Identifying Nutritional Risk in Hospitalized Children: Using Anthropometry, Comparing with Laboratory and Nutritional Screening Tools

Introduction: Malnutrition in children is one of the most serious health problems in the world. Especially, hospitalized children are under the risk of malnutrition. It is recommended that all children admitted to hospital should be assessed by anthropometry and determined their nutritional status. We aimed to identify the nutritional risk in hospitalized children by using anthropometric parameters, comparing with laboratory and STRONGkids and PYMS.

Methods: 503 patients, aged 1 month-18 years, were enrolled. Anthropometric measurements were taken and converted to z-scores. STRONGkids and PYMS tools were completed for all patients. Age, race, department for admission, disease, underlying disease, laboratory values were taken. When discharged, weight measurement was repeated.

Results: 225(%44.7) were female, 278(%55.3) were male. Age distribution was 101(%20.1) for 0-2 years, 102(%20.3) for 2-5 years, 138(%27.4) for 5-10 years and 162(%32.2) for 10-18 years. The average length of hospital stay was 9.1 days. Acute malnutrition using W/H z-score was %14.8, W/A z-score was %10.6, BMI/A z-score was %15.5. Chronic malnutrition was %9.4. When compared antropometric parameters in admission and discharge, there was no statistically significant difference. Patients with chronic malnutrition have lower serum albumin and hemoglobin levels. In general, STRONGkids tool was successful in identifying patients with z-score >-2, but patients with z-score +2, but failed in patients with z-score ≥-3 and <-2, in fact the tool overestimated this group.

Conclusion: Hospital stay does not affect the nutritional status adversely. Actual nutritional status isn’t a predictor for hospital acquired malnutrition. Children with underlying disease are more prone to developing malnutrition, attention should be paid to these children. Laboratory tests aren’t good predictors. In this study, we confirmed STRONGkids underestimate high nutritional risk patients. Instead, PYMS tool was successful in identifying high risk patients. In screening patients with high nutritional risk, we suggest using PYMS tool with anthropometric measurements.