Summary of Science: Diagnosing Pediatric Malnutrition for the Practicing Clinician

Pediatric malnutrition, specifically undernutrition, is defined as "an imbalance between nutrient requirement and intake, resulting in cumulative deficits of energy, protein or micronutrients that may negatively affect growth, development and other relevant outcomes." ^{[1] [1]} Mehta NM, Corkins MR, Lyman B, et al. Defining pediatric malnutrition: a paradigm shift toward etiology-related definitions. JPEN J Parenter Enteral Nutr. Jul 2013;37(4):460-81. doi:10.1177/0148607113479972 While there is a definition, a challenge practitioners encounter is to determine what tool to use to identify if malnutrition is present.

Pediatric malnutrition has been associated with adverse outcomes, such as developmental and growth delay, increased infection risk, increased length of stay, increased readmission rates, increased morbidity and mortality, and overall increased healthcare costs. ^{[1] [1]} Mehta NM, Corkins MR, Lyman B, et al. Defining pediatric malnutrition: a paradigm shift toward etiology-related definitions. JPEN J Parenter Enteral Nutr. Jul 2013;37(4):460-81. doi:10.1177/0148607113479972^{, [2] [2]} Suryawan A, Jalaludin MY, Poh BK, et al. Malnutrition in early life and its neurodevelopmental and cognitive consequences: a scoping review. Nutrition Research Reviews. 2022;35(1):136-149. doi:10.1017/S0954422421000159 Without a standardized method, the exact prevalence of malnutrition in hospitalized pediatric patients is unknown.

The ideal process to identify malnutrition is two-fold: nutrition screening followed by nutrition assessment.

Nutrition screening is a simple, low resource process used to identify individuals at risk of malnutrition who many benefit from nutrition assessment. ^{[3] [3]} Serón-Arbeloa C, Labarta-Monzón L, Puzo-Foncillas J, et al. Malnutrition Screening and Assessment. Nutrients. 2022;14(12):2392.

Nutrition assessment is an approach to collect and summarize appropriate data to identify nutrition related problems and their causes and may lead to nutrition diagnosis such malnutrition. ^{[4] [4]} Academy of Nutrition and Dietetics. NCP Step 1: Nutrition Assessment. Academy of Nutrition and Dietetics. Accessed October 7, 2025, 2025. https://www.ncpro.org/pubs/2023-encpt-en/category-1

Use of valid, population-appropriate nutrition screening and assessment tools is essential to ensure accurate diagnosis of malnutrition and other nutrition-related problems, optimize patient outcomes, and support efficient use of health care resources. ^{[5] [5]} Field LB, Hand RK. Differentiating malnutrition screening and assessment: a nutrition care process perspective. J Acad Nutr Diet. May 2015;115(5):824-828. doi:10.1016/j.jand.2014.11.010 In contrast, failure to use a validated tool can lead to inaccurate or missed diagnoses, inappropriate interventions, wasted clinician time and health care dollars, and ultimately, poorer patient outcomes. ^{[5] [5]} Field LB, Hand RK. Differentiating malnutrition screening and assessment: a nutrition care process perspective. J Acad Nutr Diet. May 2015;115(5):824-828. doi:10.1016/j.jand.2014.11.010

Nutrition Screening

Single Anthropometric Measurements

MUAC (Mid-Upper Arm Circumference) and WHZ (Weight-for-Length Z-Score) are commonly used metrics to screen children for acute malnutrition around the world, particularly in low to middle income countries but is less commonly used in developed nations. ^{[6] [6]} World Health Organization. Updates on the Management of Severe Acute Malnutrition in Infants and Children. Accessed October 7, 2025, https://iris.who.int/server/api/core/bitstreams/0c5e1662-a300-4827-90fa-962ac21360dd/content^{, [7] [7]} unicef. Assessing the use of acute malnutrition indicators for nutrition surveillance. Publications Office of the European Union. Accessed October 8, 2025, https://publications.jrc.ec.europa.eu/repository/handle/JRC126760^{, [8] [8]} Becker P, Abdel-Rahman S, Nemet D, et al. Measurement of mid-upper arm circumference to screen for childhood malnutrition: General applicability and use in special populations. Nutr Clin Pract. Dec 2024;39(6):1517-1528. doi:10.1002/ncp.11208 Their ability to consistently identify children at risk for malnutrition is debated.

A study conducted by Durukan et al in 2024 evaluated the reliability of mid-upper arm circumference (MUAC) z-scores in identifying malnutrition in 906 healthy children and adolescents aged 2 months to 18 years. ^{[9] [9]} Durukan HE, Dörtkardeşler BE, Tosyalı M, Gökçe Ş, Kurugöl NZ, Koç F. Assessment of the Diagnostic Performance of MUAC in Malnutrition Screening and Its Correlation with Other Anthropometric Indicators in Healthy Children and Adolescents. Children (Basel). Dec 18 2024;11(12)doi:10.3390/children11121535 MUAC z-scores showed fair agreement with BMI z-scores for malnutrition in children over two years, as well as moderate agreement with weight-for-height z-scores. Authors concluded that while MUAC z-score correlated moderately with other anthropometric measures, further large-scale studies are needed to validate its screening and diagnostic power alongside other anthropometric measures in malnutrition diagnosis.

Menber et al conducted a systematic review in 2025 comparing MUAC against WHZ in children in Africa. ^{[10] [10]} Menber Y, Belachew T, Fentahun N. Diagnostic accuracy of MUAC for assessment of acute malnutrition among children aged 6-59 months in Africa: systematic review and meta-analysis. Front Nutr. 2025;12:1536386. doi:10.3389/fnut.2025.1536386 MUAC correctly identified only 38.1% of children who were malnourished according to WHZ (sensitivity; 95% CI: 30.7–46.1%), but it accurately recognized 94.9% of children who were not malnourished (specificity; 95% CI: 93.2–96.2%). Overall, the tool showed good ability to distinguish malnourished from well-nourished children (AUC = 0.85), yet it still misclassified 11.7% of cases.

Nutrition Screening Tools

In 2018, the Academy's Evidence Analysis Center published a systematic review in the Journal of the Academy of Nutrition and Dietetics examining validated screening tools for the pediatric population. ^{[11] [11]} Becker PJ, Gunnell Bellini S, Wong Vega M, et al. Validity and Reliability of Pediatric Nutrition Screening Tools for Hospital, Outpatient, and Community Settings: A 2018 Evidence Analysis Center Systematic Review. J Acad Nutr Diet. Feb 2020;120(2):288-318.e2. doi:10.1016/j.jand.2019.06.257 The screening tools for inpatient setting with the highest validity, Grade I or II, were:

• Screening Tool for the Assessment of Malnutrition in Pediatrics (STAMP) ^{[12] [12]} McCarthy H, Dixon M, Crabtree I, Eaton-Evans MJ, McNulty H. The development and evaluation of the Screening Tool for the Assessment of Malnutrition in Paediatrics (STAMP©) for use by healthcare staff. J Hum Nutr Diet. Aug 2012;25(4):311-8. doi:10.1111/j.1365-277X.2012.01234.x
• Screening Tool for Risk on Nutritional status and Growth (STRONGkids) ^{[13] [13]} Hulst JM, Zwart H, Hop WC, Joosten KFM. Dutch national survey to test the STRONG_kids nutritional risk screening tool in hospitalized children. Clinical Nutrition. 2010;29(1):106-111. doi:10.1016/j.clnu.2009.07.006
• Paediatric Yorkhill Malnutrition Score (PYMS). ^{[14] [14]} Lestari NE, Nurhaeni N, Wanda D. The Pediatric Yorkhill Malnutrition Score Is a Reliable Malnutrition Screening Tool. Compr Child Adolesc Nurs. 2017;40(sup1):62-68. doi:10.1080/24694193.2017.1386972

More recently, Fachal et al. published a systematic review examining the validity and reliability of nutrition screening tools. ^{[15] [15]} Fachal CV, Fernandez-Gonzalez SM, Moreno-Alvarez A, Solar-Boga A. Nutritional Screening Tools in the Pediatric Population: A Systematic Review. Nutrients. Jan 24 2025;17(3)doi:10.3390/nu17030433 The authors emphasize that screening tools should be "simple and able to identify individuals at higher risk so that they can be referred early for further evaluation." A key challenge lies in balancing sensitivity and specificity, as improving one often reduces the other.

For instance, both STAMP and STRONGkids demonstrate very high sensitivity (100%) due to their comprehensive nature, but their specificity is lower (40% and 48%, respectively).

Notably, STAMP is more extensive and typically requires about 15 minutes to complete, making it more akin to a nutrition assessment tool than a nutrition screening tool.

In contrast, STRONGkids can be completed in approximately five minutes, making it a more practical choice for initial screening in clinical settings.

Unfortunately, the use of screening tools varies widely between facilities, making the prevalence of malnutrition risk in hospitals difficult to determine. In 2024, Becker et al published the results of a survey characterizing the patterns of use for nutrition screening in pediatric populations. ^{[11] [11]} Becker PJ, Gunnell Bellini S, Wong Vega M, et al. Validity and Reliability of Pediatric Nutrition Screening Tools for Hospital, Outpatient, and Community Settings: A 2018 Evidence Analysis Center Systematic Review. J Acad Nutr Diet. Feb 2020;120(2):288-318.e2. doi:10.1016/j.jand.2019.06.257

While 90% of respondents from acute care settings reported performing nutrition screening, only 63% used a valid tool. Among those using a validated tool, the most common in the United States was the Pediatric Nutrition Screening Tool (PNST), which was used by 21 of the 55 respondents (38%). According to the 2018 systematic review conducted by Becker et al the PNST demonstrated moderate or low validity depending on the anthropometric parameter used for assessment. ^{[11] [11]} Becker PJ, Gunnell Bellini S, Wong Vega M, et al. Validity and Reliability of Pediatric Nutrition Screening Tools for Hospital, Outpatient, and Community Settings: A 2018 Evidence Analysis Center Systematic Review. J Acad Nutr Diet. Feb 2020;120(2):288-318.e2. doi:10.1016/j.jand.2019.06.257

STRONGkids, STAMP, PYMS, and PNST are validated tools; however, STRONGkids currently has high validity based on available data, and may require less time than other tools which increase practicality in routine practice. The main takeaway is to choose a validated, population-appropriate tool to accurately identify pediatric population at risk for malnutrition.

Nutrition Assessment and Diagnostic Tools

Subjective Global Nutrition Assessment

The Subjective Global Nutrition Assessment (SGNA) is a comprehensive nutrition assessment tool validated in hospitalized pediatric patients, ^{[16] [16]} Secker DJ, Jeejeebhoy KN. Subjective Global Nutritional Assessment for children. Am J Clin Nutr. Apr 2007;85(4):1083-9. doi:10.1093/ajcn/85.4.1083 children with liver disease, ^{[17] [17]} Pawaria A, Khanna R, Sood V, et al. Subjective global nutritional assessment as a nutritional tool in childhood chronic liver disease. Br J Nutr. Mar 28 2022;127(6):904-913. doi:10.1017/s0007114521001604 cerebral palsy ^{[18] [18]} Bell KL, Benfer KA, Ware RS, et al. The Pediatric Subjective Global Nutrition Assessment Classifies More Children With Cerebral Palsy as Malnourished Compared With Anthropometry. Journal of the Academy of Nutrition and Dietetics. 2020/11/01/ 2020;120(11):1893-1901. doi:https://doi.org/10.1016/j.jand.2020.04.012, and those with developmental disabilities. ^{[19] [19]} Ong SH, Chen ST. Diagnosis of Malnutrition in Children and Adolescents with Identified Developmental Disabilities (IDD) Using Subjective Global Nutrition Assessment (SGNA). Journal of Tropical Pediatrics. 2022;68(2)doi:10.1093/tropej/fmac007 The SGNA evaluates multiple parameters and classifies patients as well-nourished, moderately malnourished, or severely malnourished. Developed and validated by Secker and Jeejeebhoy, the SGNA assesses:

• Weight gain or loss
• Changes in appetite and intake
• Gastrointestinal symptoms
• Functional status
• Nutrition-focused physical exam (loss of subcutaneous fat, muscle wasting, nutrition-related edema)

In the original SGNA validation study, 175 children were prospectively assessed and followed post-operatively. ^{[16] [16]} Secker DJ, Jeejeebhoy KN. Subjective Global Nutritional Assessment for children. Am J Clin Nutr. Apr 2007;85(4):1083-9. doi:10.1093/ajcn/85.4.1083 Children with malnutrition had significantly different anthropometrics and biochemical concentrations compared to well-nourished children, and experienced higher rates of infectious complications (P=0.042) and longer postoperative length of stay (p<0.002). ^{[20] [20]} Carniel MP, Santetti D, Andrade JS, et al. Validation of a subjective global assessment questionnaire. J Pediatr (Rio J). Nov-Dec 2015;91(6):596-602. doi:10.1016/j.jped.2015.03.005

Carter et al published a retrospective study in 153 children comparing the original SGNA which used anthropometric percentiles and ideal body weight, with an updated version that included change in z-scores and weight-for-length or body mass index (BMI). ^{[21] [21]} Carter L, Hulst JM, Afzal N, Jeejeebhoy K, Brunet-Wood K. Update to the pediatric Subjective Global Nutritional Assessment (SGNA). Nutr Clin Pract. Dec 2022;37(6):1448-1457. doi:10.1002/ncp.10859 In addition, the serial growth questions were updated to include z-score wording as it relates to trends. The study also revised serial growth questions to incorporate z-score trends. The analysis concluded that these updates are unlikely to significantly affect the SGNA's validity, however, may enhance its relevance to modern clinical practice.

Academy ASPEN Indicators for Malnutrition in Pediatrics (AAIMp)

To standardize the diagnosis of malnutrition in hospitalized pediatric patients, the Academy and the American Society of Parental and Enteral Nutrition (ASPEN) published a joint statement outlining a consensus-based diagnostic method. ^{[22] [22]} Jimenez EY, Lamers-Johnson E, Long JM, Woodcock L, Bliss C, Steiber AL. Predictive Validity of the Academy of Nutrition and Dietetics/American Society for Parental Nutrition Indicators to Diagnose Malnutrition and the Screening Tool for Risk on Nutritional Status and Growth among Hospitalized Children Relative to Medical Outcomes. The Journal of Pediatrics. 2025;276doi:10.1016/j.jpeds.2024.114288 This method known as the Academy/ASPEN Indicators for Malnutrition in pediatrics (AAIMp) requires assessment of z-score for weight for height/length, body mass index for age, length/height for age, or MUAC when a single data point is available. When two or more data points are available, they may also include weight-gain velocity, weight loss, deceleration in weight for length/height z-score and inadequate nutrient intake.

To evaluate the validity of this joint consensus method, a prospective cohort study was conducted across 27 acute care hospitals from 2019 to 2023, including 188 children. The nutrition screening tool STRONGkids significantly predicted increased emergency department visits and hospital readmissions for children at moderate and high nutrition risk (P = 0.028 and P=0.023). In contrast, the AAIMp method did not show statistically significant predictive validity for outcomes evaluated. ^{[22] [22]} Jimenez EY, Lamers-Johnson E, Long JM, Woodcock L, Bliss C, Steiber AL. Predictive Validity of the Academy of Nutrition and Dietetics/American Society for Parental Nutrition Indicators to Diagnose Malnutrition and the Screening Tool for Risk on Nutritional Status and Growth among Hospitalized Children Relative to Medical Outcomes. The Journal of Pediatrics. 2025;276doi:10.1016/j.jpeds.2024.114288

Main Takeaway: Although the AAIMp was developed to standardize pediatric malnutrition diagnosis, it currently lacks predictive validity. The SGNA remains the most validated and comprehensive nutrition assessment tool currently available; however, more research is needed.

Conclusion

Accurate identification of malnutrition risk and diagnosis in hospitalized pediatric populations is essential for delivering high-quality care and improving outcomes. Based on current evidence, STRONGkids demonstrates optimal validity and efficiency, making this tool a reasonable option for screening for malnutrition risk.

As a diagnostic tool, the SGNA remains the most validated comprehensive nutrition assessment tool for identifying presence and severity of malnutrition in pediatric populations. Overall practitioners should select tools that are population appropriate, supported by strong validity data, and feasible for routine use. As research emerges, the most effective tools for malnutrition screening and assessment will evolve.