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

• Published in: The American journal of clinical nutrition 2020-12, Vol.112 (6), p.1456-1467
• Main Authors: Lara-Pompa, Nara E, Hill, Susan, Williams, Jane, Macdonald, Sarah, Fawbert, Katherine, Valente, Jane, Kennedy, Kathy, Shaw, Vanessa, Wells, Jonathan C, Fewtrell, Mary
• Format: Article
• Language: English
• Subjects: Adolescent; Anthropometry; Body Composition; Body fat; Body height; Child; Child Development; Child Nutrition Disorders - diagnosis; Child, Preschool; Children; Chronic conditions; Chronic Disease; Chronic illnesses; Clinical outcomes; Complications; Diagnostic systems; Dual energy X-ray absorptiometry; Female; Humans; Male; Malnutrition; nutritional risk; Nutritional status; Parameter identification; Patients; pediatric patients; Pediatrics; Population studies; Risk; Risk Factors; screening; Weight
• ISSN: 0002-9165; 1938-3207
• DOI: 10.1093/ajcn/nqaa142

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