Determining the Probability of Vertebral Fracture

确定椎骨骨折的概率

• 批准号: 7007288
• 负责人: DANIEL P NICOLELLA
• 金额: $ 14.82万
• 依托单位: SOUTHWEST RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-01-10 至 2007-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7007288
• 关键词: bone density; bone fracture; bone imaging /visualization /scanning; computational biology; disease /disorder proneness /risk; mathematical model; method development; model design /development; physics; structural biology; substantia spongiosa; vertebrae

DESCRIPTION (provided by applicant): This exploratory proposal aims to develop and investigate a novel means of determining the risk of fracture of skeletal systems. The problem of increased risk of fracture as a result of bone mass loss due to aging or disease is a major clinical concern, and has been the focus of a substantial research effort. Although some correlative relationships have been established between fracture risk and various imaging and biochemical data, these methods remain nonspecific and have low sensitivity for predicting fracture risk. Clearly, an accurate means of predicting risk of fracture for an individual subject would have tremendous clinical benefit. We propose to combine several analysis techniques that have not been used together before to produce a rigorously verified and validated, physics based approach to quantify the probability or risk of bone fracture. Namely, probabilistic finite element methods will provide a framework for accounting for uncertainty and randomness inherently involved in the mechanics of skeletal structures, and a recently developed constitutive model will describe the time-dependent, permanent, and stiffness degradation behavior of bone. Models of vertebral trabecular bone specimens and whole vertebrae will be produced using geometry and material property descriptions based on high-resolution micro-CT imaging data, allowing quantitative evaluation of predictive accuracy for specimen-level and whole-bone level failure, and for determining the probability of structural failure with respect to loading history. Although, initially applied to models to vertebral fractures, these methods have tremendous potential for providing individualized interpretive models to predict fracture risk for the discrete patient with a higher degree of accuracy than large population statistical correlations can provide. Further, this exploratory study will provide important information to guide diagnostic imaging and constitutive model refinements, in order to fully utilize the inherent potential of these probabilistic imaging-based methods.

描述（由申请人提供）： 该探索性建议旨在开发和研究确定骨骼系统破裂风险的新颖手段。由于衰老或疾病引起的骨骼质量损失，骨折风险增加的问题是主要的临床问题，并且一直是大量研究工作的重点。尽管裂缝风险与各种成像和生化数据之间已经建立了一些相关关系，但这些方法仍然是非特异性的，并且在预测断裂风险方面具有低灵敏度。显然，预测单个受试者骨折风险的准确方法将具有巨大的临床益处。 我们建议将几种以前未被用于的分析技术结合在一起，以生成一种严格验证和经过验证的基于物理学的方法，以量化骨折的概率或风险。也就是说，概率有限元方法将为骨骼结构机械固有的不确定性和随机性提供一个框架，而最近开发的组成型模型将描述骨骼的时间依赖性，永久性和僵硬的降解行为。将使用基于高分辨率的Micro-CT成像数据的几何形状和材料特性描述来生产椎骨小梁标本和整个椎骨的模型，从而可以定量评估样品水平和全骨水平失败的预测准确性，以及确定相对于负载历史的结构失败的可能性。 尽管最初将模型应用于椎骨骨折，但这些方法具有提供个性化的解释模型的巨大潜力，以预测与大量人群统计相关性更高的离散患者的断裂风险。此外，这项探索性研究将提供重要的信息来指导诊断成像和组成型模型改进，以便充分利用这些基于概率成像的方法的固有潜力。