ArticleBMC geriatrics2025
A nomogram for predicting the risk of malnutrition in hospitalized older adults: a retrospective study.
Article in BMC geriatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 2 of them syntheses that pooled it.
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Who cites it
3 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Risk prediction models for malnutrition in dialysis patients in China: a systematic review and meta-analysis.Renal failure · 2026Pooled it
- Risk prediction model for malnutrition in older adults: a systematic review.BMC geriatrics · 2026Pooled it
- Machine learning model for predicting malnutrition risk in lung cancer patients after thoracoscopic resection: a multi-center study.Frontiers in oncology · 2026Article
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Abstract
backgroundMalnutrition is highly prevalent but under-recognized in hospitalized older adults, which is closely related to increased risk of adverse clinical outcomes and mortality. It is crucial to identify high-risk individuals at an early stage and manage them promptly. This study aimed to explore the predictive factors and develop a nomogram model for predicting the risk of malnutrition in hospitalized elderly patients.
methodsWe conducted a retrospective study of data collected from 456 older individuals admitted to geriatric wards from four hospitals in China between August 2020 and December 2020 (136 in the malnutrition group and 320 in the non-malnutrition group). Least Absolute Selection and Shrinkage Operator (LASSO) regression and stepwise multivariate logistic regression were applied to screen predictors and create a nomogram. The predictive performance of the model was assessed by receiver operating characteristic (ROC) curve, concordance index (C-index) and calibration curve. The clinical utility was estimated by decision curve analysis (DCA). Youden's Index was used to identify the optimal cut-point of the nomogram.
resultsFour independent predictive factors were utilized to construct the nomogram model after being selected by LASSO regression and multivariate logistic regression, namely body mass index (BMI), heart failure, frailty and hemoglobin. C-index of the model was 0.906 (95% CI: 0.872-0.939) and the area under the curve (AUC) was 0.906. The optimal cut-point of the nomogram was 82.74 with a sensitivity of 78.7% and specificity of 92.2% (Youden's index: 0.709). The calibration curve demonstrated a high degree of consistency between predicted probability and actual observation. The DCA indicated a favorable clinical benefit for the nomogram.
conclusionsWe have established a multi-dimensional nomogram model to predict the risk of malnutrition in Chinese hospitalized older adults. The model yields favorable predictive performance and clinical utility, which provides an effective approach for rapid identification of high-risk malnourished older individuals in clinical practice.
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