Evaluation of malnutrition and screening tools in hospitalized children.

BACKGROUND & AIMS: Detecting malnutrition and its related risk factors are crucial, in hospitalized children. Anthropometric z scores are used to assess malnutrition. Screening tools also aim to detect the presence of malnutrition and the developing risk of malnutrition in hospitalized children to determine who may benefit from nutritional support. Therefore, the aims of the study are to detect malnutrition and its related demographic and clinical risk factors in hospitalized children and determining the sensitivity of Screening Tool for the Assessment of Malnutrition in Pediatrics (STAMP) and Pediatric Yorkhill Malnutrition Score (PYMS) screening tools.

METHODS

A total of 130 hospitalized children aged between 0 and 18 years were included in to study. A survey including demographic and clinical characteristics, STAMP and PYMS were applied to parents of the children. Patients were classified into nutritional risk groups through screening tools. Anthropometric measurements (body weight, length/height, and middle upper arm circumference (MUAC) of the children were taken. Body mass index-for-age and height-for-age z scores were calculated to assess acute and chronic malnutrition prevalence. MUAC-for-age z scores were calculated as well. To detect independent risk factors for acute and chronic malnutrition multivariable logistic regression models were constructed.

RESULTS

A total of 14.6% of hospitalized children had acute malnutrition, 21.5% of children had chronic malnutrition and 27.7% of them had low MUAC standard deviation score (SDS) (less than -2). The independent risk factors for acute malnutrition were younger maternal age at birth and long length of stay (p < 0.05). The independent risk factors for chronic malnutrition were being female, younger maternal age at birth, longer illness duration and having urological or allergy and immunological diseases (p < 0.05). However, MUAC for age SDS groups were not related to any demographic and clinical factors, in children of all ages (p > 0.05). Regarding the screening tools, PYMS displayed 100% sensitivity against acute malnutrition. While PYMS displayed better sensitivity to identify acute malnutrition than STAMP, STAMP was more sensitive than PYMS to detect chronic malnutrition and low MUAC SDS.

CONCLUSIONS

Low MUAC for age SDS was not related to any demographic and clinical factors, in hospitalized children of all ages, unlike acute and chronic malnutrition, in this study. Pediatric screening tools mainly PYMS did not have high sensitivity to detect chronic malnutrition and low MUAC SDS, in hospitalized children. Therefore, the tools have to be used along with z scores of anthropometric parameters.