Journal article
Predicting admission for fall-related injuries in older adults using artificial intelligence: A proof-of-concept study
Geriatrics & gerontology international, Vol.25(2), pp.232-242
02/2025
DOI: 10.1111/ggi.15066
PMCID: PMC11788240
PMID: 39800578
Appears in UI Libraries Support Open Access
Abstract
Pre-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.
The 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.
Our 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.
Using 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; ••: ••-••.
Details
- Title: Subtitle
- Predicting admission for fall-related injuries in older adults using artificial intelligence: A proof-of-concept study
- Creators
- Nam Le - Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, USAMilan Sonka - University of Iowa, Fraternal Order of Eagles Diabetes Research CenterDionne A Skeete - University of IowaKathleen S Romanowski - Shriners Hospitals for Children - Northern CaliforniaColette Galet - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Geriatrics & gerontology international, Vol.25(2), pp.232-242
- DOI
- 10.1111/ggi.15066
- PMID
- 39800578
- PMCID
- PMC11788240
- NLM abbreviation
- Geriatr Gerontol Int
- ISSN
- 1444-1586
- eISSN
- 1447-0594
- Publisher
- Wiley
- Grant note
- R49 CE002108-05 / NCIPC CDC HHS
- Language
- English
- Electronic publication date
- 01/12/2025
- Date published
- 02/2025
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Surgery; Radiation Oncology; Fraternal Order of Eagles Diabetes Research Center; Injury Prevention Research Center; Ophthalmology and Visual Sciences
- Record Identifier
- 9984774238402771
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