WHO Collaborating Centre for Metabolic Bone Diseases, University of Sheffield, Sheffield, UK.
Osteoporos Int. 2009 May;20(5):811-7. doi: 10.1007/s00198-008-0786-9. Epub 2008 Nov 11.
Fracture risk prediction can be enhanced by the concurrent assessment of other clinical risk factors. This study demonstrates that the estimation of an individual's 10-year probability of fracture by the FRAX algorithm identifies patients at high risk of fracture who will respond to bisphosphonate therapy.
Treatments for osteoporosis are targeted largely to patients with low bone density (BMD) or a prior fragility fracture. Fracture risk prediction can be enhanced by the concurrent assessment of other clinical risk factors, but it is important to determine whether the risk so identified can be reduced by intervention. We determined the effect of a bisphosphonate on fracture rates when risk was calculated using a new risk algorithm (FRAX).
Women aged 75 years or more were recruited to a randomised, double-blind controlled trial of 800 mg oral clodronate (Bonefos) daily over 3 years. Baseline clinical risk factors were entered in the FRAX model to compute the 10-year probability of major osteoporotic fractures with or without input of femoral neck BMD. The interaction between fracture probability and treatment efficacy was examined by Poisson regression.
In 3,974 women, the interaction between fracture probability and treatment efficacy was significant when probability was assessed without BMD (p = 0.043), but not when BMD was included (p = 0.10). Efficacy was more evident in those deemed at highest risk. For example women lying at the 75th percentile of fracture probability in the absence of BMD (10-year probability 24%) treatment reduced fracture risk by 27% (HR 0.73, 95%CI 0.58-0.92). In those with a fracture probability of 30% (90th percentile), the fracture risk reduction was 38% (HR 0.62, 0.46-0.84).
The estimation of an individual's 10-year probability of fracture by the FRAX algorithm identifies patients at high risk of fracture who will respond to bisphosphonate therapy.
骨折风险预测可通过同时评估其他临床风险因素得到增强。本研究旨在证明，应用 FRAX 算法估算个体 10 年骨折概率可识别出骨折风险高且对双膦酸盐治疗有反应的患者。
骨质疏松症的治疗主要针对低骨密度（BMD）或既往脆性骨折的患者。骨折风险预测可通过同时评估其他临床风险因素得到增强，但重要的是要确定所识别的风险是否可通过干预降低。我们通过使用新的风险算法（FRAX）计算风险，来确定双膦酸盐对骨折发生率的影响。
招募年龄在 75 岁及以上的女性参加为期 3 年的 800mg 口服氯膦酸盐（Bonefos）每日 1 次的随机、双盲对照试验。将基线临床风险因素输入 FRAX 模型，以计算不包括或包括股骨颈 BMD 时主要骨质疏松性骨折的 10 年骨折概率。通过泊松回归检验骨折概率与治疗效果之间的相互作用。
在 3974 名女性中，当不考虑 BMD 评估骨折概率时，骨折概率与治疗效果之间的相互作用具有统计学意义（p=0.043），而当包括 BMD 时无统计学意义（p=0.10）。疗效在风险最高的患者中更为明显。例如，在不考虑 BMD 情况下位于骨折概率第 75 百分位数的女性（10 年骨折概率为 24%），治疗后骨折风险降低 27%（HR 0.73，95%CI 0.58-0.92）。在骨折概率为 30%（90 百分位数）的患者中，骨折风险降低 38%（HR 0.62，0.46-0.84）。
应用 FRAX 算法估算个体 10 年骨折概率可识别出骨折风险高且对双膦酸盐治疗有反应的患者。
