W L Ng1, P F Collins2, D F Hickling3, J J Bell4. 1. Nutrition and Dietetics, School of Exercise and Nutrition Sciences, Faculty of Health, QUT, Brisbane, Queensland 4059, Australia; Department of Dietetics, Sengkang General Hospital, Singapore Health Services, Singapore 544886, Singapore. 2. Nutrition and Dietetics, School of Exercise and Nutrition Sciences, Faculty of Health, QUT, Brisbane, Queensland 4059, Australia; Department of Nutrition and Dietetics, Princess Alexandra Hospital, Brisbane, Queensland 4102, Australia. 3. Nutrition and Dietetics Department, The Prince Charles Hospital, Brisbane, Chermside, Queensland 4032, Australia. 4. Nutrition and Dietetics Department, The Prince Charles Hospital, Brisbane, Chermside, Queensland 4032, Australia; School of Human Movement and Nutrition Sciences, The University of Queensland, St Lucia, Queensland 4072, Australia. Electronic address: Jack.Bell@health.qld.gov.au.
Abstract
BACKGROUND: Nutrition screening and assessment tools often include body mass index (BMI) as a component in identifying malnutrition risk. However, rising obesity levels will impact on the relevancy and applicability of BMI cut-off points which may require re-evaluation. This study aimed to explore the relationship between commonly applied BMI cut-offs and diagnosed malnutrition. METHODS: Data (age, gender, BMI and Subjective Global Assessment (SGA) ratings) were analysed for 1152 inpatients aged ≥65 years across annual malnutrition audits (2011-2015). The receiver operation characteristic (ROC) curve analysed the optimal BMI cut-off for malnutrition and concurrent validity of commonly applied BMI cut-offs in nutritional screening and assessment tools. RESULTS: Malnutrition prevalence was 36.0% (n = 372) using SGA criteria (not malnourished, moderate or severe malnutrition). Median age was 78.7 (IQR 72-85) years, median BMI 25.4 (IQR 21.8-29.7) kg/m2; 52.1% male and 51.2% overweight/obese. ROC analysis identified an optimal BMI cut-off of <26 kg/m2, 80.8% sensitivity and 61.5% specificity (AUC 0.802, 95% CI 0.773, 0.830; p < 0.0001). Commonly applied BMI cut-offs (between 18.5 and 23 kg/m2) failed to meet the alpha-priori requirement of 80% sensitivity and 60% specificity. However, BMI <23 kg/m2 had the highest agreement (κ = 0.458) with malnutrition diagnosed using the SGA. CONCLUSIONS: Both malnutrition and overweight/obesity are common in older inpatients. Continuing increases in the prevalence of overweight and obesity will impact on the sensitivity of BMI as a screening component for malnutrition risk. The current study suggests tools developed over a decade ago may need to be revisited in future.
BACKGROUND: Nutrition screening and assessment tools often include body mass index (BMI) as a component in identifying malnutrition risk. However, rising obesity levels will impact on the relevancy and applicability of BMI cut-off points which may require re-evaluation. This study aimed to explore the relationship between commonly applied BMI cut-offs and diagnosed malnutrition. METHODS: Data (age, gender, BMI and Subjective Global Assessment (SGA) ratings) were analysed for 1152 inpatients aged ≥65 years across annual malnutrition audits (2011-2015). The receiver operation characteristic (ROC) curve analysed the optimal BMI cut-off for malnutrition and concurrent validity of commonly applied BMI cut-offs in nutritional screening and assessment tools. RESULTS:Malnutrition prevalence was 36.0% (n = 372) using SGA criteria (not malnourished, moderate or severe malnutrition). Median age was 78.7 (IQR 72-85) years, median BMI 25.4 (IQR 21.8-29.7) kg/m2; 52.1% male and 51.2% overweight/obese. ROC analysis identified an optimal BMI cut-off of <26 kg/m2, 80.8% sensitivity and 61.5% specificity (AUC 0.802, 95% CI 0.773, 0.830; p < 0.0001). Commonly applied BMI cut-offs (between 18.5 and 23 kg/m2) failed to meet the alpha-priori requirement of 80% sensitivity and 60% specificity. However, BMI <23 kg/m2 had the highest agreement (κ = 0.458) with malnutrition diagnosed using the SGA. CONCLUSIONS: Both malnutrition and overweight/obesity are common in older inpatients. Continuing increases in the prevalence of overweight and obesity will impact on the sensitivity of BMI as a screening component for malnutrition risk. The current study suggests tools developed over a decade ago may need to be revisited in future.
Authors: Per Elgestad Stjernfeldt; Gerd Faxén Irving; Inger Wårdh; Robert Lundqvist; Angelika Lantto Journal: Int J Environ Res Public Health Date: 2022-05-10 Impact factor: 4.614
Authors: Iris M Y van Vliet; Antonio W Gomes-Neto; Margriet F C de Jong; Stephan J L Bakker; Harriët Jager-Wittenaar; Gerjan J Navis Journal: Eur J Clin Nutr Date: 2021-02-15 Impact factor: 4.884