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Malnutrition risk in hospitalized children: use of 3 screening tools in a large European population
Michael Chourdakis1, Christina Hecht1, Konstantinos Gerasimidis2
1Ludwig-Maximilians-University of Munich, Division of Metabolic and Nutritional Medicine, Dr. von Hauner Children's Hospital, University of Munich Medical Centre, Munich, Germany;
Insights
Pediatric malnutrition screening tools like PYMS, STAMP, and STRONGKIDS showed poor agreement in identifying at-risk children. These tools failed to consistently detect malnutrition, questioning their clinical practice utility.
Area of Science:
- Pediatric Nutrition
- Clinical Assessment
- Public Health
Background:
- Multiple malnutrition screening tools exist for pediatric inpatients.
- Evaluating the comparative performance of these tools is crucial for effective patient management.
Purpose of the Study:
- To compare the performance of three popular pediatric nutrition screening tools: Pediatric Yorkhill Malnutrition Score (PYMS), Screening Tool for the Assessment of Malnutrition in Pediatrics (STAMP), and Screening Tool for Risk of Impaired Nutritional Status and Growth (STRONGKIDS).
- To assess their correlation with anthropometric measures, body composition, and clinical outcomes in hospitalized children across Europe.
Main Methods:
- The study involved 2567 inpatients across 14 hospitals in 12 European countries.
- The PYMS, STAMP, and STRONGKIDS tools were applied, and their risk classifications were compared.
- Correlations with anthropometric data, body composition, length of hospital stay (LOS), and infection rates were analyzed.
Main Results:
- Completion rates for the screening tools were high (PYMS: 86%, STAMP: 84%, STRONGKIDS: 81%).
- Significant discrepancies in risk classification were observed between the tools, with only 41% overall agreement.
- High-risk children identified by PYMS, STAMP, and STRONGKIDS experienced longer LOS (1.4-1.8 days longer) compared to low-risk children.
- A notable percentage of high-risk children identified by these tools exhibited subnormal anthropometric measures (low BMI or height-for-age SDSs).
Conclusions:
- The identification and classification of malnutrition risk in pediatric inpatients vary significantly among the evaluated screening tools.
- A substantial proportion of children with abnormal anthropometric measurements were not consistently identified by all tools.
- The study's findings do not support the recommendation of any of these specific screening tools for routine clinical practice.
Background:
Several malnutrition screening tools have been advocated for use in pediatric inpatients.
Objective:
We evaluated how 3 popular pediatric nutrition screening tools [i.e., the Pediatric Yorkhill Malnutrition Score (PYMS), the Screening Tool for the Assessment of Malnutrition in Pediatrics (STAMP), and the Screening Tool for Risk of Impaired Nutritional Status and Growth (STRONGKIDS)] compared with and were related to anthropometric measures, body composition, and clinical variables in patients who were admitted to tertiary hospitals across Europe.
Design:
The 3 screening tools were applied in 2567 inpatients at 14 hospitals across 12 European countries. The classification of patients into different nutritional risk groups was compared between tools and related to anthropometric measures and clinical variables [e.g., length of hospital stay (LOS) and infection rates].
Results:
A similar rate of completion of the screening tools for each tool was achieved (PYMS: 86%; STAMP: 84%; and STRONGKIDS: 81%). Risk classification differed markedly by tool, with an overall agreement of 41% between tools. Children categorized as high risk (PYMS: 25%; STAMP: 23%; and STRONGKIDS: 10%) had a longer LOS than that of children at low risk (1.4, 1.4, and 1.8 d longer, respectively; P < 0.001). In high-risk patients identified with the PYMS, 22% of them had low (<-2) body mass index (BMI) SD-scores (SDSs), and 8% of them had low height-for-age SDSs. For the STAMP, the percentages were 19% and 14%, respectively, and for the STRONGKIDS, the percentages were 23% and 19%, respectively.
Conclusions:
The identification and classification of malnutrition risk varied across the pediatric tools used. A considerable portion of children with subnormal anthropometric measures were not identified with all of the tools. The data obtained do not allow recommending the use of any of these screening tools for clinical practice. This study was registered at clinicaltrials.gov as NCT01132742.
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