Author Identifier
Marc Sim: https://orcid.org/0000-0001-5166-0605
Abadi K. Gebre: https://orcid.org/0000-0002-1975-0085
Jack Dalla Via: https://orcid.org/0000-0002-1815-0838
Syed Zulqarnain Gilani: https://orcid.org/0000-0002-7448-2327
Zaid Ilyas: https://orcid.org/0000-0001-6072-2441
Cassandra Smith: https://orcid.org/0000-0002-2517-2824
David Suter: https://orcid.org/0000-0001-6306-3023
Joshua R. Lewis: https://orcid.org/0000-0003-1003-8443
Document Type
Journal Article
Publication Title
GeroScience
Publisher
Springer
School
Nutrition and Health Innovation Research Institute / School of Medical and Health Sciences / Centre for Artificial Intelligence and Machine Learning (CAIML) / School of Science
Publication Unique Identifier
10.1007/s11357-025-01589-7
RAS ID
78271
Funders
Rady Innovation Fund / Rady Faculty of Health Sciences / University of Manitoba / Medical Research Future Fund Cardiovascular Health Mission (2024225) / Royal Perth Hospital Research Foundation Fellowship / Western Australian Future Health Research and Innovation Fund / Raine Medical Research Foundation / National Heart Foundation Future Leader Fellowship (102817)
Abstract
Abdominal aortic calcification (AAC), a subclinical measure of cardiovascular disease (CVD) that can be assessed on vertebral fracture assessment (VFA) images during osteoporosis screening, is reported to be a falls risk factor. A limitation to incorporating AAC clinically is that its scoring requires trained experts and is time-consuming. We examined if our machine learning (ML) algorithm for AAC (ML-AAC24) is associated with a higher fall-associated hospitalisation risk in the Manitoba Bone Mineral Density (BMD) Registry. A total of 8565 individuals (94.0% female, age 75.7 ± 6.8 years) who had a BMD and VFA image from DXA between February 2010 and December 2017 were included. ML-AAC24 was categorised based on established categories (ML-AAC24 = low < 2; moderate 2 to < 6; high ≥ 6). Cox proportional hazards models assessed the relationship between ML-AAC24 categories and incident fall-associated hospitalisations obtained from linked health records (mean ± SD follow-up, 3.9 ± 2.2 years). Individuals with moderate (9.6%) and high ML-AAC24 (11.7%) had a greater proportion of fall-associated hospitalisations, compared to those with low ML-AAC24 (6.0%). In age and sex-adjusted models, compared to low ML-AAC24, moderate (HR 1.49, 95% CI 1.24–1.79) and high ML-AAC24 (HR 1.89, 95% CI 1.56–2.28) were associated with greater hazards for a fall-associated hospitalisation. Results were comparable (HR 1.37, 95% CI 1.13–1.65 and HR 1.60, 95% CI 1.31–1.95, respectively) after multivariable adjustment, including prior falls and CVD, as well as medication use. Integrating ML-AAC24 into bone density machine software to identify high risk individuals would opportunistically provide important information on fall and cardiovascular disease risk to clinicians for evaluation and intervention.
DOI
10.1007/s11357-025-01589-7
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
Sim, M., Gebre, A. K., Dalla Via, J., Reid, S., Jozani, M. J., Kimelman, D., ... & Leslie, W. D. (2025). Automated abdominal aortic calcification scoring from vertebral fracture assessment images and fall-associated hospitalisations: The Manitoba Bone Mineral Density Registry. GeroScience. Advance online publication. https://doi.org/10.1007/s11357-025-01589-7