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

基于通用预测因子和髋关节几何模型的新型髋部骨折风险预测工具

基本信息

批准号：
8327065
负责人：
Zhao Chen
金额：
$ 19.24万
依托单位：
UNIVERSITY OF ARIZONA
依托单位国家：
美国
项目类别：
财政年份：
2011
资助国家：
美国
起止时间：
2011-09-01 至 2015-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8327065
关键词：
Accounting Address Age Aging Algorithms Biometry Bone Density Cardiovascular system Classification Clinic Clinical Data Cohort Studies Complex Coupling Data Data Set Databases Decision Modeling Dual-Energy X-Ray Absorptiometry Elements Environment Epidemiology Female Finite Element Analysis Fracture Goals Health Hip Fractures Hip region structure Individual Institutes Interdisciplinary Study Internet Lead Learning Machine Learning Measurement Measures Methods Modeling Osteoporosis Osteoporosis prevention Outcome Patients Population Prevention Property Public Health Research Research Personnel Resources Risk Risk Assessment Risk Factors Scheme Simulate Structure Techniques Testing Training Use of New Techniques Validation Variant Woman Women&apos s Health base bone cohort data space experience flexibility high risk improved innovation member model development muscle form novel novel strategies older women predictive modeling response sarcopenia simulation skeletal stem tool working group

项目摘要

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.

描述（由申请人提供）：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%的50岁或50岁以上的女性可能在其余生中遭受脆性骨折。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，以便可以使用预防方法来减少越来越多的老年妇女的这种风险。然而，以前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，有越来越多的可用测量，如骨结构和骨骼肌质量，可以提取，例如，从双能X射线吸收测定法（DXA），并没有可靠的风险预测模型存在的基础上，这些丰富的信息。本研究的总体目标是开发一个全面而灵活的模型来评估特定女性髋部骨折的风险。这将通过构建一种新的预测器来实现，该预测器对包括髋关节结构几何形状、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。预测模型的构建将部分基于在来自全国妇女健康倡议（WHI）的大型（n = 11，432）多种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。第一个目标是根据临床数据生成一个风险模型，该模型考虑了髋部骨折相关因素的耦合效应。本研究介绍了一种新的方法在髋部骨折领域，支持向量机（SVM），它明确地确定配置的因素，可能导致髋部骨折。第二个目标是使用SVM分类器和有限元建模两者从第一个目标细化预测/决策模型。已经开发了一种方案来在高维空间中选择将提高基于SVM的风险预测模型的准确性的数据点。将使用有限元模型评价这些数据点（断裂或未断裂）。所提出的有限元模型的新奇源于其完整的参数化，以便可以研究骨反应的变异性相对于结构几何形状和材料参数的变化（即使很小）。第三个目的是验证和比较基于SVM的风险与不使用有限元分析，并开发一个髋部骨折风险计算器的网络。将使用WHI以及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互联网上部署。本研究将由一个在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、大型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丰富经验的跨学科团队使用现有队列进行，从而使本研究具有高度可行性。该研究结果将通过为髋部骨折风险评估提供创新工具，对公共卫生产生极其重大的影响。