Osteoporosis and Bone Biology Program, Garvan Institute of Medical Research, Sydney, New South Wales, Australia.
Osteoporos Int. 2010 May;21(5):863-71. doi: 10.1007/s00198-009-1026-7. Epub 2009 Jul 25.
We evaluated the prognostic accuracy of fracture risk assessment tool (FRAX) and Garvan algorithms in an independent Australian cohort. The results suggest comparable performance in women but relatively poor fracture risk discrimination in men by FRAX. These data emphasize the importance of external validation before widespread clinical implementation of prognostic tools in different cohorts.
Absolute risk assessment is now recognized as a preferred approach to guide treatment decision. The present study sought to evaluate accuracy of the FRAX and Garvan algorithms for predicting absolute risk of osteoporotic fracture (hip, spine, humerus, or wrist), defined as major in FRAX, in a clinical setting in Australia.
A retrospective validation study was conducted in 144 women (69 fractures and 75 controls) and 56 men (31 fractures and 25 controls) aged between 60 and 90 years. Relevant clinical data prior to fracture event were ascertained. Based on these variables, predicted 10-year probabilities of major fracture were calculated from the Garvan and FRAX algorithms, using US (FRAX-US) and UK databases (FRAX-UK). Area under the receiver operating characteristic curves (AUC) was computed for each model.
In women, the average 10-year probability of major fracture was consistently higher in the fracture than in the nonfracture group: Garvan (0.33 vs. 0.15), FRAX-US (0.30 vs. 0.19), and FRAX-UK (0.17 vs. 0.10). In men, although the Garvan model yielded higher average probability of major fracture in the fracture group (0.32 vs. 0.14), the FRAX algorithm did not: FRAX-US (0.17 vs. 0.19) and FRAX-UK (0.09 vs. 0.12). In women, AUC for the Garvan, FRAX-US, and FRAX-UK algorithms were 0.84, 0.77, and 0.78, respectively, vs. 0.76, 0.54, and 0.57, respectively, in men.
In this analysis, although both approaches were reasonably accurate in women, FRAX discriminated fracture risk poorly in men. These data support the concept that all algorithms need external validation before clinical implementation.
评估骨折风险评估工具（FRAX）和 Garvan 算法在澳大利亚独立队列中的预后准确性。结果表明，FRAX 在女性中的预测性能相当，但在男性中的骨折风险区分能力相对较差。这些数据强调了在不同队列中广泛临床应用预后工具之前进行外部验证的重要性。
绝对风险评估现在被认为是指导治疗决策的首选方法。本研究旨在评估 FRAX 和 Garvan 算法在澳大利亚临床环境中预测骨质疏松性骨折（髋部、脊柱、肱骨或腕部）绝对风险的准确性，FRAX 中定义为主要骨折。
对 144 名年龄在 60 至 90 岁之间的女性（69 例骨折和 75 例对照）和 56 名男性（31 例骨折和 25 例对照）进行了回顾性验证研究。确定骨折事件前的相关临床数据。基于这些变量，使用美国（FRAX-US）和英国数据库（FRAX-UK），从 Garvan 和 FRAX 算法计算出 10 年主要骨折的预测概率。计算了每个模型的受试者工作特征曲线下面积（AUC）。
在女性中，骨折组的平均 10 年主要骨折概率始终高于非骨折组：Garvan（0.33 比 0.15）、FRAX-US（0.30 比 0.19）和 FRAX-UK（0.17 比 0.10）。在男性中，尽管 Garvan 模型在骨折组中产生了更高的主要骨折平均概率（0.32 比 0.14），但 FRAX 算法并非如此：FRAX-US（0.17 比 0.19）和 FRAX-UK（0.09 比 0.12）。在女性中，Garvan、FRAX-US 和 FRAX-UK 算法的 AUC 分别为 0.84、0.77 和 0.78，而男性分别为 0.76、0.54 和 0.57。
在这项分析中，尽管两种方法在女性中都具有相当的准确性，但 FRAX 在男性中区分骨折风险的能力较差。这些数据支持所有算法在临床应用之前都需要进行外部验证的概念。
