Developing a comprehensive malnutrition prediction model for the elderly in nursing homes

Area of Science:

• Gerontology
• Public Health
• Nutritional Science

Background:

• Malnutrition is a significant health issue for elderly individuals in nursing homes, especially in aging populations like China's.
• Early identification and intervention are crucial for managing malnutrition and improving health outcomes in this demographic.

Purpose of the Study:

• To develop and validate a predictive model for identifying malnutrition risk among elderly nursing home residents in China.
• To create a user-friendly tool (nomogram) for clinical application in malnutrition risk stratification.

Main Methods:

• A cohort of 1,023 elderly residents (≥65 years) from 26 nursing homes in Wuhan, China, was analyzed.
• Data were split into model-building (70%) and internal validation (30%) sets.
• LASSO and logistic regression identified predictors; a nomogram was constructed and validated using AUC, calibration curves, and decision curve analysis (DCA).

Main Results:

• The incidence of malnutrition was 46.37%.
• Key predictors identified were feeding method, dental status, physical inactivity, Barthel Index, and anemia.
• The model demonstrated excellent predictive performance (AUC = 0.90) and clinical utility.

Conclusions:

• The developed nomogram is a reliable tool for stratifying malnutrition risk in nursing home residents.
• Further validation in diverse populations and regions is recommended to assess generalizability.