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The malnutrition screening tool versus objective measures to detect malnutrition in hip fracture
1Department of Nutrition and Dietetics, The Prince Charles Hospital, Queensland Health, Brisbane, Queensland, Australia; Centre for Dietetics Research, School of Human Movement Studies, University of Queensland, Brisbane, Queensland, Australia.
Background:
The Malnutrition Screening Tool (MST) is the most commonly used screening tool in Australia. Poor screening tool sensitivity may lead to an under-diagnosis of malnutrition, with potential patient and economic ramifications. The present study aimed to determine whether the MST or anthropometric parameters adequately detect malnutrition in patients who were admitted to a hip fracture unit.
Methods:
Data were analysed for a prospective convenience sample (n = 100). MST screening was independently undertaken by nursing staff and a nutrition assistant. Mid upper arm circumference (MUAC) was measured by a trained nutrition assistant. Nutritional risk [MST score ≥ 2, body mass index (BMI) < 22 kg m(-2) , or MUAC < 25 cm] was compared with malnutrition diagnosed by accredited practicing dietitians using International Classification of Diseases version 10-Australian Modification (ICD10-AM) coding criteria.
Results:
Malnutrition prevalence was 37.5% using ICD10-AM criteria. Delirium, dementia or preadmission cognitive impairment was present in 65% of patients. The BMI as a nutrition risk screen was the most valid predictor of malnutrition (sensitivity 75%; specificity 93%; positive predictive value 73%; negative predictive value 84%). Nursing MST screening was the least valid (sensitivity 73%; specificity 55%; positive predictive value 50%; negative predictive value 77%). There was only fair agreement between nursing and nutrition assistant screening using the MST (κ = 0.28).
Conclusions:
In this population with a high prevalence of delirium and dementia, further investigation is warranted into the performance of nutrition screening tools and anthropometric parameters such as BMI. All tools failed to predict a considerable number of patients with malnutrition. This may result in the under-diagnosis and treatment of malnutrition, leading to case-mix funding losses.
