4.2 Article

The Malnutrition Screening Tool versus objective measures to detect malnutrition in hip fracture

Journal

JOURNAL OF HUMAN NUTRITION AND DIETETICS
Volume 26, Issue 6, Pages 519-526

Publisher

WILEY-BLACKWELL
DOI: 10.1111/jhn.12040

Keywords

body mass index; elderly; hip fracture; malnutrition; nutrition screening

Funding

  1. Prince Charles Hospital Foundation

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BackgroundThe 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. MethodsData 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 score2, body mass index (BMI)<22kgm(-2), or MUAC<25cm] was compared with malnutrition diagnosed by accredited practicing dietitians using International Classification of Diseases version 10-Australian Modification (ICD10-AM) coding criteria. ResultsMalnutrition 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 (=0.28). ConclusionsIn 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.

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