Abstract

BACKGROUND: Older adults (≥ 65 years) account for a disproportionately high proportion of hospitalization and in-hospital mortality, some of which may be avoidable. Although machine learning (ML) models have already been built and validated for predicting hospitalization and mortality, there remains a significant need to optimise ML models further. Accurately predicting hospitalization may tremendously impact the clinical care of older adults as preventative measures can be implemented to improve clinical outcomes for the patient.

METHODS: In this retrospective cohort study, a dataset of 14,198 community-dwelling older adults (≥ 65 years) with complex care needs from the Inter-Resident Assessment Instrument database was used to develop and optimise three ML models to predict 30-day-hospitalization. The models developed and optimized were Random Forest (RF), XGBoost (XGB), and Logistic Regression (LR). Variable importance plots were generated for all three models to identify key predictors of 30-day-hospitalization.

RESULTS: The area under the receiver operating characteristics curve for the RF, XGB and LR models were 0.97, 0.90 and 0.72, respectively. Variable importance plots identified the Drug Burden Index and alcohol consumption as important, immediately potentially modifiable variables in predicting 30-day-hospitalization.

CONCLUSIONS: Identifying immediately potentially modifiable risk factors such as the Drug Burden Index and alcohol consumption is of high clinical relevance. If clinicians can influence these variables, they could proactively lower the risk of 30-day-hospitalization. ML holds promise to improve the clinical care of older adults. It is crucial that these models undergo extensive validation through large-scale clinical studies before being utilized in the clinical setting.

Original languageEnglish
JournalJournal of Gerontology: series A - Medical Sciences
Early online date11 May 2024
DOIs
Publication statusE-pub ahead of print - 11 May 2024

Bibliographical note

© The Author(s) 2024. Published by Oxford University Press on behalf of The Gerontological Society of America.

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