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Information on the use of validated malnutrition risk screening tools in pediatric facilities to guide malnutrition identification, diagnosis, and treatment is scarce. Therefore, a survey of pediatric healthcare facilities and practitioners to ascertain malnutrition risk screening practices in North America was conducted. A pediatric nutrition screening practices survey was developed and sent to members of the American Society for Parenteral and Enteral Nutrition, the Council for Pediatric Nutrition Professionals and the Academy of Nutrition and Dietetics Pediatric Nutrition Practice Group. Respondents represented 113 pediatric hospitals in the United States and six in Canada, of which 94 were inpatient and 59 were outpatient. Nutrition risk screening was completed in 90% inpatient settings, and 63% used a validated screening tool. Nurses performed most malnutrition risk screens in the inpatient setting. Nutrition risk screening was reported in 51% of outpatient settings, with a validated screening tool being used in 53%. Measured anthropometrics were used in 78% of inpatient settings, whereas 45% used verbally reported anthropometrics. Measured anthropometrics were used in 97% outpatient settings. Nutrition risk screening was completed in the electronic health record in 80% inpatient settings and 81% outpatient settings. Electronic health record positive screen generated an automatic referral in 80% of inpatient and 45% of outpatient settings. In this sample of pediatric healthcare organizations, the results demonstrate variation in pediatric malnutrition risk screening in North America. These inconsistencies justify the need to standardize pediatric malnutrition risk screening using validated pediatric tools and allocate resources to perform screening., (© 2024 American Society for Parenteral and Enteral Nutrition.)

Background: In 2014, the Academy of Nutrition and Dietetics and the American Society for Parenteral and Enteral Nutrition recommended indicators to identify malnutrition in children aged 1 month to 18 years. The purpose of this study was to evaluate current practices and the effectiveness of the consensus statement-recommended pediatric malnutrition indicators., Methods: A cross-sectional electronic survey link was posted in the Pediatric Nutrition Practice Group (subscribers = 3160) and Clinical Nutrition Management Practice Group's Pediatric Subunit (subscribers = 177) community discussion boards. Additionally, flyers with the survey link were distributed to registered dietitian nutritionists (RDNs) currently working in pediatrics at the 2018 Food & Nutrition Conference & Expo. Frequencies described participant characteristics, awareness of indicators, and use of indicators. Researchers used the Fisher exact test to determine associations between categorical variables., Results: Two hundred eighty-one surveys were included in data analysis. A majority of RDNs (88.6%) reported they knew the indicators existed and considered themselves knowledgeable about the indicators. Weight loss was the most frequently used indicator, with 224 RDNs (80%) reporting use as often or always. Mid-upper arm circumference (MUAC) was the least used indicator with only 68 RDNs (25%) using it often or always. Most RDNs (71.5%) reported that their facility used International Classification of Disease, Tenth Revision codes to document malnutrition., Conclusion: RDNs working with pediatric populations consistently use the recommended malnutrition indicators; however, further education is needed on the appropriate use of MUAC and length/height per age as malnutrition indicators., (© 2020 American Society for Parenteral and Enteral Nutrition.)

Background: Body mass index (BMI) is used to determine nutrition status in children with cystic fibrosis (CF); however, lean body mass (LBM) is more strongly associated with pulmonary function. Handgrip strength (HGS) measures muscle function and is reflective of LBM. The aims of this study were to assess relationships among HGS, nutrition status, and pulmonary function; changes in HGS posthospitalization; and any relationship between HGS and nutrient intake., Methods: Twenty-three children with CF aged 6-18 years participated. BMI z scores, nutrition risk scores, and pulmonary function were assessed about 5 months before, days 5-7 of hospitalization, and about 6 weeks posthospitalization. HGS z scores and arm anthropometrics were measured during and after hospitalization. Nutrient intakes were assessed during hospitalization., Results: Mean HGS z score at hospitalization was -1.95 ± 0.92 and posthospitalization was -1.59 ± 1.06 (P = .007). Mean BMI z score prehospitalization was -0.17 ± 0.63, at hospitalization was -0.09 ± 0.64, and posthospitalization was 0.06 ± 0.54 (P = .065). Mean forced expiratory volume in 1 second (FEV 1 ) prehospitalization was 93.52 ± 17.35, at hospitalization was 85.65 ± 21.57, and posthospitalization was 95.63 ± 18.18 (P = .001). No significant relationship was found between HGS z scores and BMI z scores (P = .892) or HGS z scores and FEV 1 (P = .340)., Conclusions: HGS z scores and FEV 1 significantly increased at follow-up. HGS z scores were lower than the standard even though mean BMI z scores classified participants as normal nutrition status., (© 2018 American Society for Parenteral and Enteral Nutrition.)

Background: Diagnosing undernutrition in hospitalized pediatric populations is crucial to provide timely nutrition interventions. Handgrip strength (HGS), a measurement of muscle function, is a reliable indicator of undernutrition. However, limited research exists on HGS in hospitalized pediatric patients. The primary aim of this study was to determine if HGS differed between hospitalized children within 48 hours of admission and nonhospitalized children. A secondary purpose was to describe the association of HGS with height, weight, body mass index (BMI), mid upper arm circumference (MUAC), activity level, disease severity, nutrition risk, and nutrition intervention., Methods: One hundred nine hospitalized and 110 nonhospitalized patients aged 6-14 years participated in this cross-sectional nonequivalent control group design study. Weight, height, MUAC, and HGS were measured within 48 hours of hospital admission for the hospitalized group or immediately following a well-child visit for the control group., Results: Based on analysis of covariance, the HGS was estimated to be 12.4 ± 0.37 kgF (mean ± SE) for hospitalized subjects and 13.1 ± 0.37 for nonhospitalized subjects ( P = .2053). HGS was associated with age ( P < .0001), height ( P < .0001), dominant hand ( P < .0001), and MUAC z scores ( P = .0462)., Conclusion: HGS was not significantly different between hospitalized and nonhospitalized participants, although anthropometric measurements were similar between groups. A strong relationship was demonstrated between HGS and BMI and MUAC z scores. Further research is needed that examines serial HGS measurements, feasibility in hospitalized patients, and the association of HGS measurements and nutrition risk.

Nutrition screening tools may facilitate early nutrition interventions specifically with Hispanic populations that are already at higher risk. There is a paucity of culturally competent and validated nutrition screening tools for 3–5-year-old Hispanic children. The purpose of this study was to develop a Spanish nutrition screening tool for 3–5-year-old children to be used by Spanish-speaking parents in community settings to appropriately address malnutrition risk factors with cultural sensitivity. All phases of the study were conducted in Spanish with native Spanish speakers. Face and content validity were established using focus groups, expert reviews, and pilot testing of the tool with Spanish-speaking parents. Parents of children 3–5 years old whose primary language was Spanish (n = 39) completed the nutrition screening, and a nutritionist completed an in-depth nutrition assessment of these children. Criterion validity was measured by comparing the results of the nutrition screening tool with the in-depth nutrition assessment. The nutrition screening tool had a sensitivity of 91.67% and a specificity of 81.48%. The negative predictive value was 69%, and the positive predictive value was 96%. The nutrition screening tool may be used to identify malnutrition in Hispanic children and needs further validation in larger samples. [ABSTRACT FROM AUTHOR]

Nutrition screening tools are used to identify risk of malnutrition or change in risk of malnutrition. However, it is unclear which tools have demonstrated high validity, reliability, and agreement. Our aim was to conduct a systematic review of valid and reliable pediatric nutrition screening tools for identifying malnutrition risk (under- or overnutrition), and to determine whether there are differences in validity and reliability according to users of the tools. A literature search using Medline, Embase, and CINAHL databases was conducted to identify relevant research published between 1995 and May 2017 examining validity and reliability of nutrition screening tools in the pediatric population. A multidisciplinary workgroup developed eligibility criteria, data were extracted and summarized, risk of bias was assessed, and evidence strength was graded, according to a standard process. Twenty-nine studies met inclusion criteria. Thirteen pediatric nutrition screening tools designed for various settings were included in the review (seven inpatient/hospital, three outpatient or specialty setting, and three community). The most frequently examined tools were the Screening Tool for the Assessment of Malnutrition in Pediatrics, Screening Tool for Risk on Nutritional Status and Growth (13 studies each), and Paediatric Yorkhill Malnutrition Score (nine studies). No tools demonstrated high validity. Reliability and agreement were reported infrequently. Nutrition screening tools with good/strong or fair evidence and moderate validity included the Screening Tool for the Assessment of Malnutrition in Pediatrics, Screening Tool for Risk on Nutritional Status and Growth, and Paediatric Yorkhill Malnutrition Score in the inpatient setting and Nutrition Risk Screening Tool for Children and Adolescents with Cystic Fibrosis in the specialty setting. No tools in the community setting met these criteria. While differences in validity and reliability measures among tool users were found, the significance of these findings is unclear. Limitations included few studies examining each tool, heterogeneity between studies examining a common tool, and lack of tools that included currently recommended indicators to identify pediatric malnutrition. [ABSTRACT FROM AUTHOR]

Both food insecurity and malnutrition are associated with adverse health outcomes in the pediatric population. However, the research on the relationship between these factors has been inconsistent, leading to uncertainty regarding whether or how evaluation of food insecurity should be incorporated into nutrition screening or the nutrition care process. The objective of this systematic review was to determine the association between food insecurity and malnutrition related to undernutrition or overnutrition (defined by anthropometrics) in the pediatric population in the United States. A literature search was conducted using Medline, Embase, and CINAHL databases for studies published from January 2002 through November 2017. A total of 23 studies (19 cross-sectional and 4 prospective cohort studies) met inclusion criteria and were included in qualitative analysis. In 6 studies, there was no overall relationship between food insecurity and underweight. All included studies examined the relationship between food insecurity status and overweight/obesity and results were mixed, with large cross-sectional studies demonstrating a positive relationship between food insecurity and overweight/obesity. There were no clear patterns according to subpopulation. Evidence quality was graded as fair due to heterogeneity in how food insecurity was measured and populations included as well as inconsistency in results. Use of a 2-item food insecurity screening tool may allow for efficient, effective screening of food insecurity in order to identify potential contributors overweight and obesity. [ABSTRACT FROM AUTHOR]