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Computer-based malnutrition risk calculation may enhance the ability to identify pediatric patients at
Thomais Karagiozoglou-Lampoudi1, Efstratia Daskalou2, Dimitrios Lampoudis3
1Clinical Nutrition Laboratory "Christos Mantzoros," Nutrition-Dietetics Department, Alexander Technological Education Institute of Thessaloniki, Greece clinicalnutritionlab@yahoo.gr.
Insights
The Pediatric Digital Scaled MAlnutrition Risk screening Tool (PeDiSMART) effectively identifies pediatric patients at risk of malnutrition. This computer-based tool improves the accuracy of predicting unfavorable outcomes compared to other methods.
Area of Science:
- Pediatric Nutrition
- Clinical Informatics
- Medical Device Development
Background:
- Malnutrition poses a significant risk for unfavorable outcomes in pediatric patients.
- Accurate and timely identification of malnutrition risk is crucial for effective intervention.
- Existing screening tools may have limitations in pediatric populations.
Purpose of the Study:
- To evaluate the effectiveness of the Pediatric Digital Scaled MAlnutrition Risk screening Tool (PeDiSMART) in identifying pediatric patients at risk of malnutrition-related adverse outcomes.
- To compare the performance of PeDiSMART with existing malnutrition screening tools (PYMS, STRONGkids, STAMP).
Main Methods:
- A prospective cohort study involving 500 pediatric patients (1 month to 17 years).
- Calculation of PeDiSMART scores upon admission, alongside anthropometry and bioelectrical impedance phase angle (BIA PhA).
- Comparison of PeDiSMART's association with clinical outcomes (weight loss, nutrition support, hospitalization duration) against PYMS, STRONGkids, and STAMP.
Main Results:
- PeDiSMART scores showed an inverse correlation with anthropometry and BIA PhA.
- Malnutrition risk allocation by PeDiSMART was significantly associated with adverse outcomes, independent of age and anthropometry.
- PeDiSMART demonstrated higher sensitivity (87%) and specificity (75%) than STRONGkids and STAMP, with a significantly larger area under the ROC curve.
Conclusions:
- PeDiSMART enhances the efficacy and reproducibility of identifying pediatric patients at risk of malnutrition-related adverse outcomes.
- The tool is applicable across the full age range of hospitalized pediatric patients and can be integrated into electronic health records.
- PeDiSMART-based patient risk allocation correlates strongly with clinical outcomes.
Background:
The study aimed to test the hypothesis that computer-based calculation of malnutrition risk may enhance the ability to identify pediatric patients at malnutrition-related risk for an unfavorable outcome. The Pediatric Digital Scaled MAlnutrition Risk screening Tool (PeDiSMART), incorporating the World Health Organization (WHO) growth reference data and malnutrition-related parameters, was used.
Materials And Methods:
This was a prospective cohort study of 500 pediatric patients aged 1 month to 17 years. Upon admission, the PeDiSMART score was calculated and anthropometry was performed. Pediatric Yorkhill Malnutrition Score (PYMS), Screening Tool Risk on Nutritional Status and Growth (STRONGkids), and Screening Tool for the Assessment of Malnutrition in Pediatrics (STAMP) malnutrition screening tools were also applied. PeDiSMART's association with the clinical outcome measures (weight loss/nutrition support and hospitalization duration) was assessed and compared with the other screening tools.
Results:
The PeDiSMART score was inversely correlated with anthropometry and bioelectrical impedance phase angle (BIA PhA). The score's grading scale was based on BIA Pha quartiles. Weight loss/nutrition support during hospitalization was significantly independently associated with the malnutrition risk group allocation on admission, after controlling for anthropometric parameters and age. Receiver operating characteristic curve analysis showed a sensitivity of 87% and a specificity of 75% and a significant area under the curve, which differed significantly from that of STRONGkids and STAMP. In the subgroups of patients with PeDiSMART-based risk allocation different from that based on the other tools, PeDiSMART allocation was more closely related to outcome measures.
Conclusion:
PeDiSMART, applicable to the full age range of patients hospitalized in pediatric departments, graded according to BIA PhA, and embeddable in medical electronic records, enhances efficacy and reproducibility in identifying pediatric patients at malnutrition-related risk for an unfavorable outcome. Patient allocation according to the PeDiSMART score on admission is associated with clinical outcome measures.
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