Clinical Biomechanics of Hip Fracture

髋部骨折的临床生物力学

基本信息

批准号：
9886227
负责人：
David Kopperdahl
金额：
$ 64.71万
依托单位：
O. N. DIAGNOSTICS, LLC
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-04-15 至 2023-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9886227
关键词：
Accounting Address Age Algorithms Archives Biomechanics Blinded Bone Density Calibration Caring Clinical Clinical Data Clinical assessments Cohort Studies Complex Computerized Medical Record Data Diagnosis Discrimination Dual-Energy X-Ray Absorptiometry Economics Elderly Elements Ensure Equilibrium Ethnic Origin Etiology Evaluation Event Fatty acid glycerol esters Femur Finite Element Analysis Fracture Goals Health Hip Fractures Individual Injury Intramuscular Investigation Logistics Machine Learning Measurement Measures Medical Medical Records Modeling Muscle Osteoporosis Pathway interactions Patient Care Patients Pharmaceutical Preparations Population Preventive care Preventive treatment Probability Race Recording of previous events Regression Analysis Retrospective Studies Risk Risk Assessment Risk Factors Scanning Severities Shapes Spatial Distribution Statistical Models Technology Testing Thick Tissues Training Translating Validation Weight Work X-Ray Computed Tomography abdominal CT base biomechanical model bone bone strength clinical practice clinical risk clinical translation cohort diagnostic screening ductile electronic data experience fall risk falls fracture risk high risk hip bone improved indexing insight kinematics machine learning algorithm neuromuscular older patient patient health information screening sex soft tissue standing height theories tool

项目摘要

Over 300,000 hip fractures occur each year in the U.S. and up to 25% of hip-fracture patients die within a year of their injury. Despite the importance of this clinical problem, the diagnostic screening rate for osteoporosis is only 5% of the eligible population, and the sensitivity of measuring bone mineral density (BMD) by DXA, the clinical standard test for diagnosis, is only 50%. Therefore, most patients are not being screened diagnostically for osteoporosis, and for those who are, about half who will experience a hip fracture are missed. Given that the current empirical approach is inadequate, we propose to pursue a more mechanistic approach, combining state-of-the-art biomechanics and machine learning approaches. Biomechanically, the three etiological elements of hip fracture are fall risk, femoral strength, and femoral impact force. In this project, our overall goal is to provide a deeper understanding of how all three biomechanical etiological elements interact in the event of a hip fracture and from that, directly improve clinical fracture risk assessment through the use of a single predictive “Integral Biomechanical Risk (IBR)” parameter. In addition, we will also address the problem of low DXA screening rates by further developing our Biomechanical Computed Tomography (BCT) technology. This test estimates the breaking strength of the femur using finite element analysis of routine clinical CT scans previously acquired for any medical reason, and represents an improvement compared to the use of BMD alone. Since millions of patients are scanned with CT each year, this approach could double screening rates if offered as an alternative to DXA. The proposed study will investigate this biomechanical approach in a large incident hip fracture, case-cohort study (3,000 patients with hip fracture, 6,000 without). This retrospective study will include patients seen at Kaiser Permanente who had an abdominal CT scan as part of medical care prior to any hip fracture; and have standard geriatric measurements in their electronic medical records, which we will use to estimate fall risk. Specifically, our aims are to: 1) utilize electronic medical record data and CT scans to obtain patient-specific measurements related to fall risk, femoral strength, and fall severity, and 2) combine the different elements of hip fracture etiology into the IBR parameter to test the hypothesis that this metric predicts hip fracture independent of age, sex, BMI, race/ethnicity, and history of prior fracture and improves hip fracture prediction compared to the clinical standard (BMD with FRAX). Scientifically, a major novelty of this work is its use of contemporary machine learning algorithms to inform construction of a mechanistic model of the three etiological elements of hip fracture, which should better capture any interactions between these elements compared to a purely statistical-regression approach. In addition, the study cohort will be the largest and most diverse CT-based hip fracture cohort ever assembled. Importantly, positive results from this project would provide a compelling “second front” to DXA that could be quickly translated to widespread clinical practice, profoundly impacting osteoporosis care.

在美国，每年有超过300,000个髋部骨折发生，一年内最多25％他们的受伤。尽管这个临床问题很重要，但骨质疏松症的诊断筛查率是只有5％的符合条件人口，以及DXA测量骨矿物质密度（BMD）的灵敏度，诊断的临床标准测试仅为50％。因此，大多数患者没有被诊断筛查对于骨质疏松症，对于那些骨质疏松症，大约一半会经历髋部骨折的人。鉴于当前的经验方法是不足的，我们建议采用更机械的方法，结合最先进的生物力学和机器学习方法。生物力学，三个病因髋部骨折的元素是跌倒风险，股骨力量和股骨影响力。在这个项目中，我们的总体目标是为了更深入地了解这三种生物力学的病因学元素如何相互作用髋部骨折，并通过使用单一直接改善临床断裂风险评估预测性“积分生物力学风险（IBR）”参数。此外，我们还将解决低点的问题通过进一步开发我们的生物力学计算机断层扫描（BCT）技术，DXA筛选速率。这测试使用常规临床CT扫描的有限元分析估算股骨的破裂强度先前出于任何医疗原因获得的，并且与使用BMD相比有所改善独自的。由于每年数以百万计的患者使用CT扫描，因此，如果这种方法可以筛选率，如果作为DXA的替代品。拟议的研究将在大型中调查这种生物力学方法事件髋部骨折，病例 - 霍特研究（3,000例髋部骨折患者，6,000例无）。这个回顾研究将包括在Kaiser Permanente看到的患者，他们的腹部CT扫描是医疗护理的一部分在任何髋部骨折之前；并在其电子病历中具有标准的老年测量值，我们将用来估计跌倒风险。具体而言，我们的目标是：1）使用电子病历数据和CT 扫描以获得与跌倒风险，股力量和跌倒严重程度有关的患者特定测量值，以及2）将髋部断裂病因的不同元素结合到IBR参数，以检验以下假设。度量预测髋部骨折独立于年龄，性别，BMI，种族/种族和先前骨折的历史与临床标准（FRAX的BMD）相比，髋部骨折预测改善了。从科学上讲，专业这项工作的新颖性是它使用当代机器学习算法来告知构造髋部骨折的三个病因元素的机械模型，应更好地捕获任何与纯粹的统计回归方法相比，这些元素之间的相互作用。另外，研究队列将是有史以来最大，最潜水员CT的髋部骨折队列。重要的是，该项目的积极结果将为DXA提供引人注目的“第二局部”，这可能很快转化为宽度临床实践，深远影响骨质疏松症护理。