Nguyen Tuan V, Eisman John A
Bone Biology Division, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia; St Vincent's Clinical School, UNSW Medicine, UNSW, Australia; Centre for Health Technology, University of Technology, Sydney, Australia.
Bone Biology Division, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia; St Vincent's Clinical School, UNSW Medicine, UNSW, Australia; School of Medicine Sydney, University of Notre Dame Australia, Fremantle, Australia.
J Clin Densitom. 2017 Jul-Sep;20(3):368-378. doi: 10.1016/j.jocd.2017.06.023. Epub 2017 Jul 17.
Fracture caused by osteoporosis remains a major public health burden on contemporary populations because fracture is associated with a substantial increase in the risk of mortality. Early identification of high-risk individuals for prevention is a priority in osteoporosis research. Over the past decade, few risk prediction models, including the Garvan Fracture Risk Calculator (Garvan) and FRAX, have been developed to provide absolute (individualized) risk of fracture. Recent validation studies suggested that the area under the receiver operating characteristic curve in fracture discrimination ranged from 0.61 to 0.83 for FRAX and from 0.63 to 0.88 for Garvan, with hip fractures having a better discrimination than fragility fractures as a group. Although the prognostic performance of Garvan and FRAX for fracture prediction is not perfect and there is room for further improvement, these predictive models can aid patients and doctors communicate about fracture risk in the medium term and to make rational decisions. However, the application of these predictive models in making decisions for an individual should take into account the individual's perception of the importance of fracture relative to other diseases.
骨质疏松症导致的骨折仍是当代人群面临的一项重大公共卫生负担，因为骨折与死亡率大幅上升相关。早期识别高危个体以进行预防是骨质疏松症研究的重点。在过去十年中，已经开发了一些风险预测模型，包括加尔万骨折风险计算器（Garvan）和FRAX，以提供骨折的绝对（个性化）风险。最近的验证研究表明，FRAX在骨折鉴别方面的受试者工作特征曲线下面积在0.61至0.83之间，Garvan在0.63至0.88之间，其中髋部骨折的鉴别能力优于作为一个整体的脆性骨折。尽管Garvan和FRAX在骨折预测方面的预后表现并不完美，仍有进一步改进的空间，但这些预测模型可以帮助患者和医生在中期就骨折风险进行沟通，并做出合理决策。然而，这些预测模型在为个体做出决策时的应用应考虑个体对骨折相对于其他疾病重要性的认知。
