90 % of total oxygen consumption, a severe mitochondrial dysfunction implicating bioenergetic failure during stress might explain both: a predominant hypometabolic pattern and the raised tissue oxygen tensions in septic animals and human beings. Performing skeletal muscle biopsies on 28 critically ill septic patients within 24 h of admission to intensive care, Brealey et al. [204] showed that skeletal muscle ATP concentrations were significantly lower in patients with sepsis who subsequently died than in septic patients who survived and in controls and that complex I respiratory-chain activity had a significant inverse correlation with norepinephrine requirements and a significant positive correlation with concentrations of reduced glutathione and ATP. Electron paramagnetic resonance spectra analysis of the paramagnetic centres in the muscle confirmed that a decreased concentration of mitochondrial Complex I iron-sulfur redox centres is linked to mortality [205].


Nutritional monitoring should be an integral part of the care for every pediatric critically ill patient. The nutrition monitoring records the changing nutrition status of the critically ill child and facilitates the development of a nutrition care plan. However, there is little research in the area of pediatric nutrition monitoring upon which to formulate evidence based practice guidelines. A nutrition screen, incorporating objective data such as height, weight, arm circumference, triceps skinfold, primary diagnosis, and presence of co-morbidities should be a component of the initial evaluation of all pediatric patients in an intensive care setting. Following that, repeated anthropometric and laboratory markers must be jointly analyzed, but individually interpreted according to disease and metabolic changes, in order to modify and monitor the nutritional treatment. The recently revised national guidelines for adult and pediatric critical care have emphasized the importance of accurately measured energy expenditure in patients admitted to the intensive care unit. Increases of caloric intake during the acute phase of a critical illness are well tolerated in children and may approach PBMR by the second day and PEE by the fourth day. Over the course of the disease, it seems that the most practical tool is metabolic assessment based on the combination of indirect calorimetry, nitrogen balance, plasma proteins. Since the nutrition monitoring can be viewed as an ongoing process, particularly in the acute care setting, it provides accurate information necessary for appropriate nutritional repletion and helps avoiding the complications of under- and overfeeding. Accordingly, as part of the nutrition care process, the energy expenditure and metabolic monitoring using targeted indirect calorimetry, should be completed and updated at specific intervals, as warranted by metabolic alterations in the patient’s needs or condition.


  1. 1.
    Joosten KF, Hulst JM. Malnutrition in pediatric hospital patients: current issues. Nutrition. 2011;27(2):133–7.PubMedGoogle Scholar
  2. 2.
    Mehta NM, Bechard LJ, Dolan M, Ariagno K, Jiang H, Duggan C. Energy imbalance and the risk of overfeeding in critically ill children. Pediatr Crit Care Med. 2011;12(4):398–405.PubMedGoogle Scholar
  3. 3.
    Hendricks KM, Duggan C, Gallagher L, Carlin AC, Richardson DS, Collier SB, Simpson W, Lo C. Malnutrition in hospitalized pediatric patients. Arch Pediatr Adolesc Med. 1995;149:1118–22.PubMedGoogle Scholar
  4. 4.
    Briassoulis GC, Zavras NJ, Hatzis TD. Effectiveness and safety of a protocol for promotion of early intragastric feeding in critically ill children. Pediatr Crit Care Med. 2001;2:113–21.PubMedGoogle Scholar
  5. 5.
    Taylor RM, Preedy VR, Baker AJ, Grimble G. Nutritional support in critically ill children. Clin Nutr. 2003;22:365–9.PubMedGoogle Scholar
  6. 6.
    Rogers EJ, Gilbertson HR, Heine RG, Henning R. Barriers to adequate nutrition in critically ill children. Nutrition. 2003;19:865–8.PubMedGoogle Scholar
  7. 7.
    De Wit B, Meyer R, Desai A, Macrae D, Pathan N. Challenge of predicting resting energy expenditure in children undergoing surgery for congenital heart disease. Pediatr Crit Care Med. 2010;11(4):496–501.PubMedGoogle Scholar
  8. 8.
    Mehta NM, Duggan CP. Nutritional deficiencies during critical illness. Pediatr Clin North Am. 2009;56(5):1143–60.PubMedGoogle Scholar
  9. 9.
    Waitzberg DL, Caiaffa WT, Correia MITD. Hospital malnutrition: the Brazilian national survey (IBRANUTRI): a study of 4000 patients. Nutrition. 2001;17:575–80.Google Scholar
  10. 10.
    Jones JM. The methodology of nutritional screening and assessment tools. J Hum Nutr Diet. 2002;15:59–71.PubMedGoogle Scholar
  11. 11.
    Detsky AS, Smalley PS, Chang J. Is this patient malnourished? JAMA. 1994;271:54–8.PubMedGoogle Scholar
  12. 12.
    Detsky AS, McLaughlin JR, Baker JP, Johnston N, Whittaker S, Mendelson RA, Jeejeebhoy KN. What is subjective global assessment of nutritional status? JPEN J Parenter Enteral Nutr. 1987;11:8–13.PubMedGoogle Scholar
  13. 13.
    Fiaccadori E, Lombardi M, Leonardi S, Rotelli CF, Tortorella G, Borghetti A. Prevalence and clinical outcome associated with preexisting malnutrition in acute renal failure: a prospective cohort study. J Am Soc Nephrol. 1999;10:581–93.PubMedGoogle Scholar
  14. 14.
    Secker DJ, Jeejeebhoy KN. Subjective global nutritional assessment for children. Am J Clin Nutr. 2007;85(4):1083–9.PubMedGoogle Scholar
  15. 15.
    WHO. Physical status: the use and interpretation of anthropometry. Report of a WHO Expert Commitee, WHO Technical Report Series No. 854. Geneva: WHO; 1995.Google Scholar
  16. 16.
    NCHS (National Center for Health Statistic). Growth curves children birth–18. Washington, DC: National Center for Health Statistics; 2000.Google Scholar
  17. 17.
    World Health Organization Multicentre Growth Reference Study Group. WHO child growth standards based on length/height, weight and age. Acta Paediatr Suppl. 2006;450:76S–85.Google Scholar
  18. 18.
    Hermanussen M, Aßmann C, Wöhling H, Zabransky M. Harmonizing national growth references for multi-centre surveys, drug monitoring and international postmarketing surveillance. Acta Paediatr. 2012;101(1):78–84.PubMedCentralPubMedGoogle Scholar
  19. 19.
    Brooks J, Day S, Shavelle R, Strauss D. Low weight, morbidity, and mortality in children with cerebral palsy: new clinical growth charts. Pediatrics. 2011;128(2):e299–307.PubMedGoogle Scholar
  20. 20.
    Buyukgebiz B, Ozturk Y, Yilmaz S, Arslan N. Serum leptin concentrations in children with mild-to-moderate protein-energy malnutrition. Pediatr Int. 2003;45:550–4.PubMedGoogle Scholar
  21. 21.
    Finkielman J, Gajic O, Afessa B. Underweight is independently associated with mortality in postoperative and non-operative patients admitted to the intensive care unit: a retrospective study. BMC Emerg Med. 2004;4:1–7.Google Scholar
  22. 22.
    Garrouste-Orgeas M, Troché G, Azoulay E, Caubel A, de Lassence A, Cheval C, Montesino L, Thuong M, Vincent F, Cohen Y, Timsit JF. Body mass index. An additional prognostic factor in ICU patients. Intensive Care Med. 2004;30(3):437–43.PubMedGoogle Scholar
  23. 23.
    Waterlow JC. Classification and definition of protein – calorie malnutrition. Br Med J. 1972;3:566–9.PubMedCentralPubMedGoogle Scholar
  24. 24.
    Shoeps DO, de Abreu LC, Valenti VE, Nascimento VG, de Oliveira AG, Gallo PR, Wajnsztejn R, Leone C. Nutritional status of pre-school children from low income families. Nutr J. 2011;10:43.PubMedCentralPubMedGoogle Scholar
  25. 25.
    Onis M, Yip R, Mei Z. The development of PMB-for-age reference datarecommended by a WHO Expert Committee. Bull World Health Organ. 1997;75:11–8.PubMedCentralPubMedGoogle Scholar
  26. 26.
    Soler-Cataluña JJ, Sánchez-Sánchez L, Martínez-García MA, Sánchez PR, Salcedo E, Navarro M. Mid-arm muscle area is a better predictor of mortality than body mass index in COPD. Chest. 2005;128:2108–15.PubMedGoogle Scholar
  27. 27.
    Fomon SJ. Nutritional disorders of children. Prevention, screening, and follow-up. Rockville: Department of Health. Education and Welfare Publication No (HSA); 1974. p. 77–5104.Google Scholar
  28. 28.
    Frisancho AR. Triceps skinfold and upper arm muscle size norms for assessment of nutritional status. Am J Clin Nutr. 1974;27:1052–8.PubMedGoogle Scholar
  29. 29.
    Frisancho AR. New norms of upper limb fat and muscle areas for assessment of nutritional status. Am J Clin Nutr. 1981;34:2540–5.PubMedGoogle Scholar
  30. 30.
    Ryan A, Martinez GA. Physical growth of infants 7 to 13 months of age: results from a national survey. Am J Phys Anthropol. 1987;73:449.PubMedGoogle Scholar
  31. 31.
    Gurney JM, Jelliffe DB. Arm anthropometry in nutritional assessment: nomogram for rapid calculation of muscle circumference and cross-sectional muscle and fat areas. Am J Clin Nutr. 1973;26:912–5.PubMedGoogle Scholar
  32. 32.
    Ravasco P, Camilo ME, Gouveia-Oliveira A, Adam S, Brum G. A critical approach to nutritional assessment in critically ill patients. Clin Nutr. 2002;21:73–7.Google Scholar
  33. 33.
    Zamberlan P, Delgado AF, Leone C, Feferbaum R, Okay TS. Nutrition therapy in a pediatric intensive care unit: indications, monitoring, and complications. JPEN J Parenter Enteral Nutr. 2011;35(4):523–9.PubMedGoogle Scholar
  34. 34.
    Oguz A, Karadeniz C, Pelit M, Hasanoglu A. Arm anthropometry in evaluation of malnutrition in children with cancer. Pediatr Hematol Oncol. 1999;16:35–41.PubMedGoogle Scholar
  35. 35.
    Orsi CM, Hale DE, Lynch JL. Pediatric obesity epidemiology. Curr Opin Endocrinol Diabetes Obes. 2011;18:14–22.PubMedGoogle Scholar
  36. 36.
    Hogue Jr CW, Stearns JD, Colantuoni E, Robinson KA, Stierer T, Mitter N, Pronovost PJ, Needham DM. The impact of obesity on outcomes after critical illness: a meta-analysis. Intensive Care Med. 2009;35:1152–70.PubMedGoogle Scholar
  37. 37.
    Martino JL, Stapleton RD, Wang M, Day AG, Cahill NE, Dixon AE, Suratt BT, Heyland DK. Extreme obesity and outcomes in critically ill patients. Chest. 2011;140(5):1198–206.PubMedCentralPubMedGoogle Scholar
  38. 38.
    Ziegler TR. Parenteral nutrition in the critically ill patient. N Engl J Med. 2009;361(11):1088–97.PubMedCentralPubMedGoogle Scholar
  39. 39.
    Villet S, Chiolero RL, Bollmann MD, Revelly JP, Cayeux RNMC, Delarue J, Berger MM. Negative impact of hypocaloric feeding and energy balance on clinical outcome in ICU patients. Clin Nutr. 2005;24(4):502–9.PubMedGoogle Scholar
  40. 40.
    Correia MI, Waitzberg DL. The impact of malnutrition on morbidity, mortality, length of hospital stay and costs evaluated through a multivariate model analysis. Clin Nutr. 2003;22:235–9.PubMedGoogle Scholar
  41. 41.
    Waitzberg DL. Efficacy of nutritional support: evidence-based nutrition and cost-effectiveness. Nestle Nutr Workshop Ser Clin Perform Programme. 2002;7:257–71.PubMedGoogle Scholar
  42. 42.
    Pollack MM, Wiley JS, Holbrook PR. Early nutritional depletion in critically ill children. Crit Care Med. 1981;9:580–3.PubMedGoogle Scholar
  43. 43.
    Briassoulis G, Zavras N, Hatzis T. Malnutrition, nutritional indices, and early enteral feeding in critically ill children. Nutrition. 2001;17:548–57.PubMedGoogle Scholar
  44. 44.
    Chandra RK. Nutrition and immunology: from the clinic to cellular biology and back again. Proc Nutr Soc. 1999;58:681–3.PubMedGoogle Scholar
  45. 45.
    Baue AE. Nutrition and metabolism in sepsis and multisystem organ failure. Surg Clin North Am. 1991;71:549–65.PubMedGoogle Scholar
  46. 46.
    Chang HR, Bistrian B. The role of cytokines in the catabolic consequences of infection and injury. JPEN J Parenter Enteral Nutr. 1998;22:156–66.PubMedGoogle Scholar
  47. 47.
    Fleck A, Raines G, Hawker F, Trotter J, Wallace PI, Ledingham IM, Calman KC. Increased vascular permeability: a major cause of hypoalbuminemia in disease and injury. Lancet. 1985;1:781–4.PubMedGoogle Scholar
  48. 48.
    Brugler L, Stankovic A, Bernstein L. The role of visceral protein markers in protein calorie malnutrition. Clin Chem Lab Med. 2002;40:1360–9.PubMedGoogle Scholar
  49. 49.
    Jeejeebhoy KN. Nutritional assessment. Gastroenterol Clin N Am. 1998;27:347–69.Google Scholar
  50. 50.
    Covinsky KE, Covinsky MH, Palmer RM, Sehgal AR. Serum albumin concentration and clinical assessments of nutritional status in hospitalized older people: different sides of different coins? J Am Geriatr Soc. 2002;50:631–7.PubMedGoogle Scholar
  51. 51.
    Botrán M, López-Herce J, Mencía S, Urbano J, Solana MJ, García A. Enteral nutrition in the critically ill child: comparison of standard and protein-enriched diets. J Pediatr. 2011;159(1):27–32.PubMedGoogle Scholar
  52. 52.
    Buzby GP, Mullen JP, Matthews DC. Prognostic nutritional index in gastrointestinal surgery. Am J Surg. 1980;139:160–7.PubMedGoogle Scholar
  53. 53.
    Bistrian BR, Blackburn GL, Sherman M, Scrimsaw NS. Therapeautic index of nutritional depletion in hospitalized patients. Surg Gynecol Obstet. 1975;141:512–6.PubMedGoogle Scholar
  54. 54.
    Hall JC. Use of internal validity in the construct of an index of undernutrition. JPEN J Parenter Enteral Nutr. 1990;14:582–7.PubMedGoogle Scholar
  55. 55.
    Chwals WJ, Letton RW, Jamie A, Charles B. Stratification of injury severity using energy expenditure response in surgical infants. J Pediatr Surg. 1995;30:1161–4.PubMedGoogle Scholar
  56. 56.
    Briassoulis G, Tsorva A, Zavras N, Hatzis T. Influence of an aggressive early enteral nutrition protocol on nitrogen balance in critically ill children. J Nutr Biochem. 2002;13:560–9.PubMedGoogle Scholar
  57. 57.
    Sandstrom R, Hyltander A, Korner U, Lundholm K. The effect on energy and nitrogen metabolism by continuous, bolus, or sequential infusion formulation in patients after major surgical procedures. JPEN J Parenter Enteral Nutr. 1996;19:333–40.Google Scholar
  58. 58.
    Reeds PJ, Wahle KW, Haggarty P. Energy costs of protein and fatty acid synthesis. Proc Nutr Soc. 1982;41:155–9.PubMedGoogle Scholar
  59. 59.
    Freund H, Gimmon Z, Fischer JE. Nitrogen sparing effects and mechanism of branched chain amino acids in the injured rat. Clin Nutr. 1982;1:137–46.PubMedGoogle Scholar
  60. 60.
    Briassoulis G, VenkataramanS, Thompson A. Nutritional-metabolic factors affecting nitrogen balance and substrate utilization in the critically ill. J Pediatr Intensive Care. 2012;1(2):77–86.Google Scholar
  61. 61.
    Nettleton JA, Hegsted DM. Protein-energy interrelationships during dietary restriction: effects on tissue nitrogen and protein turnover. Nutr Metab. 1975;18:31–40.PubMedGoogle Scholar
  62. 62.
    Coss-Bu JA, Klish WJ, Walding D, Stein F, Smith EO, Jefferson LS. Energy metabolism, nitrogen balance, and substrate utilization in critically ill children. Am J Clin Nutr. 2001;74:664–9.PubMedGoogle Scholar
  63. 63.
    Millward DJ, Bates PC, Grimble GK, Brown JG, Nathan M, Rennie MJ. Quantitative importance of non-skeletal-muscle sources of N-methylhistidine in urine. Biochem J. 1980;190:225–8.PubMedCentralPubMedGoogle Scholar
  64. 64.
    Botrán M, López-Herce J, Mencía S, Urbano J, Solana MJ, García A, Carrillo A. Relationship between energy expenditure, nutritional status and clinical severity before starting enteral nutrition in critically ill children. Br J Nutr. 2011;105(5):731–77.PubMedGoogle Scholar
  65. 65.
    Weissman C, Kemper M, Elwyn DH, Askanazi J, Hyman AI, Kinney JM. The energy expenditure of the mechanically ventilated critically ill patient: an analysis. Chest. 1986;89:254–9.PubMedGoogle Scholar
  66. 66.
    Weissman C, Kemper M, Hyman AI. Variation in the resting metabolic rate of mechanically ventilated critically ill patients. Anesth Analg. 1989;68:457–61.PubMedGoogle Scholar
  67. 67.
    Bursztein S, Elwyn DH, Askanazi J. Energy metabolism and indirect calorimetry in critically ill and injured patients. Acute Care. 1988–89;14–15:91–110.Google Scholar
  68. 68.
    Weir JB. New methods for calculating metabolic rate with special reference to protein metabolism. 1949. Nutrition. 1990;6:213–21.PubMedGoogle Scholar
  69. 69.
    Askanazi J, Rosenbaum SH, Hyman AI, Silverberg PA, Milic-Emili J, Kinney JM. Respiratory changes induced by the large glucose loads of total parenteral nutrition. JAMA. 1980;243:1444–7.PubMedGoogle Scholar
  70. 70.
    Covelli HD, Black JW, Olsen MS, Beekman JF. Respiratory failure precipitated by high carbohydrate loads. Ann Intern Med. 1981;95:579–81.PubMedGoogle Scholar
  71. 71.
    Guenst JM, Nelson LD. Predictors of total parenteral nutrition-induced lipogenesis. Chest. 1994;105:553–9.PubMedGoogle Scholar
  72. 72.
    McClave SA, Lowen CC, Kleber MJ, McConnell JW, Jung LY, Goldsmith LJ. Clinical use of the respiratory quotient obtained from indirect calorimetry. JPEN J Parenter Enteral Nutr. 2003;27:21–6.PubMedGoogle Scholar
  73. 73.
    Hulst JM, van Goudoever JB, Zimmermann LJ, Hop WC, Büller HA, Tibboel D, Joosten KF. Adequate feeding and the usefulness of the respiratory quotient in critically ill children. Nutrition. 2005;21:192–8.PubMedGoogle Scholar
  74. 74.
    van der Kuip M, de Meer K, Westerterp KR, Gemke RJ. Physical activity as a determinant of total energy expenditure in critically ill children. Clin Nutr. 2007;26(6):744–51.PubMedGoogle Scholar
  75. 75.
    Swinamer DL, Phang PT, Jones RL, Grace M, King EG. Twenty-four hour energy expenditure in critically ill patients. Crit Care Med. 1987;15:637–43.PubMedGoogle Scholar
  76. 76.
    McClave SA, Lowen CC, Kleber MJ, Nicholson JF, Jimmerson SC, McConnell JW, Jung LY. Are patients fed appropriately according to their caloric requirements? JPEN J Parenter Enteral Nutr. 1998;22:375–81.PubMedGoogle Scholar
  77. 77.
    Frayn KN. Calculation of substrate oxidation rates in vivo from gaseous exchange. J Appl Physiol. 1983;55:628–34.PubMedGoogle Scholar
  78. 78.
    Joosten KF, Jacobs FI, van Klaarwater E, Baartmans MG, Hop WC, Meriläinen PT, Hazelzet JA. Accuracy of an indirect calorimeter for mechanically ventilated infants and children: the influence of low rates of gas exchange and varying FiO2. Crit Care Med. 2000;28:3014–8.PubMedGoogle Scholar
  79. 79.
    Simonson DC, DeFronzo RA. Indirect calorimetry: methodological andinterpretative problems. Am J Physiol. 1990;258:E399–412.PubMedGoogle Scholar
  80. 80.
    de Klerk G, Hop WC, de Hoog M, Joosten KF. Serial measurements of energy expenditure in critically ill children: useful in optimizing nutritional therapy? Intensive Care Med. 2002;28:1781–5.PubMedGoogle Scholar
  81. 81.
    White MS, Shepherd RW, McEniery JA. Energy expenditure measurements inventilated critically ill children: within- and between-day variability. JPEN J Parenter Enteral Nutr. 1999;23:300–4.PubMedGoogle Scholar
  82. 82.
    Imura K, Okada A. Perioperative nutrition and metabolism in pediatric patients. World J Surg. 2000;24:1498–502.PubMedGoogle Scholar
  83. 83.
    Vázquez Martínez JL, Dorao Martínez-Romillo P, Diez Sebastian J, Ruza TF. Predicted versus measured energy expenditure by continuous, on-line indirect calorimetry in ventilated, critically ill children during the early post injury period. Pediatr Crit Care Med. 2004;5:19–27.PubMedGoogle Scholar
  84. 84.
    Knauth A, Baumgart S. Accurate, noninvasive quantitation of expiratory gas leak from uncuffed infant endotracheal tubes. Pediatr Pulmonol. 1990;9:55–60.PubMedGoogle Scholar
  85. 85.
    Branson RD. The measurement of energy expenditure: instrumentation, practical considerations, and clinical application. Respir Care. 1990;35:640–59.Google Scholar
  86. 86.
    Branson RD, Johannigman JA. The measurement of energyexpenditure. Nutr Clin Pract. 2004;19:622–36.PubMedGoogle Scholar
  87. 87.
    McLellan S, Walsh T, Burdess A, Lee A. Comparison between the Datex-Ohmeda M-COVX metabolic monitor and the Deltatrac II in mechanically ventilated patients. Intensive Care Med. 2002;28:870–8.PubMedGoogle Scholar
  88. 88.
    Singer P, Pogrebetsky I, Attal-Singer J, Cohen J. Comparison of metabolic monitors in critically ill, ventilated patients. Nutrition. 2006;22:1077–86.PubMedGoogle Scholar
  89. 89.
    Meyer R, Briassouli E, Briassoulis G, Habibi P. Evaluation of the M-COVX metabolic monitor in mechanically ventilated adult patients. e-SPEN. 2008;3(5):e232–9.Google Scholar
  90. 90.
    Briassoulis G, Briassoulis P, Michaeloudi E, Fitrolaki DM, Spanaki AM, Briassouli E. The effects of endotracheal suctioning on the accuracy of oxygen consumption and carbon dioxide production measurements and pulmonary mechanics calculated by a compact metabolic monitor. Anesth Analg. 2009;109(3):873–9.PubMedGoogle Scholar
  91. 91.
    Briassoulis G, Michaeloudi E, Fitrolaki DM, Spanaki AM, Briassouli E. Influence of different ventilator modes on Vo(2) and Vco(2) measurements using a compact metabolic monitor. Nutrition. 2009;25(11–12):1106–14.PubMedGoogle Scholar
  92. 92.
    van der Kuip M, de Meer K, Oosterveld MJ, Lafeber HN, Gemke RJ. Simple and accurate assessment of energy expenditure in ventilated paediatric intensive care patients. Clin Nutr. 2004;23:657–63.PubMedGoogle Scholar
  93. 93.
    Keshen TH, Miller RG, Jahoor F, Jaksic T. Stable isotopic quantitation of protein metabolism and energy expenditure in neonates on and post extracorporeal life support. J Pediatr Surg. 1997;32:958–63.PubMedGoogle Scholar
  94. 94.
    Sy J, Gourishankar A, Gordon WE, Griffin D, Zurakowski D, Roth RM, Coss-Bu J, Jefferson L, Heird W, Castillo L. Bicarbonate kinetics and predicted energy expenditure in critically ill children. Am J Clin Nutr. 2008;88(2):340–7.PubMedCentralPubMedGoogle Scholar
  95. 95.
    Duke Jr JH, Jorgensen SB, Broell JR, Long CL, Kinney JM. Contribution of protein to caloric expenditure following injury. Surgery. 1970;68:168–74.PubMedGoogle Scholar
  96. 96.
    Benotti P, Blackburn GL. Protein and caloric or macronutrient metabolic management of the critically ill patient. Crit Care Med. 1979;12:520–5.Google Scholar
  97. 97.
    Tilden SJ, Watkins S, Tong TK, Jeevanandam M. Measured energy expenditure in pediatric intensive care patients. Am J Dis Child. 1989;143:490–2.PubMedGoogle Scholar
  98. 98.
    Briassoulis G, Venkataraman S, Thompson AE. Energy expenditure in critically ill children. Crit Care Med. 2000;28:1166–72.PubMedGoogle Scholar
  99. 99.
    de Betue CT, van Waardenburg DA, Deutz NE, van Eijk HM, van Goudoever JB, Luiking YC, Zimmermann LJ, Joosten KF. Increased protein-energy intake promotes anabolism in critically ill infants with viral bronchiolitis: a double-blind randomised controlled trial. Arch Dis Child. 2011;96(9):817–22. Epub 2011 Jun 14.PubMedCentralPubMedGoogle Scholar
  100. 100.
    Vazquez Martinez JL, Martinez-Romillo PD, Diez Sebastian J, RuzaTarrio F. Predicted versus measured energy expenditure by continuous, online indirect calorimetry in ventilated, critically ill children during the early post injury period. Pediatr Crit Care Med. 2004;5:19–27.PubMedGoogle Scholar
  101. 101.
    World Health Organization. Energy and protein requirements. Report of a joint FAO/WHO/UNU expert consultation, WHO Technical Report Series No. 724. Geneva: World Health Organization; 1985.Google Scholar
  102. 102.
    Torun B, Davies PS, Livingstone MB, Paolisso M, Sackett R, Spurr GB. Energy requirements and dietary energy recommendations for children and adolescents. Eur J Clin Nutr. 1996;50:S37–81.PubMedGoogle Scholar
  103. 103.
    Henry CJK, Dyer S, Ghusein-Choueiri A. New equations to estimate basal metabolic rate in children aged 10–15 years. Eur J Clin Nutr. 1999;3:134–42.Google Scholar
  104. 104.
    Harris JA, Benedict FG. Biometric studies of basal metabolism in man. Washington, DC: Carnegie Institute, publication; 1919. p. 279.Google Scholar
  105. 105.
    Schofield WN. Predicting basal metabolic rate, new standards and review of previous work. Hum Nutr Clin Nutr. 1985;39c:5–42.Google Scholar
  106. 106.
    Rodriguez G, Moreno LA, Sarria A, Fleta J, Bueno M. Resting energy expenditure in children and adolescents: agreement between calorimetry and prediction equations. Clin Nutr. 2002;21:255–60.PubMedGoogle Scholar
  107. 107.
    Seashore JH. Nutritional support of children in the intensive care unit. Yale J Biol Med. 1984;57:111–34.PubMedCentralPubMedGoogle Scholar
  108. 108.
    Finan K, Larson DE, Goran MI. Cross-validation of prediction equations for resting energy expenditure in young, healthy children. J Am Diet Assoc. 1997;97:140–5.PubMedGoogle Scholar
  109. 109.
    Walker RN, Heuberger RA. Predictive equations for energy needs for the critically ill. Respir Care. 2009;54(4):509–21.PubMedGoogle Scholar
  110. 110.
    Pirat A, Tucker AM, Taylor KA, Jinnah R, Finch CG, Canada TD, Nates JL. Comparison of measured versus predicted energy requirements in critically ill cancer patients. Respir Care. 2009;54(4):487–94.PubMedGoogle Scholar
  111. 111.
    McArthur CD. Prediction equations to determine caloric requirements in critically ill patients. Respir Care. 2009;54(4):453–4.PubMedGoogle Scholar
  112. 112.
    Singer P, Berger MM, Van den Berghe G, Biolo G, Calder P, Forbes A, Griffiths R, Kreyman G, Leverve X, Pichard C, ESPEN. ESPEN guidelines on parenteral nutrition: intensive care. Clin Nutr. 2009;28(4):387–400.PubMedGoogle Scholar
  113. 113.
    Kreymann KG, Berger MM, Deutz NE, Hiesmayr M, Jolliet P, Kazandjiev G, Nitenberg G, van den Berghe G, Wernerman J, DGEM (German Society for Nutritional Medicine), Ebner C, Hartl W, Heymann C, Spies C, ESPEN (European Society for Parenteral and Enteral Nutrition). ESPEN guidelines on enteral nutrition: intensive care. Clin Nutr. 2006;25(2):210–23.PubMedGoogle Scholar
  114. 114.
    Petros S, Engelmann L. Validity of an abbreviated indirect calorimetry protocol for measurement of resting energy expenditure in mechanically ventilated and spontaneously breathing critically ill patients. Intensive Care Med. 2001;27(7):1164–8.PubMedGoogle Scholar
  115. 115.
    Cunningham KF, Aeberhardt LE, Wiggs BR, Phang PT. Appropriate interpretation of indirect calorimetry for determining energy expenditure of patients in intensive care units. Am J Surg. 1994;167(5):547–9.PubMedGoogle Scholar
  116. 116.
    van Lanschot JJ, Feenstra BW, Vermeij CG, Bruining HA. Calculation versus measurement of total energy expenditure. Crit Care Med. 1986;14(11):981–5.PubMedGoogle Scholar
  117. 117.
    Joosten KF. Why indirect calorimetry in critically ill patients: what do we want to measure? Intensive Care Med. 2001;27(7):1107–9.PubMedGoogle Scholar
  118. 118.
    Framson CM, LeLeiko NS, Dallal GE, Roubenoff R, Snelling LK, Dwyer JT. Energy expenditure in critically ill children. Pediatr Crit Care Med. 2007;8:264–7.PubMedGoogle Scholar
  119. 119.
    Verhoeven JJ, Hazelzet JA, van der Voort E, Joosten KF. Comparison of measured and predicted energy expenditure in mechanically ventilated children. Intensive Care Med. 1998;24:464–8.PubMedGoogle Scholar
  120. 120.
    Taylor RM, Cheeseman P, Preedy V, Baker AJ, Grimble G. Can energy expenditure be predicted in critically ill children? Pediatr Crit Care Med. 2003;4(2):176–80.PubMedGoogle Scholar
  121. 121.
    White MS, Shepherd RW, McEniery JA. Energy expenditure in 100 ventilated, critically ill children: improving the accuracy of predictive equations. Crit Care Med. 2000;28:2307–12.PubMedGoogle Scholar
  122. 122.
    Hardy CM, Dwyer J, Snelling LK, Dallal GE, Adelson JW. Pitfalls in predicting resting energy requirements in critically ill children: a comparison of predictive methods to indirect calorimetry. Nutr Clin Pract. 2002;17(3):182–9.PubMedGoogle Scholar
  123. 123.
    Sentongo TA, Tershakovec AM, Mascarenhas MR, Watson MH, Stallings VA. Resting energy expenditure and prediction equations in young children with failure to thrive. J Pediatr. 2000;136:345–50.PubMedGoogle Scholar
  124. 124.
    Mehta NM, Bechard LJ, Leavitt K, Duggan C. Cumulative energy imbalance in the pediatric intensive care unit: role of targeted indirect calorimetry. JPEN J Parenter Enteral Nutr. 2009;33(3):336–44.PubMedCentralPubMedGoogle Scholar
  125. 125.
    Brodie D, Moscrip C, Hutcheon R. Body composition measurement: a review of hydrodensitometry, anthropometry, and impedance methods. Nutrition. 1998;14:296–309.PubMedGoogle Scholar
  126. 126.
    Woodrow G. Body composition analysis techniques in adult and pediatric patients: how reliable are they? How useful are they clinically? Perit Dial Int. 2007;27 Suppl 2:245–9.Google Scholar
  127. 127.
    Monk DN, Plank LD, Franch-Arcas G, Finn PJ, Streat SJ, Hill GL. Sequential changes in the metabolic response in critically injured patients during the first 25 days after blunt trauma. Ann Surg. 1996;223:395–405.PubMedCentralPubMedGoogle Scholar
  128. 128.
    Wright CM, Sherriff A, Ward SC, McColl JH, Reilly JJ, Ness AR. Development of bioelectrical impedance-derived indices of fat and fat-free mass for assessment of nutritional status in childhood. Eur J Clin Nutr. 2008;62:210–7.PubMedGoogle Scholar
  129. 129.
    Chiolero RL, Gay LJ, Cotting J, Gurtner C, Schutz Y. Assessment of changes in body water by bioimpedance in acutely ill surgical patients. Intensive Care Med. 1992;18:322–6.PubMedGoogle Scholar
  130. 130.
    Park J, Yang WS, Kim SB, Park SK, Lee SK, Park JS, Chang JW. Usefulness of segmental bioimpedance ratio to determine dry body weight in new hemodialysis patients: a pilot study. Am J Nephrol. 2009;29:25–30.PubMedGoogle Scholar
  131. 131.
    Edefonti A, Picca M, Damiani B, Garavaglia R, Loi S, Ardissino G, Marra G, Ghio L. Prevalence of malnutrition assessed by biompedance analysis and anthropometrics in children on peritoneal dialysis. Perit Dial Int. 2001;21:172–9.PubMedGoogle Scholar
  132. 132.
    Matarese LE, Steiger E, Seidner DL, Richmond B. Body composition changes in cachectic patients receiving home parenteral nutrition. JPEN J Parenter Enteral Nutr. 2002;26:366–71.PubMedGoogle Scholar
  133. 133.
    Dufner D, Previs SF. Measuring in vivo metabolism using heavy water. Curr Opin Clin Nutr Metab Care. 2003;6:511–7.PubMedGoogle Scholar
  134. 134.
    McNaughton SA, Shepherd RW, Greer RG, Cleghorn GJ, Thomas BJ. Nutritional status of children with cystic fibrosis measured by total body potassium as a marker of body cell mass: lack of sensitivity of anthropometric measures. J Pediatr. 2000;136:188–94.PubMedGoogle Scholar
  135. 135.
    Sermet-Gaudelus I, Poisson-Salomon AS, Colomb V, Brusset MC, Mosser F, Berrier F, Ricour C. Simple pediatricnutritionalrisk score to identify children at risk of malnutrition. Am J Clin Nutr. 2000;72:64–70.PubMedGoogle Scholar
  136. 136.
    Zlotkin S. A critical assessment of the upper intake levels for infants and children. J Nutr. 2006;136(2):502S–6.PubMedGoogle Scholar
  137. 137.
    Murphy SP, Poos MI. Dietary reference intakes: summary of applications in dietary assessment. Public Health Nutr. 2002;5(6A):843–9.PubMedGoogle Scholar
  138. 138.
    Hulst JM, van Goudoever JB, Zimmermann LJ, Tibboel D, Joosten KF. The role of initial monitoring of routine biochemical nutritional markers in critically ill children. J Nutr Biochem. 2006;17:57–62.PubMedGoogle Scholar
  139. 139.
    Bistrian BR. Interaction of nutrition and infection in the hospital setting. Am J Clin Nutr. 1977;30:1228–35.PubMedGoogle Scholar
  140. 140.
    Samra T, Sharma S, Pawar M. Metabolic monitors as a diagnostic tool. J Clin Monit Comput. 2011;25(2):149–50.PubMedGoogle Scholar
  141. 141.
    McMahon MM. Management of parenteral nutrition in acutely ill patients with hyperglycemia. Nutr Clin Pract. 2004;19:120–8.PubMedGoogle Scholar
  142. 142.
    Dhar A, Castillo L. Insulin resistance in critical illness. Curr Opin Pediatr. 2011;23(3):269–74.PubMedGoogle Scholar
  143. 143.
    Verhoeven JJ, den Brinker M, Hokken-Koelega AC, Hazelzet JA, Joosten KF. Pathophysiological aspects of hyperglycemia in children with meningococcal sepsis and septic shock: a prospective, observational cohort study. Crit Care. 2011;15(1):R44. Epub 2011 Jan 31.PubMedCentralPubMedGoogle Scholar
  144. 144.
    Cheung NW, Napier B, Zaccaria C, Fletcher JP. Hyperglycemia is associated with adverse outcomes in patients receiving total parenteral nutrition. Diabetes Care. 2005;28:2367–71.PubMedGoogle Scholar
  145. 145.
    Ellger B, Debaveye Y, Vanhorebeek I, Langouche L, Giulietti A, Van Etten E, Herijgers P, Mathieu C, Van den Berghe G. Survival benefits of intensive insulin therapy in critical illness: impact of maintaining normoglycemia versus glycemia-independent actions of insulin. Diabetes. 2006;55(4):1096–105.PubMedGoogle Scholar
  146. 146.
    Scurlock C, Raikhelkar J, Mechanick JI. Critique of normoglycemia in intensive care evaluation: survival using glucose algorithm regulation (NICE-SUGAR)–a review of recent 2011 Nov;12(6):e386–90 literature. Curr Opin Clin Nutr Metab Care. 2010;13(2):211–4.PubMedGoogle Scholar
  147. 147.
    Ulate KP. A critical appraisal of Vlasselaers D, Milants I, Desmet L, Wouters PJ, Vanhorebeek I, van den Heuvel I, Mesotten D, Casaer MP, Meyfroidt G, Ingels C, Muller J, Van Cromphaut S, Schetz M, Van den Berghe G. Intensive insulin therapy for patients in paediatric intensive care: a prospective, randomised controlled study. Lancet. 2009;373:547–56.Google Scholar
  148. 148.
    Ognibene KL, Vawdrey DK, Biagas KV. The association of age, illness severity, and glycemic status in a pediatric intensive care unit. Pediatr Crit Care Med. 2011;12(6):e386–90.PubMedGoogle Scholar
  149. 149.
    Verbruggen SC, Coss-Bu J, Wu M, Schierbeek H, Joosten KF, Dhar A, van Goudoever JB, Castillo L. Current recommended parenteral protein intakes do not support protein synthesis in critically ill septic, insulin-resistant adolescents with tight glucose control. Crit Care Med. 2011;39(11):2518–25.PubMedGoogle Scholar
  150. 150.
    Abbott WC, Tayek JA, Bistrian BR, Maki T, Ainsley BM, Reid LA, Blackburn GL. The effect of nutritional support on T-lymphocyte subpopulations in protein calorie malnutrition. J Am Coll Nutr. 1986;5:577–84.PubMedGoogle Scholar
  151. 151.
    Twomey P, Ziegler D, Rombeau J. Utility of skin testing in nutritional assessment: a critical review. JPEN J Parenter Enteral Nutr. 1982;6:50–8.PubMedGoogle Scholar
  152. 152.
    Jeejeebhoy KN. Nutritional assessment. Nutrition. 2000;16:585–9.PubMedGoogle Scholar
  153. 153.
    Alberti KG, Batstone GF, Foster KJ, Johnston DG. Relative role of various hormones in mediating the metabolic response to injury. JPEN J Parenter Enteral Nutr. 1980;4:141–6.PubMedGoogle Scholar
  154. 154.
    Mastorakos G, Weber JS, Magiakou MA, Gunn H, Chrousos GP. Hypothalamic-pituitary-adrenal axis activation and stimulation of systemic vasopressin secretion by recombinant interleukin 6 in humans: potential implications for the syndrome of inappropriate vasopressin secretion. J Clin Endocrinol Metab. 1994;79:934–9.PubMedGoogle Scholar
  155. 155.
    Salas MA, Evans SW, Levell MJ, Whicher JT. Interleukin-6 and ACTH aft synergistically to stimulate the release of corticosterone from adrenal gland cells. Clin Exp Immunol. 1990;79:470–3.PubMedCentralPubMedGoogle Scholar
  156. 156.
    Tappy L, Girardet K, Schwaller N, Vollenweider L, Jéquier E, Nicod P, Scherrer U. Metabolic effects of an increase of sympathetic activity in healthy humans. Int J Obes Relat Metab Disord. 1995;19:419–22.PubMedGoogle Scholar
  157. 157.
    Smeets HJ, Kievit J, Harinck HI, Frolich M, Hermans J. Differential effects of counterregulatory stress hormones on serum albumin concentrations and protein catabolism in healthy volunteers. Nutrition. 1995;11:423–7.PubMedGoogle Scholar
  158. 158.
    Wernerman J, Botta D, Hammarqvist F, Thunell S, von der Decken A, Vinnars E. Stress hormones given to healthy volunteers alter the concentration and configuration of ribosomes in skeletal muscle, reflecting changes in protein synthesis. Clin Sci. 1989;77:611–6.PubMedGoogle Scholar
  159. 159.
    Garrel DR, Razi M, Larivière F, Jobin N, Naman N, Emptoz-Bonneton A, Pugeat MM. JPEN J Parenter Enteral Nutr. 1995;19:482–91.PubMedGoogle Scholar
  160. 160.
    Fulks RM, Li JB, Goldberg AL. Effects of insulin, glucose, and amino acids on protein turnover in rat diaphragm. J Biol Chem. 1975;250:290–8.PubMedGoogle Scholar
  161. 161.
    Mortimore GE, Mondon CE. Inhibition by insulin of valine turnover in liver. Evidence for a control of proteolysis. J Biol Chem. 1970;245:2375–83.PubMedGoogle Scholar
  162. 162.
    Herndon DN, Hart DW, Wolf SE, Chinkes DL, Wolfe RR. Reversal of catabolism by beta blockade after severe burns. N Engl J Med. 2001;345:1223–9.PubMedGoogle Scholar
  163. 163.
    Terao Y, Miura K, Saito M, Sekino M, Fukusaki M, Sumikawa K. Quantitative analysis of the relationship between sedation and resting energy expenditure in postoperative patients. Crit Care Med. 2003;31(3):830–3.PubMedGoogle Scholar
  164. 164.
    Vernon DD, Witte MK. Effect of neuromuscular blockade on oxygen consumption and energy expenditure in sedated, mechanically ventilated children. Crit Care Med. 2000;28(5):1569–71.PubMedGoogle Scholar
  165. 165.
    Kudsk KA, Chiolero RL. Current concepts in nutrition – editorial review. Curr Opin Clin Nutr Metab Care. 2005;8:167–70.PubMedGoogle Scholar
  166. 166.
    Arikan AA, Zappitelli M, Goldstein SL, Naipaul A, Jefferson LS, Loftis LL. Fluid overload is associated with impaired oxygenation and morbidity in critically ill children. Pediatr Crit Care Med. 2012;13(3):253–8.PubMedGoogle Scholar
  167. 167.
    Balogh Z, McKinley BA, Holcomb JB, Miller CC, Cocanour CS, Kozar RA, Valdivia A, Ware DN, Moore FA. Both primary and secondary abdominal compartment syndrome can be predicted early and are harbingers of multiple organ failure. J Trauma. 2003;54:848–59.PubMedGoogle Scholar
  168. 168.
    McClave SA, Martindale RG, Vanek VW, McCarthy M, Roberts P, Taylor B, Ochoa JB, Napolitano L, Cresci G, A.S.P.E.N. Board of Directors, American College of Critical Care Medicine, Society of Critical Care Medicine. Guidelines for the provision and assessment of nutrition support therapy in the adult critically ill patient: Society of Critical Care Medicine (SCCM) and American Society for Parenteral and Enteral Nutrition (A.S.P.E.N.). JPEN J Parenter Enteral Nutr. 2009;33(3):277–316.PubMedGoogle Scholar
  169. 169.
    Mehta NM, Compher C. ASPEN: clinical guidelines: nutrition support of the critically ill child. JPEN J Parenter Enteral Nutr. 2009;33:260–76.PubMedGoogle Scholar
  170. 170.
    Peterson S, Chen Y. Systemic approach to parenteral nutrition in the ICU. Curr Drug Saf. 2010;5(1):33–40.PubMedGoogle Scholar
  171. 171.
    Gramlich L, Kichian K, Pinilla J, Rodych NJ, Dhaliwal R, Heyland DK. Does enteral nutrition compared to parenteral nutrition result in better outcomes in critically ill adult patients? A systematic review of the literature. Nutrition. 2004;20(10):843–8.PubMedGoogle Scholar
  172. 172.
    Crook MA, Hally V, Panteli JV. The importance of the refeeding syndrome. Nutrition. 2001;17:632–7.PubMedGoogle Scholar
  173. 173.
    Byrnes MC, Stangenes J. Refeeding in the ICU: an adult and pediatric problem. Curr Opin Clin Nutr Metab Care. 2011;14(2):186–92.PubMedGoogle Scholar
  174. 174.
    Varsano S, Shapiro M, Taragan R, Bruderman I. Hypophosphatemia as a reversible cause of refractory ventilator failure. Crit Care Med. 1983;11:908–9.PubMedGoogle Scholar
  175. 175.
    Stanga Z, Brunner A, Leuenberger M, Grimble RF, Shenkin A, Allison SP, Lobo DN. Nutrition in clinical practice-the refeeding syndrome: illustrative cases and guidelines for prevention and treatment. Eur J Clin Nutr. 2008;62(6):687–94.PubMedGoogle Scholar
  176. 176.
    Wernerman J, Kirketeig T, Andersson B, Berthelson H, Ersson A, Friberg H, Guttormsen AB, Hendrikx S, Pettilä V, Rossi P, Sjöberg F, Winsö O, For the Scandinavian Critical Care Trials Group. Scandinavian glutamine trial: a pragmatic multi-centrerandomised clinical trial of intensive care unit patients. Acta Anaesthesiol Scand. 2011;55(7):812–8.PubMedGoogle Scholar
  177. 177.
    Andrews PJ, Avenell A, Noble DW, Campbell MK, Croal BL, Simpson WG, Vale LD, Battison CG, Jenkinson DJ, Cook JA, Scottish Intensive Care Glutamine or Selenium Evaluative Trial Trials Group. Randomised trial of glutamine, selenium, or both, to supplement parenteral nutrition for critically ill patients. BMJ. 2011;342:d1542.PubMedGoogle Scholar
  178. 178.
    Briassoulis G, Filippou O, Hatzi E, Papassotiriou I, Hatzis T. Early enteral administration of immunonutrition in critically ill children: results of a blinded randomized controlled clinical trial. Nutrition. 2005;21(7–8):799–807.PubMedGoogle Scholar
  179. 179.
    Briassoulis G, Filippou O, Kanariou M, Hatzis T. Comparative effects of early randomized immune or non-immune-enhancing enteral nutrition on cytokine production in children with septic shock. Intensive Care Med. 2005;31(6):851–8.PubMedGoogle Scholar
  180. 180.
    Briassoulis G, Filippou O, Kanariou M, Papassotiriou I, Hatzis T. Temporal nutritional and inflammatory changes in children with severe head injury fed a regular or an immune-enhancing diet: a randomized, controlled trial. Pediatr Crit Care Med. 2006;7(1):56–62.PubMedGoogle Scholar
  181. 181.
    Martindale RG, McClave SA, Vanek VW, McCarthy M, Roberts P, Taylor B, Ochoa JB, Napolitano L, Cresci G, American College of Critical Care Medicine, A.S.P.E.N. Board of Directors. Guidelines for the provision and assessment of nutrition support therapy in the adult critically ill patient: Society of Critical Care Medicine and American Society for Parenteral and Enteral Nutrition: Executive Summary. Crit Care Med. 2009;37:1757–61.PubMedGoogle Scholar
  182. 182.
    Hulst JM, van Goudoever JB, Zimmermann LJ, Hop WC, Albers MJ, Tibboel D, Joosten KF. The effect of cumulative energy and protein deficiency on anthropometric parameters in a pediatric ICU population. Clin Nutr. 2004;23:1381–9.PubMedGoogle Scholar
  183. 183.
    Sugita H, Kaneki M, Sugita M, Yasukawa T, Yasuhara S, Martyn JA. Burn injury impairs insulin-stimulated Akt/PKB activation in skeletal muscle. Am J Physiol Endocrinol Metab. 2005;288(3):E585–91.PubMedGoogle Scholar
  184. 184.
    Vanhorebeek I, Langouche L. Molecular mechanisms behind clinical benefits of intensive insulin therapy during critical illness: glucose versus insulin. Best Pract Res Clin Anaesthesiol. 2009;23(4):449–59.PubMedGoogle Scholar
  185. 185.
    Pollack MM, Ruttimann UE, Wiley JS. Nutritional depletions in critically ill children: associations with physiologic instability and increased quantity of care. JPEN J Parenter Enteral Nutr. 1985;9(3):309–13.PubMedGoogle Scholar
  186. 186.
    Mehta NM, McAleer D, Hamilton S, Naples E, Leavitt K, Mitchell P, Duggan C. Challenges to optimal enteral nutrition in a multidisciplinary pediatric intensive care unit. JPEN J Parenter Enteral Nutr. 2010;34(1):38–45.PubMedGoogle Scholar
  187. 187.
    Meyer R, Harrison S, Sargent S, Ramnarayan P, Habibi P, Labadarios D. The impact of enteral feeding protocols on nutritional support in critically ill children. J Hum Nutr Diet. 2009;22(5):428–36.PubMedGoogle Scholar
  188. 188.
    Heyland DK, Cahill NE, Dhaliwal R, Sun X, Day AG, McClave SA. Impact of enteral feeding protocols on enteral nutrition delivery: results of a multicenter observational study. JPEN J Parenter Enteral Nutr. 2010;34(6):675–84.PubMedGoogle Scholar
  189. 189.
    Poulard F, Dimet J, Martin-Lefevre L, Bontemps F, Fiancette M, Clementi E, Lebert C, Renard B, Reignier J. Impact of not measuring residual gastric volume in mechanically ventilated patients receiving early enteral feeding: a prospective before-after study. JPEN J Parenter Enteral Nutr. 2010;34(2):125–30.PubMedGoogle Scholar
  190. 190.
    Davies AR. Gastric residual volume in the ICU: can we do without measuring it? JPEN J Parenter Enteral Nutr. 2010;34(2):160–2.PubMedGoogle Scholar
  191. 191.
    Chwals WJ. Energy expenditure in critically ill infants. Pediatr Crit Care Med. 2008;9(1):121–2.PubMedGoogle Scholar
  192. 192.
    Briassoulis G, Briassouli E, Tavladaki T, Ilia S, Fitrolaki DM, Spanaki AM. Unpredictable combination of metabolic and feeding patterns in malnourished critically ill children: the malnutrition-energy assessment question. Intensive Care Med. 2014;40(1):120–2.Google Scholar
  193. 193.
    Berger MM, Que YA. Bioinformatics assistance of metabolic and nutrition management in the ICU. Curr Opin Clin Nutr Metab Care. 2011;14(2):202–8.PubMedGoogle Scholar
  194. 194.
    Chwals WJ. Overfeeding the critically ill child: fact or fantasy? New Horiz. 1994;2:147–55.PubMedGoogle Scholar
  195. 195.
    Klein CJ, Stanek GS, Wiles CE. Overfeeding macronutrients to critically ill adults: metabolic complications. J Am Diet Assoc. 1998;98:795–806.PubMedGoogle Scholar
  196. 196.
    McCowen KC, Bistrian BR. Hyperglycemia and nutrition support: theory and practice. Nutr Clin Pract. 2004;19:235–44.PubMedGoogle Scholar
  197. 197.
    Verbruggen S, Sy J, Arrivillaga A, Joosten K, van Goudoever J, Castillo L. Parenteral amino acid intakes in critically ill children: a matter of convenience. JPEN J Parenter Enteral Nutr. 2010;34(3):329–40.PubMedGoogle Scholar
  198. 198.
    McCall M, Jeejeebhoy K, Pencharz P, Moulton R. Effect of neuromuscular blockade on energy expenditure in patients with severe head injury. JPEN J Parenter Enteral Nutr. 2003;27:27–35.PubMedGoogle Scholar
  199. 199.
    Li J, Zhang G, Herridge J, Holtby H, Humpl T, Redington AN, Van Arsdell GS. Energy expenditure and caloric and protein intake in infants following the Norwood procedure. Pediatr Crit Care Med. 2008;9(1):55–61.PubMedGoogle Scholar
  200. 200.
    Jaksic T, Shew SB, Keshen TH, Dzakovic A, Jahoor F. Do critically ill surgical neonates have increased energy expenditure? J Pediatr Surg. 2001;36:63–7.PubMedGoogle Scholar
  201. 201.
    Finestone HM, Greene-Finestone LS, Foley NC, Woodbury MG. Measuring longitudinally the metabolic demands of stroke patients: resting energy expenditure is not elevated. Stroke. 2003;34:502–7.PubMedGoogle Scholar
  202. 202.
    Briassoulis G, Venkataraman S, Thompson A. Cytokines and metabolic patterns in pediatric patients with critical illness. Clin Dev Immunol. 2010;2010:354047. Epub 2010 May 16.PubMedCentralPubMedGoogle Scholar
  203. 203.
    Lancel S, Hassoun SM, Favory R, Decoster B, Motterlini R, Neviere R. Carbon monoxide rescues mice from lethal sepsis by supporting mitochondrial energetic metabolism and activating mitochondrial biogenesis. J Pharmacol Exp Ther. 2009;329(2):641–8.PubMedGoogle Scholar
  204. 204.
    Brealey D, Brand M, Hargreaves I, Heales S, Land J, Smolenski R, Davies NA, Cooper CE, Singer M. Association between mitochondrial dysfunction and severity and outcome of septic shock. Lancet. 2002;360(9328):219–23.PubMedGoogle Scholar
  205. 205.
    Svistunenko DA, Davies N, Brealey D, Singer M, Cooper CE. Mitochondrial dysfunction in patients with severe sepsis: an EPR interrogation of individual respiratory chain components. Biochim Biophys Acta. 2006;1757(4):262–72.PubMedGoogle Scholar

Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  1. 1.PICUUniversity Hospital, University of CreteHeraklion, CreteGreece

Personalised recommendations