The European Society for Clinical Nutrition and Metabolic Care (ESPEN) (www.ESPEN.org) defines malnutrition as ‘a state of nutrition in which a deficiency or excess (or imbalance) of energy, protein, and other nutrients causes measurable adverse effects on tissue/body form (body shape, size and composition) and function, and clinical outcome’ . This definition is aimed at emphasizing that malnutrition is a disease with adverse consequences on body composition and function, and not just a change of body shape or appearance.
To prevent malnutrition and, especially, hospital-acquired malnutrition, the risk of nutritional depletion needs to be identified as soon as possible, best at admission, so that appropriate nutritional intervention can be initiated at an early stage. Routine nutritional screening is rarely carried out in pediatric patients because of the lack of a simple and properly validated nutritional screening tool.
The current practice of identifying children at risk of malnutrition is heavily reliant on the interpretation of anthropometric data and clinical judgment, the reliability of which is dependent on pediatric nutrition knowledge, usually of a pediatrician or registered pediatric dietitian. Severe cases of malnutrition are relatively easily recognized; however, the identification of children with mild or moderate malnutrition or at risk of malnutrition, which is also very important, is not as easily achieved.
MALNUTRITION IN HOSPITALIZED PEDIATRIC PATIENTS
The reported prevalence of acute malnutrition in infants and children admitted to hospitals from different countries ranges from 6.1 to 40.9% [2–12]. In children with an underlying disease, higher prevalence of chronic malnutrition (44–64%) was reported in several studies [13▪,14–18], including a recent study demonstrating a prevalence of 90% in children with congenital heart defect .
The reasons for such differences within the reported rates of malnutrition in hospitalized children are multiple: heterogeneity of assessors and data collection; the inconsistency of definitions used to classify nutritional status; and the diversity of the study population, type of institution and country of recruitment.
Undernutrition in childhood has been associated with poor growth, reduced educational and social achievements and possible implications for adult health and performance [19,20▪,21]. Malnutrition in hospitalized children is a highly relevant pathologic condition and a risk factor for unfavorable outcome, prolonged hospital stay, delayed recovery and increased care costs [22–25].
CLINICAL JUDGMENT, ANTHROPOMETRY OR NUTRITION HISTORY?
The value of clinical judgment alone for identifying nutrition risk is debatable and has been found uniformly poor in the absence of anthropometric measurements [26,27]. Anthropometric measurements, such as weight and height, and the interpretation of these, are an objective and quantitative element of nutritional assessment. Traditionally, acute undernutrition in children has been defined as low weight-for-age or low weight-for-height (wasting), and chronic undernutrition has been classified on the basis of low height-for-age (stunting) as described by Waterlow . Indices derived from percentage weight-for-height have been developed, but these require more calculations and a certain degree of competence in dealing with growth charts. The accuracy of these calculations, even when undertaken by experienced professionals, has been questioned. Several studies and reviews have shown that the classification of nutritional status in children is highly dependent on the criteria and cut-off values used to categorize undernutrition [12,29,30].
Anthropometric assessment using weight and height is generally considered to be a basic requirement of the admission process. However, in clinical practice, many limitations exist . A lack of functioning, calibrated and fit-for-purpose equipment is common [32,33]. When equipment is available, the technique used to obtain measurements is not always standardized and the recording of measurements is often poor, if done at all [34▪▪]. The information that can be derived from single measurements is limited because growth rates differ between children and with the developmental stage. In view of these difficulties, use of anthropometric indices or one of the classification methods to define nutritional status and the risk of malnutrition in hospitalized children is currently less than satisfactory.
The assessment of energy intake is considered as a key part of the nutritional assessment. Indeed, reduction of dietary intake, together with the increase of energy requirements, is the main cause of hospital undernutrition and can contribute to its worsening. The subjective assessment of dietary intake by the patient himself/herself is included in several nutritional indices in adults, such as the Subjective Global Assessment (SGA), the Mini Nutritional Assessment (MNA) or the Nutritional Risk Score (NRS) . A poor nutrient intake was associated with a higher rate of infections, poor wound healing, more frequent cardiac complications and even increased mortality [36,37]. From a clinical point of view, the availability of methods allowing a quick assessment of daily energy intake would be of utmost interest also in children.
The ‘NutritionDay’ project is an ESPEN supported 1-day, cross-sectional audit of nutritional status and food intake primarily in hospitalized adults followed by an outcome evaluation 30 days later, which is performed yearly across many European hospitals. During ‘NutritionDay’ 2006, a history was obtained from 14 665 (90%) participants, and individual information about actual food intake was obtained from 14 474 (89%) patients. Individual food intakes on NutritionDay revealed that less than half of all patients finished their meals. In this single-day audit of food intake, even when taking into account other variables, a progressive increase of 30-day mortality was associated with decreased food intake .
Insufficient nutritional intake in hospital was addressed in 2003 by a resolution from the European Council; and in 2006 by guidelines from UK's National Institute for Health and Clinical Excellence (NICE): however, it is unknown by now whether these initiatives will have impact on nutrition care in European hospitals [38,39].
PEDIATRIC NUTRITION SCREENING TOOLS
National and international health organizations have recommended that all adults should have their nutritional status assessed and screened for nutrition risk at any encounter with health services [40,41]. For this purpose, nutrition risk screening tools have been designed for the early identification of malnutrition or undernutrition by staff who are not expert in nutrition . These screening tools have been validated in a variety of clinical settings and with different patient groups. However, none of these adult tools are validated for use in children. The reasons are multiple but mainly the difficulty to assess improper growth, the pediatric equivalent to adult weight loss based on one weight or height measurement. In addition, the clinical implications of diseases are different for children, the underlying cause and pathology differ in some instances, and the impact of disease on growth and subsequent development is an additional important complicating factor.
In order to improve nutritional care in pediatric hospitals, the European Society for Paediatric Gastroenterology, Hepatology and Nutrition (www.ESPGHAN.org) Committee on Nutrition has recommended the establishment of nutrition support teams whose tasks should include among others ‘identification of patients at risk of malnutrition, provision of adequate nutritional management, education and training of hospital staff and audit of practice’. However, these recommendations have not been widely introduced into routine clinical practice . During the last few years, impressive efforts have been made to create simple and useful nutrition screening tools for children. The scope of this review is to describe these tools and the recent ESPEN research project aimed to link anthropometric measurements to outcome (e.g. length of hospital stay), to establish broadly agreed, evidence-based criteria for malnutrition in children and to put forward an evidence-based screening tool for pediatric malnutrition and malnutrition risk.
At least five malnutrition screening tools have been developed in the last decade to address the risk of malnutrition in hospitalized children (Table 1). These tools have been tested by their authors in the original published studies, without having been properly validated in larger cohorts or by other authors. Furthermore, there is no documentation of the impact of screening tools implementation with respect to overall benefit and cost, an essential prerequisite for inclusion of these tools in routine pediatric care.
Sermet-Gaudelus et al.  developed and tested a screening tool based on prospective nutritional assessment and a weight loss greater than 2% from admission weight as the cut-off for nutrition risk. Nutritional risk was assessed prospectively in 296 children by evaluating various factors within 48 h of admission. Multivariate analysis indicated that food intake less than 50%, pain, and grade 2 and 3 pathologic conditions (P = 0.0001 for all) were associated with weight losses of greater than 2%. The Pediatric Nutritional Risk Score (PNRS) ranged from 0 to 5 and was calculated by adding the values for the significant risk factors as follows: 1 for food intake less than 50%, 1 for pain, 1 for grade 2 pathologic condition and 3 for grade 3 pathologic condition. A score of 1 or 2 is supposed to indicate moderate risk and a score of at least 3 to indicate high risk of malnutrition. Although this tool appears to be quick and simple to use, the study does not detail on the conditions required for implementation (e.g. staff training and resources) or the reliability and the reproducibility of the tool in practice.
Secker and Jeejeebhoy  developed and tested a Subjective Global Nutritional Assessment (SGNA) score for children. The SGNA consisted of a nutrition-oriented physical examination and information on the child's recent and current height and weight, parental heights, dietary intake, frequency and duration of gastrointestinal symptoms, current functional capacity and recent changes. The SGNA was tested on a population of children undergoing surgery, and the occurrence of nutrition-associated complications was documented at 30 days after surgery. SGNA divided children into three groups: well nourished, moderately malnourished and severely malnourished. The children categorized as malnourished had a higher rate of infectious complications and a longer postoperative length of stay than the well nourished children. Although this is so far the only pediatric tool that correlated nutritional status categories with outcome, one of the limitations of SGNA use in clinical practice may be the time required to complete it. Although referred to as a screening tool, the name acknowledges that the SGNA is more a structured nutritional assessment. The authors did not report the time taken to complete the SGNA or the level of training and expertise in nutrition assessment of the assessors. These are critical considerations that require clarification.
STAMP – Screening Tool for the Assessment of Malnutrition in Pediatrics – is a 5-step tool that was tested in comparison to a full nutritional assessment in a group of 89 children aged 2–17 years admitted for surgery . STAMP consists of three elements: clinical diagnosis (classified by the possible nutritional implications), nutritional intake and anthropometric measurements (weight). Each element is scored and nutritional risk is translated into the need for a referral for full assessment. No outcomes were evaluated with the STAMP tool.
The Paediatric Yorkhill Malnutrition Score (PYMS) assesses four steps considered as predictors or symptoms of malnutrition: BMI, history of recent weight loss, changes in nutritional intake and the predicted effect of the current medical condition on the nutritional status of the patient . Each step bears a score of up to 2, and the total score reflects the degree of the nutrition risk of the patient. Of the 247 children studied, the nurse-rated PYMS identified 59% of those rated at high risk by full dietetic assessment. Of those rated at high risk by the nursing PYMS, 47% were confirmed as high risk on full assessment. These results can be interpreted that at least half of children were inadequately referred to dietitians for evaluation, and almost 40% were missed by the nurse-administered PYMS, but most of these children would not have been identified at all without PYMS. As has been shown by earlier studies, health staff are poor at recognizing undernutrition [26,27]. The fact that use of PYMS by a dietitian identified more true cases also suggests that the diagnostic accuracy of the PYMS might possibly be improved by further training and continuous use. The authors also performed a comparison of screening tools with research dietitians’ assessment. SGNA had the highest specificity and positive predictive value, but very low sensitivity, which might not be surprising considering that the SGNA is rather an assessment method than a screening tool. The PYMS identified all the children who screened at high risk by the SGNA, but only 52% of those screened at high risk by the STAMP. Likewise, the STAMP and the PYMS completed by the research dietitians both achieved high specificity and sensitivity, but the positive predictive value was higher for the PYMS which also showed higher agreement with the research dietetic assessment.
STRONGkids – Screening Tool Risk on Nutritional status and Growth – has been developed and tested in a multicenter study that included 424 children aged 3.5 years (range 31 days to 17.7 years) admitted to seven academic and 37 general hospitals in the Netherlands . The STRONGkids screening tool consists of four elements: subjective clinical assessment, high-risk disease, nutritional intake and weight loss or poor weight gain. Measurements of weight and length were also performed. SD scores of 2 or less for weight-for-height and height-for-age were considered to indicate acute and chronic malnutrition, respectively. The study data show a significant relationship between high-risk score in STRONGkids and weight for height z-score. In addition, the length of hospital stay was significantly different between the lowest and highest risk groups. The authors claim great simplicity with the use of their screening tool; however, the STRONGkids has two weak points: the subjective clinical assessment item ‘was carried out by skilled pediatricians’, whereas one would ideally wish for a screening tool that can be applied by all healthcare workers; the 4th item ‘weight loss or poor weight gain’ or anthropometric indices calculation require either previous knowledge of the child weight/length (rarely available beside infancy) or time-consuming assessment and interpretation of these indexes.
None of the tools described above were validated in larger study populations beyond the first publication setting. Most of these screening tools have not been correlated with clinical outcome or have weaknesses that may be a barrier for using them as universal screening tools. Assessment of the consequences of implementing any of these tools in pediatric clinical routine practice, including potential benefit and burdens for the patients and their families as well as health and economic consequences, is not available.
A research project to address some of the open questions is currently being performed with support by a Network Grant of ESPEN and in collaboration with the Working Group on Malnutrition of the ESPGHAN. This mutlticenter study coordinated by Professor Berthold Koletzko, Munich, Germany, is performed in 14 pediatric departments in 12 European countries. Demographic and medical data were collected in over 2400 pediatric inpatients. Anthropometric measurements and interviews were performed during the first 24 h after admission, and outcome data were collected after discharge. The initial interview included the questions of three previously proposed screening tools: STAMP, PYMS and STRONGkids. The results of this project will help to establish the criteria to link anthropometric measurements with outcomes such as length of hospital stay. Hopefully, it will lead to agreed, evidence-based criteria for malnutrition in children and provide further information on possible selection of an appropriate screening tool for children.
Proper assessment of nutritional status should be a standard requirement of child care aimed to identify those patients who can benefit from and need targeted intervention. Nutritional assessment should provide reliable information on the child's nutritional status, a risk assessment of future development of underweight or overweight, and the basis for decisions on further diagnostic steps, monitoring and therapeutic interventions. The purpose of nutritional screening, that should precede detailed assessment, is to identify children at risk for inadequate nutritional intakes or occurrence of undernutrition, and thus to select children who should receive a more detailed nutritional assessment. Any pediatric nutrition screening tool for broad use should be rapid and easy to use by admitting staff or community healthcare teams without the need of involving qualified nutrition experts. The ideal screening tool should consist of a few easily obtainable data points, which might include both objective (anthropometry) and subjective (disease state/food intake/nutrition history) data. Any tool designed to become part of the routine pediatric assessment should be well reproducible, have good sensitivity and specificity with regard to significant health outcomes, support the cause of child nutrition on a larger scale and be cost-effective. We hope that additional information on the potential strengths and limitations of existing tools will become available in the near future, which may contribute to delineation of broadly agreed standards for useful nutrition screening in pediatrics.
This work was financially supported in part by a Network Grant of the European Society of Clinical Nutrition and Metabolic Care ( www.espen.org ). Additional financial support by the Munich Centre of Health Sciences (McHealth) and the Child Health Foundation ( http://www.kindergesundheit.de ) is gratefully acknowledged. B.K. is the recipient of the Freedom to Discover Award of the Bristol Myers Squibb Foundation, New York, NY, USA. The authors are most grateful indeed for the active contributions of the further partners of the multicentric ESPEN Network Grant study, Carlo Agostoni, Milano; Carmen Culcitchi, Cluj-Napoca; Diana Flynn, Glasgow; Frédéric Gottrand, Lille; Koen Joosten and Jessie Hulst, Rotterdam; Harma Koetse, Groningen; Sanja Kolacek, Zagreb; Janusz Ksiazyk, Warsaw; Peter Sullivan, Oxford; and Hania Szajewska, Warsaw.
Conflicts of interest
There are no conflicts of interest.
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