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

描述（由申请人提供）：骨质疏松症是一个重大的公共卫生问题。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%可能在其剩余的生命中遭受脆性骨折。髋部骨折是最有害的骨折类型。已经进行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髋部骨折的风险。然而，以往的风险评估方法仅限于少数变量和这些因素的线性组合。此外，可用的测量方法越来越多，例如，可以从双能x射线吸收仪（DXA）中提取骨骼结构和骨骼肌质量，但基于这些丰富的信息，不存在可靠的风险预测模型。本研究的总体目标是建立一个全面而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，该预测器可以对包括髋关节结构几何、肌肉减少症测量以及先前研究中确定的风险因素在内的数据进行分类。该预测模型的构建将部分基于一项来自全国妇女健康倡议（WHI）的大型（n = 11,432）多民族骨骼队列研究。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。第一个目标是建立一个基于临床数据的风险模型，该模型考虑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（SVM），该方法明确识别可能导致髋部骨折的因素配置。第二个目标是使用支持向量机分类器和有限元建模来改进第一个目标的预测/决策模型。提出了一种在高维空间中选择数据点的方案，以提高基于支持向量机的风险预测模型的准确性。这些数据点将使用有限元模型进行评估（断裂与否）。所提出的有限元模型的新颖之处在于它的完全参数化，因此可以根据结构几何形状和材料参数的变化（即使很小）来研究骨反应的可变性。第三个目标是验证和比较使用有限元分析和不使用有限元分析的基于svm的风险，并为网络开发髋部骨折风险计算器。将使用来自WHI和其他队列的数据集进行交叉验证。SVM分类方法的灵活性使其易于在Internet上部署。本研究将由一个具有骨质疏松症流行病学研究、DXA测量（包括髋关节结构和肌肉减少症）、骨折评估、大数据集生物统计学方法、高维分析和有限元建模经验的跨学科团队使用现有队列进行，从而使本研究具有很高的可行性。该研究结果为髋部骨折风险评估提供了一种创新的工具，将对公众健康产生极其重要的影响。

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.