Author Identifier (ORCID)
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
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.
Document Type
Journal Article
Date of Publication
1-1-2025
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
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)
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