Clinical Biomechanics of Hip Fracture

美国每年发生超过30万例髋部骨折，高达25%的髋部骨折患者在一年内死亡 他们的伤。尽管这一临床问题的重要性，骨质疏松症的诊断筛查率是 只有5%的合格人群，以及DXA测量骨矿物质密度（BMD）的灵敏度， 临床诊断标准试验，只有50%。因此，大多数患者没有接受诊断筛查 对于骨质疏松症，对于那些谁是，大约一半的人将经历髋部骨折错过。鉴于 目前的经验方法是不够的，我们建议追求一个更机械的方法，结合 最先进的生物力学和机器学习方法。从生物力学上讲， 髋部骨折的要素是跌倒风险、股骨强度和股骨撞击力。在这个项目中，我们的总体目标是 更深入地了解这三个生物力学病因学因素在事件中如何相互作用， 的髋部骨折，并从这一点，直接改善临床骨折风险评估，通过使用单一的 预测性“整体生物力学风险（IBR）”参数。此外，我们还将解决低 DXA筛查率通过进一步发展我们的生物力学计算机断层扫描（BCT）技术。这 测试使用常规临床CT扫描的有限元分析来估计股骨的断裂强度 先前因任何医疗原因获得的，与使用BMD相比， 一个人由于每年有数百万患者接受CT扫描，如果 作为DXA的替代品。拟议的研究将在一个大的生物力学方法进行调查， 髋关节骨折事件，病例队列研究（3，000例髋关节骨折患者，6，000例无骨折患者）。这项回顾性 这项研究将包括在Kaiser Permanente接受腹部CT扫描作为医疗护理一部分的患者 在任何髋部骨折之前;并在其电子医疗记录中具有标准的老年测量， 我们将使用它来评估跌倒风险。具体而言，我们的目标是：1）利用电子病历数据和CT 扫描以获得与跌倒风险、股骨强度和跌倒严重程度相关的患者特定测量值，以及2） 联合收割机将髋部骨折病因学的不同要素结合到IBR参数中，以检验这一假设， 指标预测髋部骨折与年龄、性别、BMI、人种/种族和既往骨折史无关， 与临床标准（BMD与FRAX）相比，提高了髋部骨折预测。科学家，一个专业 这项工作的新奇在于它使用了当代机器学习算法来构建一个 髋关节骨折的三个病因因素的机械模型，它应该更好地捕捉任何 与纯粹的回归分析方法相比，这些元素之间的相互作用。此外该 研究队列将是有史以来最大、最多样化的基于CT的髋部骨折队列。重要的是， 该项目的积极成果将为DXA提供一个引人注目的“第二战线”， 转化为广泛的临床实践，深刻影响骨质疏松症护理。

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.