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. 2022 Sep 26;7(5):105.
doi: 10.3390/geriatrics7050105.

Key Factors and AI-Based Risk Prediction of Malnutrition in Hospitalized Older Women

Nekane Larburu  1   2 , Garazi Artola  1   2 , Jon Kerexeta  1   2 , Maria Caballero  3 , Borja Ollo  3 , Catherine M Lando  3
Affiliations

Key Factors and AI-Based Risk Prediction of Malnutrition in Hospitalized Older Women

Nekane Larburu et al. Geriatrics (Basel). .

Abstract

The numerous consequences caused by malnutrition in hospitalized patients can worsen their quality of life. The aim of this study was to evaluate the prevalence of malnutrition on the elderly population, especially focusing on women, identify key factors and develop a malnutrition risk predictive model. The study group consisted of 493 older women admitted to the Asunción Klinika Hospital in the Basque Region (Spain). For this purpose, demographic, clinical, laboratory, and admission information was gathered. Correlations and multivariate analyses and the MNA-SF screening test-based risk of malnutrition were performed. Additionally, different predictive models designed using this information were compared. The estimated frequency of malnutrition among this population in the Basque Region (Spain) is 13.8%, while 41.8% is considered at risk of malnutrition, which is increased in women, with up to 16.4% with malnutrition and 47.5% at risk of malnutrition. Sixteen variables were used to develop a predictive model obtaining Area Under the Curve (AUC) values of 0.76. Elderly women assisted at home and with high scores of dependency were identified as a risk group, as well as patients admitted in internal medicine units, and in admissions with high severity.

Keywords: hospitalized patients; malnutrition; older adults; predictive model.

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Conflict of interest statement

All authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
(a) Accuracy of the different amounts of variables by repeated cross-validation for RFE; and (b) feature list sorted by relevance.
Figure 2
Figure 2
Diagram of the logic used for the prediction of risk of malnutrition.
See this image and copyright information in PMC

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