Which Screening Tool Performs Best in Identifying Malnutrition Risk Among Hospitalized Older Adults With Cardiovascular Disease? a Diagnostic Accuracy Study Comparing Six Different Screening Tools With Glim Criteria

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2025

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Springer

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Purpose This study aimed to compare the performance of six different screening tools with the GLIM criteria in identifying malnutrition risk among older adults with cardiovascular diseases. Methods In this diagnostic accuracy study, data from 669 patients aged 65 and older, hospitalized at Band & imath;rma Training and Research Hospital diagnosed with cardiovascular disease, were evaluated. The data were obtained using a questionnaire that included a Demographic Information Form, Global Leadership Initiative on Malnutrition (GLIM) Criteria, Malnutrition Screening Tool (MST), Malnutrition Universal Screening Tool (MUST), Short Nutritional Assessment Questionnaire (SNAQ), Mini Nutritional Assessment-Short Form (MNA-SF), Nutritional Risk Screening-2002 (NRS-2002), and Graz Malnutrition Screening (GMS) tool. GLIM criteria were used as gold standard and six screening tools were used as index tests. Receiver Operating Characteristic (ROC) curve analysis was conducted to calculate the Area Under the Curve (AUC), and the sensitivity, specificity, accuracy, and Cohen's Kappa (kappa) coefficient of the index tests were assessed. Results The mean age of the patients was 75.5 +/- 7.7 years, 55.2% were female. According to the GLIM criteria, the prevalence of malnutrition was calculated as 22.0%. The AUC calculated by ROC indicated that MST (AUC: 0.905) had excellent predictive value, while MUST (AUC: 0.874), SNAQ (AUC: 0.851), MNA-SF (AUC: 0.842), and GMS (AUC: 0.820) demonstrated good predictive value. Among the screening tools, GMS had the highest sensitivity (92.5%), whereas MNA-SF exhibited the highest specificity (91.6%). MNA-SF also demonstrated the highest agreement with the GLIM criteria (Cohen's kappa: 66.8) and the highest accuracy (88.3%). Conclusion This study found that the MNA-SF tool outperformed other comprehensive screening tools when evaluating malnutrition risk in light of the GLIM criteria.

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Durmaz, Duygu/0000-0001-9617-8046; , Ayca Aydin/0000-0001-9750-5678; Arslan, Sedat/0000-0002-3356-7332; Tari Selcuk, Kevser/0000-0003-1766-4914

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Malnutrition, Glim Criteria, Nutritional Screening Tools, Cardiovascular Disease

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