Nutritional assessment and clinical application of nutritional risk screening tools in critically ill children.
- Author:
Jun-Ying QIAO
1
;
Fei-Fei GUO
;
Fan LI
;
Li-Xia CHEN
;
Bin LUAN
Author Information
1. Department of Pediatrics, Third Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China. junying.qiao@163.com.
- Publication Type:Journal Article
- MeSH:
Child;
Critical Illness;
Humans;
Malnutrition;
Mass Screening;
Nutrition Assessment;
Nutritional Status;
Risk Assessment
- From:
Chinese Journal of Contemporary Pediatrics
2019;21(6):528-533
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE:To investigate the nutritional status of critically ill hospitalized children and to explore the value of nutritional risk screening tools in the nutritional risk assessment.
METHODS:The clinical data of 211 critically ill children who were admitted to the pediatric intensive care unit from November 2017 to April 2018 were collected to evaluate their nutritional status on admission and at discharge. Two nutritional risk screening tools, STRONGkids and PYMS, were used for nutritional risk screening in the 211 children.
RESULTS:Among the 211 patients, 68 (32.2%) were found to have malnutrition on admission, with 34 cases each of moderate and severe malnutrition. Moderate or high nutritional risk was found in 154 cases (73.0%) with STRONGkids and 165 cases (78.2%) with PYMS. Using weight-for-age Z-score as the gold standard to evaluate the efficacy of the two nutritional risk screening tools, the areas under the receiver operating characteristic curves of STRONGkids and PYMS were 0.822 and 0.759 respectively. Both tools had a significant clinical value in screening for malnutrition (P<0.05), but there was no significant difference in clinical efficacy between them (P>0.05). With the optimal cut-off value of 3 points, the sensitivities of STRONGkids and PYMS for screening of malnutrition were 92.1% and 76.2% respectively. The children with moderate or high nutritional risk on admission had a significantly poorer prognosis than those with low nutritional risk (P=0.014 and 0.001 respectively). The children with severe malnutrition had a significantly poorer prognosis than those with normal nutrition (P=0.0009).
CONCLUSIONS:The detection rates of malnutrition and nutritional risk are high in critically ill children. Malnutrition/high nutritional risk is related to a poor prognosis. Both STRONGkids and PYMS have a clinical value for nutritional risk screening in critically ill children, and they have similar clinical efficacy; however, STRONGkids is more sensitive.
