The Morphology and Characteristics of Hallux Rigidus

拇趾僵硬（HR）是大脚趾骨关节炎的终末期，这是一种退行性关节疾病， 大约每40个50岁以上的人中就有一个。第一跖趾关节炎的发生发展 关节（MTPJ）疼痛，影响生活质量，降低活动性和改变步态模式。早期治疗 疼痛缓解包括物理治疗、药物和改进的鞋类。如果这些治疗不能提供 缓解和关节炎进展，关节功能大部分丧失（因此终末期疾病进展的僵硬）。 关于退伍军人，从2008年到2017年，退伍军人医疗保健系统的全国数据产生了87，798 HR患者就诊次数（VA Puget Sound医疗保健系统同期为1，897次）以及 全国患者就诊人数逐年增加，从2008年的9，396人次增加到2017年的22，180人次。 为了使资深HR患者与适当的治疗相匹配，疾病进展的阶段需要 要正确分类。 最常见的分类系统使用放射学检查结果。通常，AP和侧位X射线视图是 评估关节间隙（间隙减少表明软骨丢失）、骨赘（和关节炎指标），以及 关节形状（因为HR患者优先失去软骨并在背侧表面获得骨赘）。然而 评估的确切特征以及如何考虑这些特征没有标准化。这一问题进一步复杂化， 一些分级方案将活动范围和疼痛的临床结果融合到已经复杂的领域中 放射学特征。最后，虽然2D X射线是诊断的标准，但HR是一种复杂的3D疾病 并且已经假设某些骨的位置（第一跖骨隆起）和形态 （关节面形状）可能与HR相关，并且可能使患者易于发展HR。 测量可能难以在2D X射线上实现，并且难以使用手动和主观测量。 因此，我们建议利用现有的对照足负重CT数据， 15个HR主题我们将使用这些成像数据来评估与以下一些相关的组内相关性： 更常见的人力资源分类方案（目标1）。这将使我们能够建立可靠性措施， 现有的临床分级拇趾僵硬的方法，这将有助于告知临床社区的可靠性 这对治疗选择和病人护理有直接影响。 我们还将利用我们之前在评估3D骨骼解剖结构方面的经验，半自动化一套第一射线 位置（关节间距、第一跖骨排列、第一跖骨抬高）和形态（矢状 平面跖骨头弯曲）特征，其被怀疑与HR相关（目标2）。 与这项工作相关的最大挑战之一是通过以下方式建立一个用户友好的环境： 临床医生可以快速选择骨骼特征，这将通过算法确定测量结果， 立即报告。为此，我们将聘请一名研究工程师，在开发用户的特定背景 界面和3D数字对象操作。这种专业化旨在生成一个记录良好的 完善的分析程序，临床医生可以很容易地学习。另一个重大挑战是 确定我们建议的额外措施在增强或取代 目前的分类制度。这些测量的好处将支持我们的额外目标， 为加强现行分类办法（可能通过增加一项额外措施）奠定基础 或开发新的分类方案（通过新的半自动测量）两个项目领域 本研究将开发初步数据，以便在未来进行Merit审查。

Hallux Rigidus (HR) is the end stage of great toe osteoarthritis, which is a degenerative joint disease that effects approximately 1 in 40 people over the age of 50. The development of arthritis in the first metatarsophalangeal joint (MTPJ) is painful, effecting quality of life, reducing mobility and altering gait patterns. Early treatments for pain relief include physical therapy, pharmaceuticals and modified footwear. If these treatments fail to provide relief and the arthritis progresses, joint function is largely lost (hence rigidus for end-stage disease progression). Concerning veterans, from 2008-2017, nationwide data from the Veterans Healthcare System yielded 87,798 patient visits for HR (1,897 in that same period for the VA Puget Sound Healthcare System), and the number of patient visits has consistently increased year to year from 9,396 visits nationwide in 2008 to 22,180 visits in 2017. In order to properly match veteran HR patients with proper treatment, the stage of the disease progression needs to be properly classified. The most common classification systems use radiographic findings. Typically, AP and lateral X-ray views are evaluated for joint space (reduced space indicates lost cartilage), osteophytes (and indicator of arthritis), and joint shape (as HR patients preferentially lose cartilage and gain osteophytes on the dorsal surface). Yet the exact features evaluated, and how they are considered, is not standardized. This issue is further complicated by some grading schemes which blend clinical findings of range of motion and pain, into the already-complex field of radiographic features. Finally, while 2D X-rays are the standard for diagnosis, HR is a complex 3D disease and it has been hypothesized that certain bone positional (metatarsus primus elevatus) and morphological (articular surface shape) may be correlated with HR and possibly predispose patients to develop HR. Such measurements can be difficult to realize on 2D X-ray, and using manual and subjective measures. Therefore we propose to utilize existing weight-bearing CT data from control feet and obtain similar data from fifteen HR subjects. We will use this imaging data to evaluate the Intraclass Correlation associated with some of the more common HR classification schemes (Aim 1). This will allow us to establish reliability measures for the existing methods of clinically grading hallux rigidus, this will aid to inform the clinical community of the reliability of currently practiced diagnostic methods—which have a direct impact on treatment selection and patient care. We will also use our prior experience in evaluating 3D bony anatomy to semi-automate a suite of first ray positional (joint spacing, first metatarsal alignment, metatarsus primus elevatus) and morphological (sagittal plane metatarsal head curvature) features, which are suspected to have a correlation to HR (Aim 2). One of the greatest challenges associated with this work relate to developing a user-friendly environment by which clinicians can rapidly select bone features, which will algorithmically determined measurements for immediate reporting. To this end we will hire a research engineer with specific background in developing user interfaces and 3D digital object manipulation. This specialization is designed to generate a well-documented and polished analysis program which can be readily learned by clinicians. The other significant challenge is in determining the potential benefit of our proposed additional measurements in either enhancing or replacing the current classifications schemes. The benefit of these measurements will support our additional goal to lay the groundwork for either enhancing a current classification scheme (potentially by adding an additional measure) or developing a new classification scheme (via a new slate of semi-automated measurements) two project areas which this study will develop preliminary data for follow up with a future Merit Review.