Real-time monitoring of knee forces and kinematics in vivo

描述(申请人提供)：全膝关节置换术已成为终末期关节炎的标准护理。美国人口普查局的数据预测，到2030年，对初次全膝关节置换术的需求将增长到每年350万例。到2015年，这将使翻修膝关节手术的需求翻一番，到2030年，需求将增加600%。膝盖力直接影响人工关节部件的存活率、关节支撑面的磨损以及骨-植入物界面的完整性。过大的膝盖力会加速骨水泥界面的破裂，或导致底层骨的损伤和坍塌。膝盖力和部件设计特征还决定了轴承表面的接触应力，这些应力与材料磨损和损坏的大小和分布直接相关。在所有活动中了解体内膝关节接触力和应力对于清楚地识别植入失败的风险将是非常有价值的。我们的设计目标是开发一种连续监测膝部力和运动学的系统。我们将使用一种新的算法来根据植入力感应型胫骨假体中测量的膝内力来确定膝关节运动学。我们将根据透视测量的运动学来验证结果。我们将开发一种可穿戴式数据采集系统，用于连续的无监督数据监测。我们将开发一种模式识别算法来对活体活动进行分类。这是一种获得体内膝关节接触力和运动学的独特方法，同时还可以对膝关节置换术进行完整的接触分析。在拟议的技术成熟度水平上监测、表征和分类体内较长时期的活动的能力是新颖的。使用该系统产生的数据将识别当前设计中的弱点和潜在的失败区域，为提高全膝关节置换术的功能和耐用性提供洞察力，并支持基于证据的患者安全术后康复、娱乐和锻炼的教育。公共卫生相关性：收集的数据将对膝关节生物力学领域，特别是膝关节置换领域有巨大的好处。我们将能够在更长的时间段(几天或几周)内持续监控数据，并记录自然发生的事件(与精心设计的活动形成对比)。由于我们将计算胫股接触作为确定运动学的算法的一部分，力和运动学已经伴随着接触分析。我们已收到多个实验室(包括斯坦福大学、哈佛大学、英国牛津大学特别外科医院、佛罗里达大学、首尔国立大学、澳大利亚墨尔本大学和梅奥诊所)对数据的请求，以开发或验证膝关节动力学和运动学的计算机和体外模型，以及开发更具临床意义的磨损和疲劳测试方案。这些数据可以作为损伤和磨损模型的输入，以预测故障或验证预测膝盖力和运动学的膝关节生物力学模型。膝盖的设计也在不断演变。一个例子包括允许更大的膝盖功能的设计，以及允许患者参与涉及跪、蹲和盘腿坐的活动的设计。分析这些活动的研究在没有活体验证这些力量的情况下估计了高膝力。据报道，经常蹲、跪或盘腿坐的患者翻修膝关节成形术的发生率较高。新的和现有的假体设计将不得不进行修改，以承受预期的负荷增加。替代轴承表面正在被引入，这些表面需要比当前建议的标准更多的临床相关测试。持续监测日常条件下的活体膝部力和运动学将识别当前和未来设计中的弱点和潜在失败区域，并将为提高全膝关节置换术的功能和耐用性提供洞察力。

DESCRIPTION (provided by applicant): Total knee arthroplasty has become the standard of care for end-stage arthritis. The US Census Bureau data predicts that demand for primary total knee arthroplasties will grow to 3.5 million procedures annually by 2030. This will double the demand for revision knee surgery by 2015 and will increase demand 600% by 2030. Knee forces directly affect arthroplasty component survivorship, wear of articular bearing surfaces, and integrity of the bone-implant interface. Excessive knee forces accelerate breakdown of the cement interface or induces damage and collapse of the underlying bone. Knee forces and component design features also determine the contact stresses on the bearing surfaces, which are directly associated with the magnitude and distribution of material wear and damage. Knowledge of in vivo knee contact forces and stresses during all activities will be extremely valuable in clearly identifying risks for implant failure. Our design objective is to develop a system for continuously monitoring knee forces and kinematics. We will use a novel algorithm to determine knee kinematics from knee forces measured in implanted force-sensing tibial prosthesis. We will validate the results against fluoroscopically measured kinematics. We will develop a wearable data acquisition system for continuous unsupervised data monitoring. We will develop a pattern recognition algorithm to classify activities in vivo. This is a unique method of obtaining in vivo knee contact forces and kinematics together with a complete contact analysis for knee arthroplasty. The ability to monitor, characterize, and classify activities in vivo over extended periods at the proposed level of technical sophistication is novel. Data generated using this system will identify weaknesses and potential areas of failure in current designs, provide insight into enhancing the function and durability of total knee arthroplasty, and support evidence-based patient education on safe postoperative rehabilitation, recreation, and exercise. PUBLIC HEALTH RELEVANCE: The data collected will be of enormous benefit to the field of knee biomechanics in general and knee arthroplasty in particular. We will be able to continuously monitor data over extended periods of time (days or weeks) and to record naturally occurring events (in contrast to choreographed activity). Since we compute tibiofemoral contact as part of the algorithm to determine the kinematics, the forces and kinematics are already accompanied with contact analysis. We have received requests from several laboratories (including Stanford University, Harvard University, Hospital for Special Surgery, Oxford University, UK, University of Florida, Seoul National University, University of Melbourne, Australia and the Mayo Clinic) for data to develop or validate in silico and in vitro models of knee kinetics and kinematics, as well as to develop more clinically relevant wear and fatigue testing protocols. These data can be used as input into damage and wear models to predict failure or for validation of biomechanical models of the knee, which predict knee forces and kinematics. Knee designs are constantly evolving. One example includes designs that will permit greater knee function and that will allow patients to engage in activities that involve kneeling, squatting, and sitting cross-legged. Studies analyzing these activities have estimated high knee forces without in vivo validation of these forces. A higher incidence of revision knee arthroplasties is reported in patients that routinely squat, kneel, or sit cross-legged. New and existing prosthetic designs will have to be modified to withstand the anticipated increase in loading. Alternative bearings surfaces are being introduced that require more clinically relevant testing than the currently proposed standards. Continuously monitoring in vivo knee forces and kinematics under daily conditions will identify weaknesses and potential areas of failure in current and future designs and will provide insight into enhancing the function and durability of total knee arthroplasty.