ArticleBMC geriatrics2023
A nomogram model for predicting malnutrition among older hospitalized patients with type 2 diabetes: a cross-sectional study in China.
Article in BMC geriatrics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.
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7 citing papers in PubMed, 2 syntheses or guidelines pooled it, 8 citations in OpenAlex.
- Risk prediction model for malnutrition in older adults: a systematic review.BMC geriatrics · 2026Pooled it
- Prevalence and risk factors of malnutrition in elderly patients with diabetes mellitus: a meta-analysis and systematic review.Frontiers in nutrition · 2026Pooled it
- Machine learning model for predicting malnutrition risk in lung cancer patients after thoracoscopic resection: a multi-center study.Frontiers in oncology · 2026Article
- Fibrinogen/albumin ratio is associated with first-ever cardiovascular events in patients with peritoneal dialysis.Annals of medicine · 2025Article
- Impact of geriatric nutritional risk index and diabetes mellitus on prognosis in ischaemic heart failure with reduced ejection fraction.Scientific reports · 2025Article
- Development and validation of a nomogram for predicting refractory peritoneal dialysis related peritonitis.Renal failure · 2024Article
- Association Between Pharmacological Treatment Regimens and Quality of Life Among Rural Type 2 Diabetic Patients: A Comparative Analysis in Eastern China.Inquiry : a journal of medical care organization, provision and financingArticle
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5 authors at 4 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundMalnutrition remains a pervasive issue among older adults, a prevalence that is markedly higher among those diagnosed with diabetes. The primary objective of this study was to develop and validate a risk prediction model that can accurately identify instances of malnutrition among elderly hospitalized patients with type 2 diabetes mellitus (T2DM) within a Chinese demographic.
methodsThis cross-sectional study was conducted between August 2021 and August 2022, we enrolled T2DM patients aged 65 years and above from endocrinology wards. The creation of a nomogram for predicting malnutrition was based on risk factors identified through univariate and multivariate logistic regression analyses. The predictive accuracy of the model was evaluated by the receiver operating characteristic curve (ROC),the area under the ROC (AUC), the concordance index (C-index), and calibration curves.
resultsThe study included a total of 248 older T2DM patients, with a recorded malnutrition prevalence of 26.21%. The identified critical risk factors for malnutrition in this cohort were body mass index, albumin, impairment in activities of daily living, dietary habits, and glycosylated hemoglobin. The AUC of the nomogram model reached 0.914 (95% CI: 0.877-0.951), with an optimal cutoff value of 0.392. The model demonstrated a sensitivity of 80.0% and a specificity of 88.5%. Bootstrap-based internal verification results revealed a C-index of 0.891, while the calibration curves indicated a strong correlation between the actual and predicted malnutrition risks.
conclusionsThis study underscores the critical need for early detection of malnutrition in older T2DM patients. The constructed nomogram represents a practical and reliable tool for the rapid identification of malnutrition among this vulnerable population.
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