Modeling fracture risk using bone density, age, and years since menopause
Modeling fracture risk using bone density, age, and years since menopause
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DOI：
10.1016/s0749-3797(18)30140-5
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发表时间：
1997-11-01
影响因子：
5.5
通讯作者：
El-Hajj Fuleihan, G
中科院分区：
文献类型：
--
作者：
Carroll, J;Testa, MA;El-Hajj Fuleihan, G
前言：预防策略对于降低骨质疏松症的发病率及其后果至关重要。然而，预测个人未来骨折风险的简单算法很少。该研究的目的是定义一种临床决策辅助工具，使医生能够预测个人一生的骨折风险，从而制定预防性治疗方法。方法：使用骨矿物质密度（BMD）、年龄、绝经后年限和体重开发了骨丢失的预测方程。这适用于年龄在40-80岁的正常和骨质疏松妇女（n = 117）进行骨质疏松症研究筛选。脊柱BMD临界值为0.86 gm/cm（2）时，检测椎体骨折受试者的敏感性为90%，特异性为60%，因此被定义为高-风险BMD.使用来自上述方程的参数估计和个体的临床数据，我们导出预测曲线-以预测个体将达到上述定义的高风险BMD的年龄，因此，该人的预期数量的剩余寿命年的骨折的高风险。结论：本研究提出了一个概念框架的发展，临床决策援助，提供指导方针，预防骨质疏松症。一项纵向研究，包括其他变量，如普遍的骨折和骨转换的生化标志物将进一步验证这一模型，并提高其应用。
Introduction: Preventive strategies are essential for reducing the incidence of osteoporosis and its consequences. However, simple algorithms that predict an individual's future risk of fractures are scarce. The purpose of the study was to define a clinical decision aid that enables physicians to project an individual's lifetime fracture risk and therefore institute preventive therapies.Methods: A predictor equation for bone loss was developed using bone mineral density (BMD), age, years since menopause, and weight. This was applied to normal and osteoporotic women, ages 40-80 years (n = 117) screened for osteoporosis studies.Results: A spinal BMD cutoff of 0.86 gm/cm(2) had a sensitivity of 90% and a specificity of 60% for detecting subjects with vertebral fractures and was therefore defined as a high-risk BMD.Using the parameter estimates from the above equation and an individual's clinical data, we derived prediction curves-to forecast the age at which that individual would reach the above defined high-risk BMD, and therefore that person's expected number of remaining life-years at high risk for fractures.Conclusions: This study proposes a conceptual framework for the development of a clinical decision aid to provide guidelines for the prevention of osteoporosis. A longitudinal study that incorporates other variables such as prevalent fractures and biochemical markers of bone turnover would further validate this model and enhance its application.