A New Hip Fracture Risk Prediction Tool Based on Common Predictors and Hip Geomer

描述(申请人提供)：骨质疏松症是一个主要的公共卫生问题。女性患骨质疏松症的风险特别高，50岁或以上的女性中有50%可能在其余生中遭受脆性骨折的痛苦。髋部骨折是最有害的骨折类型。已经开展了评估髋部骨折风险的研究，因此可以使用预防方法来降低越来越多的老年妇女的这种风险。然而，以往的风险评估方法仅限于少数几个变量和这些因素的线性组合。此外，有越来越多的可用指标，如骨结构和骨骼肌质量，可以提取，例如，从双能X射线吸收法(DXA)，而没有可靠的风险预测模型基于这些丰富的信息。这项研究的总体目标是开发一个全面和灵活的模型来评估特定女性髋部骨折的风险。这将通过构建一个新的预测器来实现，该预测器对包括髋关节结构几何形状、骨量测量以及先前研究中确定的风险因素的数据进行分类。预测模型的构建将部分基于在全国妇女健康倡议(WHI)的一个大型(n=11,432)多民族骨骼队列中进行的研究。此外，为了提高风险预测的质量，将使用有限元模拟的计算数据。有三个具体目标。第一个目标是基于临床数据生成一个风险模型，该模型解释了髋部骨折相关因素的耦合效应。本研究在髋部骨折领域引入了一种新的方法--支持向量机，它显式地识别可能导致髋部骨折的因素的配置。第二个目标是使用支持向量机分类器和有限元建模从第一个目标改进预测/决策模型。开发了一种在高维空间中选择数据点的方案，该方案将提高基于支持向量机的风险预测模型的精度。这些数据点将使用有限元模型进行评估(骨折与否)。所提出的有限元模型的新颖之处在于其完全的参数化，因此可以相对于结构几何和材料参数的变化(甚至很小)来研究骨响应的可变性。第三个目标是在使用和不使用有限元分析的情况下验证和比较基于支持向量机的风险，并开发用于网络的髋部骨折风险计算器。将使用WHI和其他队列的数据集进行交叉验证。支持向量机分类方法的灵活性使其很容易在互联网上部署。这项研究将由一个跨学科团队利用现有的队列进行，该团队在骨质疏松症流行病学研究、包括髋部结构和骨量减少的DXA测量、骨折评估、大数据集的生物统计学方法、高维分析和有限元建模方面具有经验，因此使这项研究具有很高的可行性。研究结果将为髋部骨折风险评估提供一种创新的工具，从而对公众健康产生极其重大的影响。 公共卫生相关性：这项研究将使用创新的方法、现有的队列资源和跨学科的专业知识来解决一个重大的公共卫生挑战：评估髋部骨折的风险，这是最有害的脆性骨折类型。这项研究旨在为研究人员和临床医生提供一种更好的网络风险评估工具，以评估个人髋部骨折的风险。这项研究将测试新的预测因素，并使用无假设建模方法来捕捉预测因素与骨折风险的复杂和非线性关系。这项研究对于减少庞大且不断增长的美国老年妇女人口的骨折负担具有重要意义。

DESCRIPTION (provided by applicant): Osteoporosis is a major public health problem. Women are at a particularly high risk for osteoporosis and 50% of women age 50 or older may suffer from a fragility fracture in their remaining lifetime. Hip fractures are the most detrimental type of fractures. Research has been conducted to assess hip fracture risk so prevention methods could be used to reduce this risk in the growing number of older women. However, previous risk assessment approaches are limited to a few variables and linear combinations of these factors. Also, there is an increasing number of available measures, such as bone structures and skeletal muscle mass, that can be extracted, for instance, from dual-energy X-ray absorptiometry (DXA), and no reliable risk prediction model exist based on this wealth of information. The overall goal of this study is to develop a comprehensive and flexible model to assess the risk of hip fracture for a specific woman. This will be achieved by constructing a novel predictor that classifies data that include hip structural geometry, sarcopenia measurements as well as risk factors identified in previous studies. The construction of the predictive model will be partly based on a study conducted among a large (n = 11,432) multi-ethnic bone cohort from the nationwide Women's Health Initiative (WHI). In addition, to enhance the quality of the risk prediction, computational data from finite element simulations will be used. There are three specific aims. The first aim is to generate a risk model, based on clinical data that accounts for the coupling effects of the factors involved in hip fracture. This research introduces a new approach in the field of hip fracture, Support Vector Machines (SVM), which explicitly identifies the configurations of factors that are likely to lead to hip fracture. The second aim is to refine the prediction/decision model from the first aim using both the SVM classifier and finite element modeling. A scheme has been developed to select, in a high dimensional space, data points that would improve the accuracy of the SVM-based risk prediction model. These data points would be evaluated (fracture or not) using a finite element model. The novelty of the proposed finite element model stems from its full parameterization so that the variability of the bone response can be studied with respect to variations (even small) of structural geometry and material parameters. The third aim is to validate and compare the SVM-based risk with and without the use of finite element analysis and develop a hip fracture risk calculator for the web. A cross validation will be performed using data sets from the WHI as well as other cohorts. The flexibility of the SVM classification approach makes it easily deployable on the Internet. This study will be carried out using existing cohorts by an interdisciplinary team with experience in epidemiology of osteoporosis research, DXA measurements including hip structures and sarcopenia, fracture assessments, biostatistics approaches for large datasets, high dimensional analysis and finite element modeling, thus making this study highly feasible. The study results will have an extremely significant public health impact by providing an innovative tool for hip fracture risk assessments. PUBLIC HEALTH RELEVANCE: This study will use innovative approaches, existing cohort resources, and interdisciplinary expertise to address a significant public health challenge: assessing the risk of hip fracture, the most detrimental type of fragility fractures. The study aims for a better risk assessment tool on the web that can be used by researchers and clinicians to assess an individual's hip fracture risk. This research will test new predictors and use the assumption free modeling approach to capture complex and non-linear relationships of predictors with fracture risk. This research is significant for reducing fracture burdens in the large and growing U.S. older women population.