ORIGINAL RESEARCH article
Front. Nutr.
Sec. Clinical Nutrition
Volume 12 - 2025 | doi: 10.3389/fnut.2025.1551483
A predictive model based on the GLIM diagnosis for malnutrition in elderly heart failure patients
Provisionally accepted
- Wuxi People’s Hospital Affiliated to Nanjing Medical University, Wuxi, China
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Background&aims：Malnutrition is closely associated with adverse clinical outcomes in elderly heart failure (HF) patients. Currently, there is a distinct absence of specific diagnostic tools to identify malnutrition within this particular population.Therefore, this study aims to analyze the factors influencing malnutrition in elderly HF patients based on the Global Leadership Initiative on Malnutrition (GLIM) criteria, with the goal of developing a rapid and accurate diagnostic method to identify malnutrition.The research incorporated a primary cohort study of 163 HF patients aged 65 and above and a validation cohort of 69 patients. The nutritional status of these patients was assessed according to the GLIM criteria. Logistic regression analysis was conducted to determine the independent risk factors of malnutrition.Subsequently, a nomogram model was developed and validated.Results: According to the GLIM criteria, 54 patients (33.1%) and 22 patients (32.4%) in two patient cohorts were suffering from malnutrition. The logistic analyses revealed that body mass index (BMI), grip strength, mid-upper arm circumference (MUAC), fat-free mass (FFM), and albumin independently serve as risk factors of malnutrition in elderly HF patients. The nomogram model demonstrates excellent discriminative ability, with an area under the curve (AUC) of 0.921 (95% CI: 0.881-0.962). While the AUC of validation cohort is 0.899 (95% CI: 0.827-0.972).In elderly HF patients, BMI, grip strength, FFM, MUAC and albumin are identified as independent risk factors for malnutrition. The constructed nomogram based on these factors can accurately predict malnutrition and holds significant practical value.
Keywords: ：Heart failure, GLIM, Malnutrition, predictive model, nomogram
Received: 04 Jan 2025; Accepted: 16 May 2025.
Copyright: © 2025 Tang, Zhang, Xiao, Yang, Zhu and Gao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Xinyu Tang, Wuxi People’s Hospital Affiliated to Nanjing Medical University, Wuxi, China
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