Details

Autor(en) / Beteiligte

• Dalla Via, Jack
• Gebre, Abadi K.
• Smith, Cassandra
• Gilani, Zulqarnain
• Suter, David
• Sharif, Naeha
• Szulc, Pawel
• Schousboe, John T.
• Kiel, Douglas P.
• Zhu, Kun
• Leslie, William D.
• Prince, Richard L.
• Lewis, Joshua R.
• Sim, Marc

Titel

Machine‐Learning Assessed Abdominal Aortic Calcification is Associated with Long‐Term Fall and Fracture Risk in Community‐Dwelling Older Australian Women

Ist Teil von

• Journal of bone and mineral research, 2023-12, Vol.38 (12), p.1867-1876

Ort / Verlag

Hoboken, USA: John Wiley & Sons, Inc

Erscheinungsjahr

2023

Quelle

Electronic Journals Library

Beschreibungen/Notizen

• 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).