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).

中文翻译：

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机器学习评估的腹主动脉钙化与社区居住的老年澳大利亚女性长期跌倒和骨折风险相关

腹主动脉钙化（AAC）是公认的晚期血管疾病指标，与较高的心血管风险和较差的长期预后相关。AAC 可以在骨密度筛查时使用经过验证的 24 点评分方法 (AAC-24) 在双能 X 射线骨密度测定 (DXA) 衍生的侧脊柱图像上进行评估，该图像用于椎骨骨折评估。先前的研究已发现 AAC-24 评分、跌倒事件和骨折之间存在密切关联。然而，手动 AAC 评估的一个主要限制是它需要训练有素的专家。因此，我们开发了一种自动机器学习算法来评估 AAC-24 分数 (ML-AAC24)。在这项前瞻性研究中，我们评估了来自珀斯老年妇女纵向研究的 1023 名社区老年女性（平均年龄 75 ± 3 岁）的 ML-AAC24 与长期跌倒和骨折之间的关联。经过 10 年的随访，253 名 (24.7%) 名女性每 4-6 个月自我报告一次，并通过 X 光检查确认有一次临床骨折，169 名 (16.5%) 名女性因骨折住院治疗，由关联医院确定放电数据。14.5 年来，根据关联的出院数据，有 393 名 (38.4%) 女性经历了跌倒伤害，需要住院治疗。调整基线骨折风险后，中度至广泛 AAC (ML-AAC24 ≥ 2) 的女性发生临床骨折的风险更大（风险比 [HR] 1.42；95% 置信区间 [CI]，1.10–1.85）和跌倒风险与 AAC 低（ML-AAC24 ≤ 1）的患者相比，相关住院治疗（HR 1.35；95% CI，1.09–1.66）。与手动评估的 AAC-24 类似，ML-AAC24 与骨折住院治疗无关。使用机器学习获得的相对危险估计与使用手动评估的 AAC-24 分数获得的相对危险估计相似。总之，这种用于评估 AAC 的新型自动化方法可以在骨密度测试时轻松、无缝地捕获，与长期临床骨折和伤害性跌倒事件具有密切的关联。然而，ML-AAC24 算法的性能需要在独立队列中进行验证。© 2023 作者。《Journal of Bone and Mineral Research》由 Wiley periodicals LLC 代表美国骨与矿物研究学会 (ASBMR) 出版。