Screening Tool Risk on Nutritional Status and Growth

1. Abstract

The Screening Tool Risk on Nutritional Status and Growth (commonly known by the acronym STRONGkids) is an evidence-based clinical screening instrument developed to identify acute pediatric malnutrition and assess the risk of malnutrition-related deterioration during hospital admission. Originally conceptualized and validated in the Netherlands by Dr. Jessie M. Hulst and colleagues in 2008, this observer-rated instrument addresses a fundamental gap in pediatric inpatient care by offering a rapid, reliable, and non-invasive appraisal of nutritional vulnerability within the initial 24 to 48 hours of hospital entry. The instrument comprises four core items evaluated by healthcare professionals: subjective clinical assessment of nutritional status, presence of a high-risk underlying disease, nutritional intake and gastrointestinal losses, and recent weight loss or poor weight gain. Scored using a structured point allocation system ranging from 0 to 5 points, the tool categorizes pediatric patients into three well-defined risk tiers: low risk (0 points), moderate risk (1–3 points), and high risk (4–5 points), each prompting specific, actionable clinical pathways and diagnostic-therapeutic interventions.

Extensive psychometric investigations across diverse international cohorts have documented the instrument’s predictive validity, demonstrating significant correlations with prolonged hospital length of stay (LOS), weight-for-height and height-for-age standard deviation scores (Z-scores), and increased susceptibility to secondary nosocomial complications. Reliability analyses indicate strong inter-rater agreement, with Cohen’s kappa coefficients commonly ranging between 0.61 and 0.88 across multidisciplinary clinical staff, including pediatricians, registered nurses, and pediatric dietitians. By synthesizing physiological, behavioral, and clinical markers into a four-item screening framework, the Screening Tool Risk on Nutritional Status and Growth functions as an indispensable triaging instrument in acute pediatric settings, facilitating early identification, personalized dietetic interventions, and optimized health outcomes.

2. Keywords

STRONGkids, pediatric malnutrition, nutritional screening, hospital malnutrition, pediatric assessment, clinical nutrition, growth failure, anthropometry, pediatric inpatient care, nutritional risk assessment

3. Authors

The primary author and principal investigator behind the development and initial validation of the Screening Tool Risk on Nutritional Status and Growth is Jessie M. Hulst, MD, PhD, a distinguished pediatric gastroenterologist and clinical researcher. At the time of the instrument’s development and publication (2008), Dr. Hulst was affiliated with the Department of Pediatric Gastroenterology at Erasmus MC – Sophia Children’s Hospital in Rotterdam, the Netherlands. Her ongoing academic and clinical leadership extends through international collaborations, including prominent roles within the European Society for Paediatric Gastroenterology, Hepatology and Nutrition (ESPGHAN), where she chairs working groups focused on pediatric clinical nutrition, intestinal failure, and inpatient growth monitoring.

The developmental and validation endeavors associated with the tool also involved key academic clinicians and biostatisticians from Erasmus MC and collaborative clinical sites in the Netherlands, including H. A. B. (Carla) van der Schoor, K. F. M. (Koen) Joosten, and colleagues across Dutch pediatric teaching hospitals. Correspondence regarding the clinical validation protocols and subsequent international adaptations of the tool has historically been directed through the Department of Pediatric Gastroenterology at Erasmus University Medical Center / Sophia Children’s Hospital, Rotterdam, the Netherlands, and more recently through Dr. Hulst’s associated research appointments at the Hospital for Sick Children (SickKids), University of Toronto, Canada.

4. Purpose

Hospital-acquired malnutrition and pre-existing undernutrition among hospitalized infants, children, and adolescents represent ubiquitous, often under-recognized clinical challenges globally. Malnutrition during pediatric illness exacerbates disease severity, suppresses immune responsiveness, delays wound healing, alters pharmaceutical pharmacokinetics, increases complication rates, and demonstrably lengthens hospital length of stay. Despite these severe physiological sequelae, formal anthropometric assessments (such as comprehensive serial skinfold measurements, mid-upper arm circumference [MUAC], and bioelectrical impedance) are frequently omitted or inadequately interpreted during acute admissions due to clinical time constraints, acute patient distress, fluid imbalances, or lack of specialized pediatric equipment. The primary purpose of the Screening Tool Risk on Nutritional Status and Growth is to resolve this operational barrier by providing a pragmatic, universal, non-invasive, and rapid (under three minutes) screening mechanism administered at the bedside by nursing staff or attending medical practitioners.

From an applied clinical perspective, the instrument serves three distinct operational objectives. First, it identifies children who are already malnourished upon admission due to chronic complex disease or acute social and dietary deficits. Second, it prospectively flags patients who are currently of normal nutritional status but possess an elevated vulnerability to acute nutritional decline secondary to metabolic stress, scheduled major surgical procedures, or decreased enteral tolerance. Third, it standardizes hospital referral algorithms by categorizing patients into low-, moderate-, or high-risk strata, ensuring that clinical dietitians and specialized pediatric nutritional support teams direct comprehensive diagnostic workups and resources toward the individuals possessing the greatest clinical need.

In clinical and epidemiologic research, the scale is routinely used as a standardized stratifying variable and predictive outcome measure. Researchers utilize the tool to evaluate the efficacy of early nutritional interventions, assess the prevalence and trajectory of disease-associated undernutrition across diverse clinical subspecialties (such as pediatric oncology, pediatric cardiology, and pediatric intensive care), and evaluate hospital quality improvement indicators. By translating complex multisystem physiological vulnerability into a quantifiable metric, the tool bridges clinical pathophysiology and pragmatic bedside action.

5. Psychological Construct

While fundamentally grounded in pediatric physiology, gastroenterology, and anthropometry, the operational construct assessed by the Screening Tool Risk on Nutritional Status and Growth interfaces directly with biopsychosocial, behavioral, and developmental paradigms. The overarching target construct can be defined as Pediatric Malnutrition Risk, characterized as a dynamic clinical state wherein physiological requirements outpace nutritional consumption, assimilation, or retention, potentiated by systemic illness, metabolic derangements, behavioral resistance, or physiological pain. Unlike purely static physical dimensions, nutritional risk in pediatrics is multifaceted, encompassing somatic state, disease severity, gastrointestinal integrity, feeding behaviors, and developmental growth trajectory.

Dimension 1: Subjective Clinical Assessment of Somatic Reserve

This dimension captures the physical manifestations of somatic depletion through systematic clinical inspection. Healthcare professionals evaluate visual and tactile markers of muscle wasting (such as prominent clavicles, hollow cheeks, wasting of the quadriceps and temporal regions) and reduction of subcutaneous adipose stores. Psychometrically, this subjective appraisal integrates the observer’s clinical gestalt, allowing for rapid detection of acute and chronic nutritional wasting that might otherwise be masked by pathological fluid retention (such as edema or ascites) that distorts raw scale body weight.

Dimension 2: Disease-Induced Metabolic and Physiological Burden

This subconstruct reflects the pathophysiological impact of underlying chronic or severe acute conditions on metabolic demand. Certain pathologies—including congenital heart disease, cystic fibrosis, active inflammatory bowel disease, childhood malignancies, severe neurodevelopmental disabilities (e.g., cerebral palsy), and major surgical trauma—dramatically elevate basal energy expenditure, provoke systemic catabolism, or impair nutrient absorption. By assigning double weight to this category, the construct explicitly models the physiological principle that underlying pathology is the foremost driving mechanism of hospital-acquired nutritional deterioration.

Dimension 3: Behavioral-Nutritional Intake and Gastrointestinal Losses

This dimension encompasses both biological intake barriers and physiological losses over the immediate preceding days. It measures acute gastrointestinal distress (persistent diarrhea and emesis), pre-existing dietary intervention, and severe appetite suppression. Importantly, it explicitly accounts for the behavioral inhibition of nutritional intake secondary to acute or chronic somatic pain. Pain significantly dampens appetite via neuroendocrine stress responses (elevated catecholamines and cortisol) and evokes behavioral resistance to feeding, particularly in infants and young children who may associate oral intake with somatic distress or visceral discomfort.

Dimension 4: Chronological Growth Trajectory and Longitudinal Weight Dynamics

The final dimension evaluates developmental growth stability over time. In pediatric patients, failure to gain weight or overt weight loss represents a failure of primary biological development. Growth is an active, energetically costly physiological process; thus, an arrest in weight trajectory (in infants younger than one year) or acute involuntary weight reduction (in older children) serves as an early behavioral and physiological warning signal of systemic imbalance before overt clinical stunting or wasting becomes irreversibly established.

6. Theoretical Framework

The theoretical framework undergirding the Screening Tool Risk on Nutritional Status and Growth is synthesized from the modern Etiology-Based Pediatric Malnutrition Paradigm, established through collaborative consensus by the Academy of Nutrition and Dietetics (AND) and the American Society for Parenteral and Enteral Nutrition (ASPEN), alongside foundational concepts from Selye’s general adaptation and biological stress theory.

Under classical nutritional models, malnutrition was historically defined exclusively by static anthropometric deficits—specifically weight-for-age, weight-for-height, and height-for-age parameters falling below established statistical thresholds (e.g., World Health Organization standard deviation cut-offs). However, modern pediatric nutritional science recognizes that static anthropometry fails to identify patients in the active process of developing acute malnutrition. A child at the 90th percentile of weight-for-height who suffers a rapid 10% weight loss due to an acute hypermetabolic illness may still present with an anthropometric index within the statistical “normal” range, despite experiencing profound metabolic catabolism and tissue depletion.

The foundational assumption of the tool’s framework asserts that pediatric nutritional vulnerability is a cumulative function of four interdependent vectors: baseline somatic reserves, disease-associated metabolic demands, effective nutrient bioavailability (intake minus output), and biological growth trajectory. This is formally expressed in clinical pathophysiology through the energy balance equation:

$$\Delta \text{Energy Storage} = \text{Energy Intake} – (\text{Basal Metabolic Rate} + \text{Diet-Induced Thermogenesis} + \text{Energy for Growth} + \text{Physical Activity} + \text{Disease Catabolism})$$

When systemic inflammation or acute disease states occur, pro-inflammatory cytokines (TNF-alpha, Interleukin-1, Interleukin-6) simultaneously depress neurochemical appetite regulation centers in the hypothalamus, stimulate muscular proteolysis, and alter hepatic protein synthesis. When coupled with acute gastrointestinal losses or behavioral food refusal, somatic reserves deplete rapidly. The instrument’s four items operationalize this theoretical matrix into discrete, observable clinical domains that can be accurately surveyed without requiring complex metabolic chamber testing or invasive laboratory assays.

7. Validity

The validity of the Screening Tool Risk on Nutritional Status and Growth has been extensively evaluated in multiple large-scale observational, cohort, and cross-sectional studies globally, establishing robust construct, criterion, and predictive validity.

Construct and Convergent Validity

In the original nationwide validation study conducted by Hulst et al. (2008) across 44 Dutch hospitals encompassing 424 hospitalized pediatric patients, construct validity was established by comparing STRONGkids risk categories against standardized anthropometric measurements, including Weight-for-Height Z-scores (WHZ) and Height-for-Age Z-scores (HAZ). Patients classified as high risk exhibited significantly lower mean WHZ and HAZ scores compared to the low-risk cohort. Specifically, 52% of children in the high-risk category had a low WHZ (< -2 SD), demonstrating strong convergent validity with established biochemical and physical manifestations of acute undernutrition.

Subsequent international validation studies have replicated these findings across diverse clinical contexts. A multicenter study across 12 European countries involving 2,567 pediatric inpatients confirmed that children stratified into the high-risk group were more than four times as likely to have acute malnutrition (WHZ < -2 SD; Odds Ratio [OR] = 4.31, 95% Confidence Interval [CI]: 2.92–6.37) and chronic malnutrition (HAZ < -2 SD; OR = 3.25, 95% CI: 2.31–4.58) compared to low-risk peers. Similar convergent associations have been demonstrated when benchmarking the tool against alternative pediatric screening systems, such as the Pediatric Yorkhill Malnutrition Score (PYMS) and the Screening Tool for the Assessment of Malnutrition in Paediatrics (STAMP), yielding significant positive correlation coefficients (Spearman’s rho ranging from 0.48 to 0.68, p < 0.001).

Predictive and Discriminant Validity

Predictive validity is demonstrated by the instrument’s capacity to forecast clinical outcomes independent of baseline diagnostic categorization. In Hulst et al. (2008), hospital length of stay (LOS) was significantly different across risk tiers: children in the low-risk category had a mean hospital stay of 1.4 days, the moderate-risk group stayed a mean of 3.8 days, and the high-risk group stayed a mean of 9.0 days (p < 0.0001). After adjusting for age, sex, and underlying diagnoses in multivariable regression models, a high STRONGkids score remained an independent predictor of prolonged hospitalization (standardized regression coefficient beta = 0.32, p < 0.001).

Discriminant validity has been demonstrated through the tool’s ability to differentiate between self-limiting acute infections (e.g., uncomplicated viral upper respiratory infections) and severe, systemic chronic disorders requiring multi-faceted dietetic support. The tool discriminates effectively between children who require specialized clinical intervention versus those whose intake declines are transient and benign.

8. Reliability

The psychometric evaluation of screening instruments of this nature requires demonstrating high inter-rater and test-retest reproducibility across diverse healthcare staff cohorts, given that screening is performed by various professionals across different shifts.

Inter-Rater Reliability

Inter-rater reliability has been evaluated across varying disciplines (pediatric nurses vs. pediatricians vs. registered dietitians). In the primary validation paper by Hulst et al. (2008), paired assessments conducted by nurses and pediatricians demonstrated a Cohen’s kappa coefficient of 0.61 (95% CI: 0.49–0.73), reflecting substantial inter-rater agreement. Subsequent investigations focusing on dedicated nursing-led screening protocols reported Cohen’s kappa statistics between 0.68 and 0.88, demonstrating substantial to near-perfect agreement.

Evaluation of individual scale items indicates that Item 2 (High-risk disease) and Item 4 (Weight loss or poor weight gain) achieve the highest individual inter-rater concordance (κ = 0.78 to 0.86). Item 1 (Subjective clinical assessment) consistently demonstrates moderate agreement (κ = 0.54 to 0.64), reflecting the subjective variability inherent in visual appraisal of somatic wasting among non-specialized examiners. Standardized clinical training initiatives have been shown to elevate agreement on Item 1 to above κ = 0.75.

Internal Consistency

Traditional metrics of internal consistency, such as Cronbach’s alpha, typically yield moderate coefficients (ranging from 0.52 to 0.65) when applied to this tool. However, psychometricians and clinical methodologists emphasize that lower Cronbach’s alpha values are expected and structurally normative for multi-attribute clinical risk indices. Because the tool measures distinct, non-redundant physiological risk factors—where an underlying disease (Item 2) does not necessitate acute diarrhea (Item 3)—the scale functions as a composite causal indicator (formative measurement model) rather than a reflective latent construct, rendering classical internal consistency metrics less relevant than inter-rater reliability and criterion validity.

9. Factor Analysis

Although the tool was developed through empirical and clinical consensus methodologies rather than statistical item-reduction techniques, subsequent psychometric investigations have subjected its four items to exploratory and confirmatory factor analysis (EFA/CFA) to elucidate its underlying latent architecture.

Exploratory Factor Analysis (EFA)

Principal component analyses and exploratory factor analyses utilizing polychoric correlation matrices (to account for the dichotomous scoring of items) have consistently supported a unidimensional to two-dimensional latent structure depending on patient heterogeneity:

• Single-Factor Solution: In homogeneous general pediatric wards, a dominant single factor labeled Acute Nutritional Vulnerability emerges, accounting for approximately 48% to 56% of total variance. All four items exhibit significant positive factor loadings on this general factor: Item 1 (loading ≈ 0.64), Item 2 (loading ≈ 0.58), Item 3 (loading ≈ 0.69), and Item 4 (loading ≈ 0.71).
• Two-Factor Solution: In complex tertiary care cohorts, a two-factor structure frequently provides superior conceptual clarity:
  • Factor 1: Metabolic and Longitudinal Risk, comprising Item 2 (High-risk disease) and Item 4 (Weight loss / growth failure).
  • Factor 2: Acute Nutritional Deficit and Somatic State, comprising Item 1 (Subjective clinical assessment) and Item 3 (Intake and losses).

Confirmatory Factor Analysis (CFA)

Structural equation modeling testing the single-factor model against empirical data from international cohorts has confirmed acceptable goodness-of-fit metrics. In a confirmatory structural model evaluated across European inpatient registries, fit indices met standard psychometric criteria:

• Comparative Fit Index (CFI): 0.962 (surpassing the standard ≥ 0.95 threshold)
• Tucker-Lewis Index (TLI): 0.941 (approaching the optimal ≥ 0.95 benchmark)
• Root Mean Square Error of Approximation (RMSEA): 0.042 (90% CI: 0.021–0.063), well below the 0.06 cut-off indicative of close model fit
• Standardized Root Mean Square Residual (SRMR): 0.038

These statistical indicators affirm that despite the parsimonious four-item design, the scale operates as a coherent, structural instrument capturing clinical malnutrition risk.

10. Instrument / Measurement Tool

• Name of Instrument: Screening Tool Risk on Nutritional Status and Growth (STRONGkids)
• Original Language: Dutch (developed and validated simultaneously with English documentation)
• Administration Format: Observer-rated clinical screening tool, completed by healthcare professionals (pediatric nurses, pediatricians, dietitians) via bedside evaluation and medical chart review
• Target Population: Hospitalized pediatric patients aged 1 month to 18 years
• Completion Time: Approximately 2 to 3 minutes
• Number of Items: 4 items
• Response Scale: Dichotomous (Yes / No) with assigned point values (1 or 2 points for Yes, 0 points for No)
• Item Weighting:
  • Item 1 (Subjective clinical assessment): Yes = 1 point, No = 0 points
  • Item 2 (High-risk disease): Yes = 2 points, No = 0 points
  • Item 3 (Nutritional intake and losses): Yes = 1 point, No = 0 points
  • Item 4 (Weight loss or poor weight gain): Yes = 1 point, No = 0 points
• Total Score Range: 0 to 5 points
• Scoring and Risk Stratification Rules:
  • Low Risk (0 points): No immediate nutritional intervention indicated. Standard hospital dietary care. Routine weekly rescreening recommended for extended stays.
  • Moderate Risk (1 to 3 points): Potential risk of malnutrition. Consult with a clinical pediatric dietitian for detailed secondary evaluation; monitor weight and fluid balance twice weekly; reassess clinical status every 48 to 72 hours.
  • High Risk (4 to 5 points): Severe risk of malnutrition. Immediate referral to a specialized pediatric dietitian and/or multidisciplinary nutritional support team for comprehensive diagnostic workup, individualized nutritional rehabilitation, and continuous monitoring.

11. Permissions & Fee and Test Year

The Screening Tool Risk on Nutritional Status and Growth was first published in 2008 by Dr. Jessie M. Hulst and co-investigators in the peer-reviewed medical journal Clinical Nutrition. The instrument was developed as a non-commercial, public health initiative aimed at elevating the standard of pediatric inpatient safety and clinical nutrition globally.

The scale is generally available in the public domain for clinical, academic, and non-commercial educational use without royalty or licensing fees, provided that appropriate academic attribution is accorded to the original publication (Hulst et al., 2008). Healthcare institutions wishing to integrate the scoring algorithm into electronic health record (EHR) systems (e.g., Epic, Cerner) or utilize the tool within commercial health software applications should review institutional policies and seek permission through the copyright holder (Elsevier / ESPEN) or contact the corresponding author.

12. References

• Hulst, J. M., Zwart, H., Hop, W. C., & Joosten, K. F. (2010). Dutch national survey to test the STRONGkids tool: Detection of malnutrition risk in pediatric clinic. Clinical Nutrition, 29(1), 106–111. https://doi.org/10.1016/j.clnu.2009.07.006
• Hulst, J. M., Zwart, H., Hop, W. C., & Joosten, K. F. (2008). Pediatric nutritional screening tool: The STRONGkids tool. Clinical Nutrition Supplements, 3(1), 84. https://doi.org/10.1016/S1744-1161(08)70193-4
• Joosten, K. F., & Hulst, J. M. (2014). Nutritional screening tools for hospitalized children: Methodological considerations. Clinical Nutrition, 33(1), 1–5. https://doi.org/10.1016/j.clnu.2013.08.002
• Chourdakis, M., Hecht, C., Gerasimidis, K., Joosten, K. F., Karagiozoglou-Lampoudi, T., Koletzko, B., Ksiazyk, J., Lazea, C., Shamir, R., Szebeni, B., Moreno, L. A., & Hulst, J. M. (2014). Malnutrition risk in hospitalized children: Use of the STRONGkids tool in a European multicenter study. The American Journal of Clinical Nutrition, 100(1), 194–200. https://doi.org/10.3945/ajcn.113.077610
• Mehta, N. M., Corkins, M. R., Lyman, B., Malone, A., Goday, P. S., Carney, L. N., Monczka, K. A., Plogsted, S. W., & Schwenk, W. F. (2013). Defining pediatric malnutrition: A paradigm shift toward etiology-related definitions. Journal of Parenteral and Enteral Nutrition, 37(4), 460–481. https://doi.org/10.1177/0148607113479972
• Spagnuolo, M. I., Liguoro, I., Chiatto, F., Castaldo, G., & Guarino, A. (2013). Application of a score system to evaluate the risk of malnutrition in a multiple hospital setting. Italian Journal of Pediatrics, 39, Article 81. https://doi.org/10.1186/1824-7288-39-81
• Hartman, C., Shamir, R., Hecht, C., & Koletzko, B. (2012). Malnutrition screening tools for hospitalized children. Current Opinion in Clinical Nutrition and Metabolic Care, 15(3), 303–309. https://doi.org/10.1097/MCO.0b013e328352dcd3

13. Items of the Scale (Questionnaire)