입원 환자 영양검색 지표 개발

• Published in: Korean journal of community nutrition Vol. 11; no. 6; pp. 779 - 784
• Main Authors: 김수안(Su An Kim), 김소연(So Yeon Kim), 손정민(Cheong Min Sohn)
• Format: Journal Article
• Language: Korean
• Published: 대한지역사회영양학회 2006
• Subjects: malnutrition; Nutrition screening index; subjective global assessment
• ISSN: 1226-0983; 2287-1624; 2951-3146

Several studies about hospital malnutrition have been reported that about more than 40% of hospitalized patients are having nutritional risk factors and hospital malnutrition presents a high prevalence. People in a more severe nutritional status ended up with a longer length of hospital stay and higher hospital cost. Nutrition screening tools identify individuals who are malnourished or at risk of becoming malnourished and who may benefit from nutritional support. For the early detection and treatment of malnourished hospital patients , few valid screening instruments fur Koreans exist. Therefore, the aim of this study was to develop a simple, reliable and valid malnutrition screening tool that could be used at hospital admission to identify adult patients at risk of malnutrition using medical electrical record data. Two hundred and one patients of the university affiliated medical center were assessed on nutritional status and classified as well nourished, moderately or severely malnourished by a Patient-Generated subjective global assessment (PG-SGA) being chosen as the 'gold standard' for defining malnutrition. The combination of nutrition screening questions with the highest sensitivity and specificity at prediction PG-SGA was termed the nutrition screening index (NSI). Odd ratio, and binary logistic regression were used to predict the best nutritional status predictors. Based on regression coefficient score, albumin less than 3.5 g/dl, body mass index (BMI) less than $18.5kg/m^2$, total lymphocyte count less than 900 and age over 65 were determined as the best set of NSI. By using best nutritional predictors receiver operating characteristic curve with the area under the curve, sensitivity and 1-specificity were analyzed to determine the best optimal cut-off point to decide normal or abnormal in nutritional status. Therefore simple and beneficial NSI was developed for identifying patients with severe malnutrition. Using NSI, nutritional information of the severe malnutrition patient should be shared with physicians and they should be cared for by clinical dietitians to improve their nutritional status.