ArticleFrontiers in cardiovascular medicine2023
A novel nomogram for predicting risk of malnutrition in patients with heart failure.
Article in Frontiers in cardiovascular medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 6 citations in OpenAlex.
- Establishment and validation of a machine learning model to stratify malnutrition risk in hospitalized older patients with chronic heart failure.Asia Pacific journal of clinical nutrition · 2026Article
- Development and validation of a machine learning-based model for diagnosing perioperative malnutrition in older adults with hip fracture.Frontiers in nutrition · 2026Article
- Development and Validation of a Risk-Prediction Nomogram for Nutritional Risk in Non-Dialysis Chronic Renal Failure Patients.Risk management and healthcare policy · 2026Article
- Relationship of malnutrition and frailty on prolonged stay at the hospital in heart failure patients.ESC heart failure · 2025Article
- A nomogram for predicting the risk of malnutrition in hospitalized older adults: a retrospective study.BMC geriatrics · 2025Article
- Factors Influencing Malnutrition in Patients with Heart Failure: A Scoping Review Based on the Biopsychosocial Model.Risk management and healthcare policy · 2025Review
- The diagnostic value investigation of programmed cell death genes in heart failure.BMC cardiovascular disorders · 2024Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and aims: This study aimed to explore the risk factors of malnutrition in patients with heart failure and construct a novel nomogram model. Methods and results: A cross-sectional study based on the STROBE checklist. Patients with heart failure from July 2020 to August 2021 were included. Patients were divided into a malnutrition group and a normal nutrition group based on the Society's recommended AND-ASPEN standard. Logistic regression was used to analyze the independent risk factors for malnutrition. A new prediction model of nomogram was constructed based on the risk factors, and its fit and prediction performance were evaluated. Of 433 patients, 66 (15.2%) had malnutrition and 367 (84.8%) had normal nutrition, Logistic regression analyses showed that the risk factors for malnutrition were total protein, hemoglobin, triglyceride, and glucose levels. The regression model based on the above four variables showed an area under the curve of 0.858. The novel nomogram model had a sensitivity of 78.5% and a specificity of 77.3%. After 2000 bootstrap resampling iterations, AUC was 0.852. Conclusions: The novel nomogram model can predict the odds of malnutrition in patients with heart failure at the early stage of admission, and can provide a reference for nursing staff to optimize nutritional care for inpatient with heart failure and to develop a discharge nutritional care plan.
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