ArticleGeriatrics & gerontology international2025
Predicting admission for fall-related injuries in older adults using artificial intelligence: A proof-of-concept study.
Article in Geriatrics & gerontology international, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Predicting admission for fall-related injuries in older adults using artificial intelligence: A proof-of-concept study.Geriatrics & gerontology international · 2025Article
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5 authors.
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Abstract
aimPre-injury frailty has been investigated as a tool to predict outcomes of older trauma patients. Using artificial intelligence principles of machine learning, we aimed to identify a "signature" (combination of clinical variables) that could predict which older adults are at risk of fall-related hospital admission. We hypothesized that frailty, measured using the 5-item modified Frailty Index, could be utilized in combination with other factors as a predictor of admission for fall-related injuries.
methodsThe National Readmission Database was mined to identify factors associated with admission of older adults for fall-related injuries. Older adults admitted for trauma-related injuries from 2010 to 2014 were included. Age, sex, number of chronic conditions and past fall-related admission, comorbidities, 5-item modified Frailty Index, and medical insurance status were included in the analysis. Two machine learning models were selected among six tested models (logistic regression and random forest). Using a decision tree as a surrogate model for random forest, we extracted high-risk combinations of factors associated with admission for fall-related injury.
resultsOur approach yielded 18 models. Being a woman was one of the factors most often associated with admission for fall-related injuries. Frailty appeared in four of the 18 combinations. Being a woman, aged 65-74 years and presenting a 5-item modified Frailty Index score >3 predicted admission for fall-related injuries in 80.3% of this population.
conclusionUsing artificial intelligence principles of machine learning, we were able to develop 18 signatures allowing us to identify older adults at risk of admission for fall-related injuries. Future studies using other databases, such as TQIP, are warranted to validate our high-risk combination models. Geriatr Gerontol Int 2025; 25: 232-242.
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