ArticleBMC geriatrics2025
Developing a comprehensive malnutrition prediction model for the elderly in nursing homes.
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 1 paper, 1 of them a synthesis that pooled it.
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Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Risk prediction model for malnutrition in older adults: a systematic review.BMC geriatrics · 2026Pooled it
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
purposeMalnutrition among elderly nursing home residents represents a critical public health challenge, particularly in rapidly aging societies such as China. This study aimed to develop and validate a predictive model for malnutrition risk tailored to this vulnerable population.
methodsWe analyzed clinical data from 1,023 elderly individuals (aged ≥ 65 years) across 26: nursing homes in Wuhan, China (March-October 2023). Participants were randomly divided into model-building (70%, n = 716) and internal validation cohorts (30%, n = 307). LASSO regression and logistic regression identified key predictors, and a nomogram was constructed. Model performance was assessed via AUC, calibration curves, and decision curve analysis (DCA).
resultsThe malnutrition incidence was 46.37%. Five predictors were significant: feeding method (OR = 2.89, 95% CI: 1.75-4.76), dental status (OR = 0.56, 95% CI: 0.37-0.86), physical inactivity (OR = 1.75, 95% CI: 1.09-2.80), Barthel Index (OR = 0.96 per 10-point decrease), and anemia (OR = 1.91, 95% CI: 1.10-3.30). The model showed excellent discrimination (AUC = 0.90, 95% CI: 0.85-0.94) and calibration (mean absolute error = 0.026). DCA indicated clinical utility across threshold probabilities (2-97%).
conclusionThis nomogram provides a robust tool for malnutrition risk stratification in nursing homes. Future studies should validate its generalizability across diverse populations and regions.
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