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. 2020 Dec 10;112(6):1456-1467.
doi: 10.1093/ajcn/nqaa142.

Use of standardized body composition measurements and malnutrition screening tools to detect malnutrition risk and predict clinical outcomes in children with chronic conditions

Nara E Lara-Pompa  1 , Susan Hill  2 , Jane Williams  1 , Sarah Macdonald  2 , Katherine Fawbert  2 , Jane Valente  2 , Kathy Kennedy  1 , Vanessa Shaw  2 , Jonathan C Wells  1 , Mary Fewtrell  1
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Free article

Use of standardized body composition measurements and malnutrition screening tools to detect malnutrition risk and predict clinical outcomes in children with chronic conditions

Nara E Lara-Pompa et al. Am J Clin Nutr. .
Free article

Abstract

Background: Better tools are needed to diagnose and identify children at risk of clinical malnutrition.

Objectives: We aimed to compare body composition (BC) and malnutrition screening tools (MSTs) for detecting malnutrition on admission; and examine their ability to predict adverse clinical outcomes [increased length of stay (LOS) and complications] in complex pediatric patients.

Methods: This was a prospective study in children 5-18 y old admitted to a tertiary pediatric hospital (n = 152). MSTs [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)] were completed on admission. Weight, height, and BC [fat mass (FM) and lean mass (LM) by DXA] were measured (n = 118). Anthropometry/BC and MSTs were compared with each other and with clinical outcomes.

Results: Subjects were significantly shorter with low LM compared to reference data. Depending on the diagnostic criteria used, 3%-17% were classified as malnourished. Agreement between BC/anthropometric parameters and MSTs was poor. STAMP and STRONGkids identified children with low weight, LM, and height. PYMS, and to a lesser degree STRONGkids, identified children with increased LOS, as did LM compared with weight or height. Patients with complications had lower mean ± SD LM SD scores (-1.38 ± 1.03 compared with -0.74 ± 1.40, P < 0.05). In multivariable models, PYMS high risk and low LM were independent predictors of increased LOS (OR: 3.76; 95% CI: 1.36, 10.35 and OR: 3.69; 95% CI: 1.24, 10.98, respectively). BMI did not predict increased LOS or complications.

Conclusions: LM appears better than weight and height for predicting adverse clinical outcomes in this population. BMI was a poor diagnostic parameter. MSTs performed differently in associations to BC/anthropometry and clinical outcomes. PYMS and LM provided complementary information regarding LOS. Studies on specific patient populations may further clarify the use of these tools and measurements.

Keywords: body composition; clinical outcomes; malnutrition; nutritional risk; pediatric patients; screening.

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