Priya Dewansingh1, Margreet Euwes2, Wim P Krijnen3, Jaap H Strijbos4, Cees P van der Schans5, Harriët Jager-Wittenaar6. 1. Research Group Healthy Ageing, Allied Health Care and Nursing, Hanze University of Applied Sciences, Groningen, The Netherlands. Electronic address: p.dewansingh@pl.hanze.nl. 2. Acute Care Rehabilitation, Nij Smellinghe Hospital, Drachten, the Netherlands. 3. Research Group Healthy Ageing, Allied Health Care and Nursing, Hanze University of Applied Sciences, Groningen, The Netherlands; Faculty of Mathematics and Natural Sciences, University of Groningen, Groningen, the Netherlands. 4. Department of Lung Diseases, Nij Smellinghe Hospital, Drachten, the Netherlands. 5. Research Group Healthy Ageing, Allied Health Care and Nursing, Hanze University of Applied Sciences, Groningen, The Netherlands; Faculty of Medical Sciences, University Medical Center Groningen, Groningen, the Netherlands; Department of Rehabilitation Medicine, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Department of Health Psychology Research, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands. 6. Research Group Healthy Ageing, Allied Health Care and Nursing, Hanze University of Applied Sciences, Groningen, The Netherlands; Department of Oral and Maxillofacial Surgery, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
Abstract
OBJECTIVE: Malnutrition screening instruments used in hospitals mainly include criteria to identify characteristics of malnutrition. However, to tackle malnutrition in an early stage, identifying risk factors for malnutrition in addition to characteristics may be valuable. The aim of this study was to determine the predictive validity of the Patient-Generated Subjective Global Assessment (PG-SGA SF), which addresses malnutrition characteristics and risk factors, and the Short Nutritional Assessment Questionnaire (SNAQ), which addresses mainly malnutrition characteristics, for length of stay (LOS) in a mixed hospital population. METHODS: Patients (N = 443) were screened with the PG-SGA SF and SNAQ in the first 72 h after admission to the lung, cardiology, or surgery ward. The McNemar-Bowker test was used to investigate the symmetry between the SNAQ and PG-SGA SF categorization for low, medium, and high risk. The predictive value of the PG-SGA SF and SNAQ was assessed by γ-regression before and after adjusting for several confounders. RESULTS: Of the 443 patients included, 23% and 58% were categorized as being at medium/high risk for malnutrition according to the SNAQ and PG-SGA SF, respectively. The regression analysis indicated that LOS of high-risk patients according to PG-SGA SF was 36% longer than that of low-risk patients (P = 0.001). LOS in patients at high risk according to the SNAQ did not significantly differ from that of SNAQ low-risk patients. CONCLUSIONS: The PG-SGA SF, as a proactive malnutrition screening instrument, predicts LOS in various hospital wards, whereas the SNAQ, as a reactive instrument, does not. Therefore, we recommend the PG-SGA SF for proactive screening for malnutrition risk.
OBJECTIVE: Malnutrition screening instruments used in hospitals mainly include criteria to identify characteristics of malnutrition. However, to tackle malnutrition in an early stage, identifying risk factors for malnutrition in addition to characteristics may be valuable. The aim of this study was to determine the predictive validity of the Patient-Generated Subjective Global Assessment (PG-SGA SF), which addresses malnutrition characteristics and risk factors, and the Short Nutritional Assessment Questionnaire (SNAQ), which addresses mainly malnutrition characteristics, for length of stay (LOS) in a mixed hospital population. METHODS: Patients (N = 443) were screened with the PG-SGA SF and SNAQ in the first 72 h after admission to the lung, cardiology, or surgery ward. The McNemar-Bowker test was used to investigate the symmetry between the SNAQ and PG-SGA SF categorization for low, medium, and high risk. The predictive value of the PG-SGA SF and SNAQ was assessed by γ-regression before and after adjusting for several confounders. RESULTS: Of the 443 patients included, 23% and 58% were categorized as being at medium/high risk for malnutrition according to the SNAQ and PG-SGA SF, respectively. The regression analysis indicated that LOS of high-risk patients according to PG-SGA SF was 36% longer than that of low-risk patients (P = 0.001). LOS in patients at high risk according to the SNAQ did not significantly differ from that of SNAQ low-risk patients. CONCLUSIONS: The PG-SGA SF, as a proactive malnutrition screening instrument, predicts LOS in various hospital wards, whereas the SNAQ, as a reactive instrument, does not. Therefore, we recommend the PG-SGA SF for proactive screening for malnutrition risk.