Optimizing a risk algorithm to improve malnutrition detection in the older adult population
Abstract
Background: Malnutrition is a frequently underrecognized condition that contributes to adverse clinical outcomes, including increased morbidity, mortality, higher length of stay in hospitals, and difficult recovery. It is known to be a multi-factorial condition, and the Global Leadership Initiative on Malnutrition (GLIM) has established a recent definition of malnutrition to systematically integrate both phenotypic indicators and underlying etiologic factors.
Methods: This work conceptually examines the need for a structured alarm system for malnutrition based on established risk factors combined with a strong, standardized definition. Core components include the integration of phenotypic criteria from the GLIMM present in the interRAI assessment instruments (e.g., unintentional weight loss and low body mass index), combined with key risk factors such as advanced age, adapted feeding mode, functional and cognitive decline, based on previous work.
Results: Retrospective data from a total of 2300 older people in the community (median age 82.0, 64.4% female) and 2942 residents of Flemish nursing homes (88.0, 70.0% female) were analysed. Approximately 8.7% of the population at home showed a high risk of malnutrition, presenting low BMI and an adapted mode of nutritional intake, mostly due to dysphagia. In the nursing home population, this risk was higher, 12.3% (p<.001).
Conclusions: The implementation of a standardized, risk-based alarm system for malnutrition has the potential to transform current practices from reactive to proactive care. Anchored in the GLIMM robust definition for risk assessment, this strategy can improve early detection, optimize intervention timing, and ultimately lead to better patient outcomes and more efficient use of healthcare resources.
Key messages: • Malnutrition is a multifactorial condition that typically develops gradually, often resulting in a persistent chronic state.
• Targeted strategies and multidisciplinary approaches are necessary to manage and mitigate the risk of malnutrition.
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BibTeX
@article{mello2026optimizing,
title = {{Optimizing a risk algorithm to improve malnutrition detection in the older adult population}},
author = {JM De Almeida Mello and J Duyck and H Ruan and P Vandenbulcke},
journal = {The European Journal of Public Health},
year = {2026},
month = oct,
doi = {10.1093/eurpub/ckag169.1453},
url = {https://doi.org/10.1093/eurpub/ckag169.1453},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - Optimizing a risk algorithm to improve malnutrition detection in the older adult population
AU - JM De Almeida Mello
AU - J Duyck
AU - H Ruan
AU - P Vandenbulcke
PY - 2026
DA - 2026/10/01
JO - The European Journal of Public Health
DO - 10.1093/eurpub/ckag169.1453
UR - https://doi.org/10.1093/eurpub/ckag169.1453
AB - Background: Malnutrition is a frequently underrecognized condition that contributes to adverse clinical outcomes, including increased morbidity, mortality, higher length of stay in hospitals, and difficult recovery. It is known to be a multi-factorial condition, and the Global Leadership Initiative on Malnutrition (GLIM) has established a recent definition of malnutrition to systematically integrate both phenotypic indicators and underlying etiologic factors. Methods: This work conceptually examines the need for a structured alarm system for malnutrition based on established risk factors combined with a strong, standardized definition. Core components include the integration of phenotypic criteria from the GLIMM present in the interRAI assessment instruments (e.g., unintentional weight loss and low body mass index), combined with key risk factors such as advanced age, adapted feeding mode, functional and cognitive decline, based on previous work. Results: Retrospective data from a total of 2300 older people in the community (median age 82.0, 64.4% female) and 2942 residents of Flemish nursing homes (88.0, 70.0% female) were analysed. Approximately 8.7% of the population at home showed a high risk of malnutrition, presenting low BMI and an adapted mode of nutritional intake, mostly due to dysphagia. In the nursing home population, this risk was higher, 12.3% (p<.001). Conclusions: The implementation of a standardized, risk-based alarm system for malnutrition has the potential to transform current practices from reactive to proactive care. Anchored in the GLIMM robust definition for risk assessment, this strategy can improve early detection, optimize intervention timing, and ultimately lead to better patient outcomes and more efficient use of healthcare resources. Key messages: • Malnutrition is a multifactorial condition that typically develops gradually, often resulting in a persistent chronic state. • Targeted strategies and multidisciplinary approaches are necessary to manage and mitigate the risk of malnutrition.
SN - 1101-1262
SN - 1464-360X
ER -