The Morphology and Characteristics of Hallux Rigidus

拇趾关节炎(HR)是拇趾骨关节炎的末期，是一种退行性关节疾病，影响 大约每40个人中就有一个在50岁以上。第一跖趾关节炎的发生发展 关节(MTPJ)疼痛，影响生活质量，降低活动度，改变步态模式。早期治疗 疼痛缓解包括物理治疗、药物和改良的鞋类。如果这些治疗不能提供 缓解和关节炎的进展，关节功能大部分丧失(因此僵硬的终末期疾病进展)。 关于退伍军人，从2008年到2017年，来自退伍军人医疗系统的全国数据产生了87,798人 HR的病人就诊次数(同期为VA Puget Sound医疗系统的1,897人次)，以及 就诊人次逐年增长，从2008年的9396人次增加到2017年的22180人次。 为了使退伍军人HR患者与适当的治疗相匹配，疾病的进展阶段需要 被适当地归类。 最常见的分类系统使用放射学检查结果。通常，AP和侧位X光视图是 评估关节间隙(减少的间隙表示软骨丢失)、骨赘(和关节炎的指标)，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。这个问题进一步复杂化了，因为 一些将运动范围和疼痛的临床结果融合到已经很复杂的领域的分级方案 放射影像特征。最后，虽然2D X光是诊断的标准，但HR是一种复杂的3D疾病 并假设某些骨骼的位置和形态 (关节表面形状)可能与HR相关，并可能使患者更容易发生HR。是这样的 在2DX光上，使用人工和主观测量可能很难实现测量。 因此，我们建议利用来自对照足部的现有承重CT数据，并从 15名HR受试者。我们将使用这些成像数据来评估与以下一些相关的类内相关性 更常见的人力资源分类办法(目标1)。这将使我们能够为 现有的拇指僵硬临床分级方法，这将有助于告知临床社区可靠性 目前实行的诊断方法--这些方法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骨骼解剖方面的经验来半自动处理一套第一射线 位置(关节间距、第一跖骨对齐、跖骨原始隆起)和形态(矢状面 被怀疑与心率相关的(目标2)特征。 与这项工作相关的最大挑战之一是通过以下方式开发一个用户友好的环境 哪些临床医生可以快速选择骨骼特征，这些特征将通过算法确定测量 立即报告。为此，我们将聘请一名具有特定用户开发背景的研究工程师 界面和3D数字对象操作。此专业化认证旨在生成文档齐全的 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加强或取代 当前的分类方案。这些测量的好处将支持我们的额外目标，即 增强当前分类方案的基础工作(可能通过增加额外的措施) 或者开发新的分类方案(通过一系列新的半自动测量)两个项目领域 这项研究将为今后的功绩审查提供初步数据。

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.