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

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.

Carbapenem-resistant Enterobacterales colonization and subsequent infection in a neonatal intensive care unit in Shanghai, China

“…All clinical information for CR-KP patients was systematically reviewed from electronic medical records including sex, birth weight, gestational age, date of NICU admission, previous surgery, and invasive procedures, including mechanical ventilation, umbilical venous catheter (UV), umbilical arteriosus catheter (UA), peripherally inserted central catheter (PICC), nasogastric tube insertions, and antibiotic exposure. Neonatal nutritional risk assessment (improved evaluation scale based on STRONGkids [ 9 ] in our hospital; Table I ) and the Score for Neonatal Acute Physiology II (SNAP-II) [ 10 ] were also completed on admission.…”

The efficacy of nutritional screening tools for diagnosis of malnutrition in children with epidermolysis bullosa

“…Most works in literature recommended STRONGkids as a more efficient nutritional screening tool for patients with chronic diseases (Huysentruyt et al, 2013;Durakbaşa.et al, 2014;Lara-Pompa et al, 2020), but with a focus on EB patients, STRONGkids was very inefficient. This is because EB is not included in the disease list for calculating STRONGkids.…”

Validation of bioelectrical impedance analysis for body composition assessment in children with obesity aged 8-14y

“…Furthermore, there are several conditions in which analysing body composition rather than simple anthropometry may have several advantages in terms of diagnosing and performing interventions. For example, in sarcopenic obesity, body composition measures allow focused nutritional interventions, or fat free mass has shown to be a predictor of hospital stay duration in children while BMI not [49]. That is why there are several predictive equations population-specific published to estimate body composition from BIA.…”