Computer‐Based Malnutrition Risk Calculation May Enhance the Ability to Identify Pediatric Patients at Malnutrition‐Related Risk for Unfavorable Outcome |
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| Authors: | Thomais Karagiozoglou‐Lampoudi MD PhD Efstratia Daskalou MSc Dimitrios Lampoudis MSc Aggeliki Apostolou MSc Charalampos Agakidis MD PhD |
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| Affiliation: | 1. Clinical Nutrition Laboratory “Christos Mantzoros,” Nutrition‐Dietetics Department, Alexander Technological Education Institute of Thessaloniki, Greece;2. Department of Applied Informatics, University of Macedonia, Thessaloniki, Greece;3. 1st Pediatric Department, Aristotle University of Thessaloniki, Hippokratio General Hospital, Thessaloniki, Greece |
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| Abstract: | 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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| Keywords: | hospital malnutrition pediatric patients computer‐based score |
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