Assessment of ultrasound features of knee osteoarthritis in a population-based community cohort

基于人群的社区队列中膝骨关节炎超声特征的评估

• 批准号: 10158441
• 负责人: Amanda E Nelson
• 金额: $ 33.18万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-05 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10158441
• 关键词: Address; African American; Age; Area; Assessment tool; Bilateral; Biochemical; Biological Markers; Bone Spur; Calcium; Cartilage; Categories; Characteristics; Chronic; Claustrophobias; Clinic; Clinical; Clinical Assessment Tool; Clinical Data; Clinical Research; Clinical Trials; Cohort Studies; Communities; County; Crystal Formation; Crystallization; Data; Data Set; Degenerative polyarthritis; Development; Diagnostic radiologic examination; Discrimination; Disease; Enrollment; Ethnic Origin; Etiology; Foundations; Future; General Population; Goals; Health; Hispanics; Image; Implant; Individual; Infrastructure; Intervention; Joints; Knee; Knee Osteoarthritis; Machine Learning; Magnetic Resonance Imaging; Measures; Meniscus structure of joint; Mission; Modality; Musculoskeletal; National Institute of Arthritis and Musculoskeletal and Skin Diseases; Nature; Outcome; Pain; Participant; Pathology; Performance; Phase; Phenotype; Physical activity; Popliteal Cyst; Population; Population Study; Prevalence; Public Health; Race; Receiver Operating Characteristics; Research; Risk Factors; Sex Differences; Specialist; Standardization; Subgroup; Symptoms; Syndrome; Synovial Membrane; Synovitis; Testing; Time; Tissues; Ultrasonography; Variant; Woman; Work; aged; base; bone; cohort; cost; cost effective; effusion; follow-up; imaging genetics; imaging modality; individual variation; interest; machine learning method; men; novel; point of care; population based; recruit; rheumatologist; sex; uptake; Address; African American; Age; Area; Assessment tool; Bilateral; Biochemical; Biological Markers; Bone Spur; Calcium; Cartilage; Categories; Characteristics; Chronic; Claustrophobias; Clinic; Clinical; Clinical Assessment Tool; Clinical Data; Clinical Research; Clinical Trials; Cohort Studies; Communities; County; Crystal Formation; Crystallization; Data; Data Set; Degenerative polyarthritis; Development; Diagnostic radiologic examination; Discrimination; Disease; Enrollment; Ethnic Origin; Etiology; Foundations; Future; General Population; Goals; Health; Hispanics; Image; Implant; Individual; Infrastructure; Intervention; Joints; Knee; Knee Osteoarthritis; Machine Learning; Magnetic Resonance Imaging; Measures; Meniscus structure of joint; Mission; Modality; Musculoskeletal; National Institute of Arthritis and Musculoskeletal and Skin Diseases; Nature; Outcome; Pain; Participant; Pathology; Performance; Phase; Phenotype; Physical activity; Popliteal Cyst; Population; Population Study; Prevalence; Public Health; Race; Receiver Operating Characteristics; Research; Risk Factors; Sex Differences; Specialist; Standardization; Subgroup; Symptoms; Syndrome; Synovial Membrane; Synovitis; Testing; Time; Tissues; Ultrasonography; Variant; Woman; Work; aged; base; bone; cohort; cost; cost effective; effusion; follow-up; imaging genetics; imaging modality; individual variation; interest; machine learning method; men; novel; point of care; population based; recruit; rheumatologist; sex; uptake

Project summary Our long-term goal is to demonstrate the utility of ultrasound for OA assessment, standardize its acquisition and scoring, and promote increased uptake of US for use in clinical, research, and trial settings. Knee osteoarthritis (KOA) is highly prevalent and frequently debilitating. Development of potential treatments has been hampered by the heterogenous nature of this common chronic condition, which is characterized by a number of subgroups, or phenotypes, with different underlying pathophysiological mechanisms. Imaging, genetics, biochemical biomarkers, and other features can be used to characterize phenotypes, but variations in data types can make it difficult to harmonize definitions. While radiography is widely used in KOA imaging, it is limited in its ability to assess early disease (when interventions are most likely to succeed) and is insensitive to change. Ultrasound (US) is a widely accessible, time-efficient and cost-effective imaging modality that can provide detailed and reliable information about all joint tissues (e.g., cartilage, meniscus, synovium, bone), and could therefore inform phenotypes in KOA (e.g., by presence of synovitis, effusion, cartilage damage, calcium crystal deposition, and popliteal cysts). Use of US is currently limited by the lack of systematically performed studies in well-characterized non-clinical populations. To address this gap and further the use of this advantageous imaging modality for KOA, we will obtain standardized US and radiography in the population- based Johnston County Health Study (JoCoHS), the new enrollment phase of the 25+ year Johnston County OA Project which includes white, African American, and Hispanic men and women aged 35-70, to achieve three aims. In Aim 1, we will determine the population prevalence (n~3000) of knee US features including cartilage and meniscal damage, synovitis/effusion, calcium crystal deposition, popliteal cysts and osteophytes overall and in key subgroups by age, sex, race/ethnicity, and symptom status. Aim 2 will allow quantification of the associations between these US features and radiographic findings and symptom scores overall and in key subgroups (e.g., those with and without radiographic KOA, by sex, by race/ethnicity). For Aim 3, we will apply novel machine learning methodologies (e.g., Direction-projection-permutation [DiProPerm] hypothesis testing, Joint and Individual Variation [JIVE], and Distance-Weighted Discrimination [DWD]) to a) develop an overall US score for symptomatic KOA and b) identify the contribution of US variables to phenotypes relevant to KOA based on general health, physical activity, and functional assessments. This study is a crucial step to establish the foundation for US as an assessment tool for clinical use, research, and clinical trials in KOA, providing unique population-based cross-sectional data regarding the utility of US and forming the basis for future longitudinal work evaluating its value and performance characteristics related to incident and progressive KOA.

项目摘要 我们的长期目标是证明超声进行OA评估的实用性，标准化其收购 和评分，并促进我们在临床，研究和试验环境中使用的吸收。膝盖 骨关节炎（KOA）高度普遍，经常使人衰弱。潜在治疗的发展 受到这种常见慢性病的异质性的阻碍，其特征是 亚组或表型的数量，具有不同的基本病理生理机制。成像， 遗传学，生化生物标志物和其他特征可用于表征表型，但变化的变化 数据类型可以使定义很难统一。虽然X射线照相被广泛用于KOA成像，但它是 其评估早期疾病的能力有限（当干预措施最有可能成功时），对 改变。超声（美国）是一种广泛访问，时间效率且具有成本效益的成像方式，可以 提供有关所有关节组织（例如软骨，半月板，滑膜，骨）的详细信息 因此，可以为KOA中的表型提供信息（例如，通过滑膜炎，积液，软骨损伤，钙 晶体沉积和popliteal囊肿）。目前，我们的使用受到系统性缺乏的限制 在良好的非临床人群中进行的研究。解决这一差距，并进一步使用此差距 KOA的有利成像方式，我们将在人群中获得标准化的US和X射线照相。 约翰斯顿县健康研究（Jocohs），约翰斯顿县25岁以上的新入学阶段 OA项目，包括白人，非裔美国人和西班牙裔男女35-70岁，以实现 三个目标。在AIM 1中，我们将确定膝盖美国特征的人口流行（n〜3000） 软骨和半月板损伤，滑膜炎/积液，钙晶体沉积，popliteal囊肿和骨植物 总体和关键亚组按年龄，性别，种族/种族和症状状况。 AIM 2将允许量化 这些美国特征与射线照相发现与症状得分的整体和关键的关联 亚组（例如，有和没有射线照相的人，通过性别，种族/族裔）。对于AIM 3，我们将申请 新颖的机器学习方法（例如，方向预测 - 渗透[二倍体]假设测试， 关节和个人变化[JIVE]，以及距离加权歧视[DWD]） 美国有症状的KOA和b）确定美国变量对与KOA相关的表型的贡献 基于一般健康，体育活动和功能评估。这项研究是建立的关键步骤 我们作为KOA的临床使用，研究和临床试验的评估工具的基础，提供 关于美国实用性并为未来构成基础的独特基于人群的横截面数据 纵向工作评估其与事件和进行性KOA有关的价值和绩效特征。