Document Type
Journal Article
Publication Title
Journal of Bone and Mineral Research
Volume
38
Issue
12
First Page
1867
Last Page
1876
Publisher
Wiley
School
School of Medical and Health Sciences / Nutrition and Health Innovation Research Institute
RAS ID
60470
Funders
National Health and Medical Research Council / Funding information: https://doi.org/10.1002/jbmr.4921
Grant Number
NHMRC Numbers : 254627, 303169, 572604
Grant Link
http://purl.org/au-research/grants/nhmrc/254627 http://purl.org/au-research/grants/nhmrc/303169 http://purl.org/au-research/grants/nhmrc/572604
Abstract
Abdominal aortic calcification (AAC), a recognized measure of advanced vascular disease, is associated with higher cardiovascular risk and poorer long-term prognosis. AAC can be assessed on dual-energy X-ray absorptiometry (DXA)-derived lateral spine images used for vertebral fracture assessment at the time of bone density screening using a validated 24-point scoring method (AAC-24). Previous studies have identified robust associations between AAC-24 score, incident falls, and fractures. However, a major limitation of manual AAC assessment is that it requires a trained expert. Hence, we have developed an automated machine-learning algorithm for assessing AAC-24 scores (ML-AAC24). In this prospective study, we evaluated the association between ML-AAC24 and long-term incident falls and fractures in 1023 community-dwelling older women (mean age, 75 ± 3 years) from the Perth Longitudinal Study of Ageing Women. Over 10 years of follow-up, 253 (24.7%) women experienced a clinical fracture identified via self-report every 4–6 months and verified by X-ray, and 169 (16.5%) women had a fracture hospitalization identified from linked hospital discharge data. Over 14.5 years, 393 (38.4%) women experienced an injurious fall requiring hospitalization identified from linked hospital discharge data. After adjusting for baseline fracture risk, women with moderate to extensive AAC (ML-AAC24 ≥ 2) had a greater risk of clinical fractures (hazard ratio [HR] 1.42; 95% confidence interval [CI], 1.10–1.85) and fall-related hospitalization (HR 1.35; 95% CI, 1.09–1.66), compared to those with low AAC (ML-AAC24 ≤ 1). Similar to manually assessed AAC-24, ML-AAC24 was not associated with fracture hospitalizations. The relative hazard estimates obtained using machine learning were similar to those using manually assessed AAC-24 scores. In conclusion, this novel automated method for assessing AAC, that can be easily and seamlessly captured at the time of bone density testing, has robust associations with long-term incident clinical fractures and injurious falls. However, the performance of the ML-AAC24 algorithm needs to be verified in independent cohorts. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).
DOI
10.1002/jbmr.4921
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
Dalla Via, J., Gebre, A. K., Smith, C., Gilani, Z., Suter, D., Sharif, N., . . . Sim, M. (2023). Machine-learning assessed abdominal aortic calcification is associated with long-term fall and fracture risk in community-dwelling older Australian women. Journal of Bone and Mineral Research, 38(12), 1867-1876. https://doi.org/10.1002/jbmr.4921