Nutritional assessment: comparison of clinical assessment and objective variables for the prediction of length of hospital stay and readmission

Background: Nutritional assessment commonly includes multiple nutrition indicators (NIs). To promote efficiency, a minimum set is needed for the diagnosis of malnutrition in the acute care setting. Objective: The objective was to compare the ability of different NIs to predict outcomes of length of hospital stay and readmission to refine the detection of malnutrition in acute care. Design: This was a prospective cohort study of 1022 patients recruited from 18 acute care hospitals (academic and community), from 8 provinces across Canada, between 1 July 2010 and 28 February 2013. Participants were patients aged >= 18 y admitted to medical and surgical wards. NIs measured at admission were subjective global assessment (SGA; SGA A = well nourished, SGA B mild or moderate malnutrition, and SGA C = severe malnutrition), Nutrition Risk Screening (2002), body weight, midarm and calf circumference, serum albumin, handgrip strength (HGS), and patient-self assessment of food intake. Logistic regression determined the independent effect of NIs on the outcomes of length of hospital stay (<7 d and >= 7d) and readmission within 30 d after discharge. Results: In total, 733 patients had complete NI data and were available for analysis. After we controlled for age, sex, and diagnosis, only SGA C (OR: 2.19; 95% CI: 1.28, 3.75), HGS (OR: 0.98; 95% CI: 0.96, 0.99 per kg of increase), and reduced food intake during the first week of hospitalization (OR: 1.51; 95% CI: 1.08, 2.11) were independent predictors of length of stay. SGA C (OR: 2.12; 95% CI: 1.24, 3.93) and HGS (OR: 0.96; 95% CI: 0.94, 0.98) but not food intake were independent predictors of 30-d readmission. Conclusions: SGA, HGS, and food intake were independent predictors of outcomes for malnutrition. Because food intake in this study was judged days after admission and HGS has a wide range of normal values, SGA is the single best predictor and should be advocated as the primary measure for diagnosis of malnutrition.