Identifying determinants of rapid structural and/or clinical progression in knee osteoarthritis by quantitative assessment of structural features on radiographs

通过定量评估射线照片上的结构特征来确定膝骨关节炎快速结构和/或临床进展的决定因素

• 批准号: 10859277
• 负责人: JEFFREY W DURYEA
• 金额: $ 40.2万
• 依托单位: UNIVERSITY OF ARIZONA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-07 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10859277
• 关键词: Acceleration; Administrative Supplement; Algorithms; Area; Bone Marrow; Bone Spur; Clinical; Clinical Trials; Clinical Trials Design; Complex; Cross-Sectional Studies; Data; Data Set; Degenerative polyarthritis; Development; Disease; Enrollment; Etiology; Frequencies; Future; Goals; Grant; Hospitals; Image; Individual; Intervention; Joints; Knee; Knee Osteoarthritis; Lesion; Longitudinal cohort study; Longitudinal, observational study; Machine Learning; Magnetic Resonance Imaging; Mission; Modeling; Musculoskeletal Diseases; Pain; Pain Measurement; Pain Research; Patients; Pattern; Pharmaceutical Preparations; Phenotype; Progressive Disease; Reporting; Research; Rheumatism; Risk Factors; Severities; Sex Differences; Standardization; Statistical Methods; Structure; Synovitis; Testing; Time; Tissues; U-Series Cooperative Agreements; United States National Institutes of Health; Visit; cohort; data reuse; deep learning; deep learning model; disability; effusion; falls; high risk; improved; individualized medicine; innovation; interest; knee pain; learning strategy; novel; parent grant; patient subsets; pharmacologic; physically handicapped; prevent; psychosocial; racial difference; radiologist; screening; secondary analysis; skin disorder; social determinants; symptom treatment; symptomatic improvement

Osteoarthritis (OA) is the most common musculoskeletal disorder and presents a large societal burden. Knee pain in patients with knee OA is a leading contributor to physical disability and a major reason for hospital visits. An improved understanding of the etiology of knee pain has been hampered in part by knee OA being a multifactorial and progressive disease of the whole joint; consequently, knee pain progression may be the result of local or regional abnormalities of several different structural features over time. The long-term goal is to accelerate the development of optimal screening for enrollment into clinical trials to test promising treatments for symptom improvement. The overall objective in this application is to study the association of different MRI-based features with the temporal patterns of various knee pain measurements (e.g., knee pain frequency and severity) in OA. The central hypothesis is that there are some temporal knee pain phenotypes and various MRI-defined structural features (e.g., bone marrow lesions) are associated with the phenotypes. This hypothesis is formulated largely based on the preliminary studies, including the Osteoarthritis Initiative (OAI), the Multicenter Osteoarthritis Study (MOST), the semi-quantitative (SQ) readings, the complex knee pain measurements in the OAI and MOST studies, and projects on machine/deep learning to accurately predict SQ readings for MRIs that do not have existing radiologist-derived readings in the OAI and MOST studies. The central hypothesis will be tested by pursuing two specific aims: 1) identify different temporal knee pain phenotypes based on all available longitudinal knee pain measurements and the related knee pain risk factors in the MOST and OAI; and 2) associate the MRI-defined structural features at baseline with the identified temporal knee pain phenotypes. The research proposed in this application is innovative in several ways. It considers various definitions of knee pain and the available pain measurement data in the super- large longitudinal OAI and MOST studies and applies machine learning, deep learning and statistical methods to identify knee pain phenotypes and associate them with MRI-based factors. This new and substantively different approach to understanding knee pain is expected to overcome the limitations of existing studies (e.g., single knee pain measurement-based and cross-sectional studies), thereby opening new horizons for detecting different temporal knee pain phenotypes and allowing identification of individuals at high risk of various temporal knee pain phenotypes for more targeted enrollment into clinical trials.

骨关节炎（OA）是最常见的肌肉骨骼疾病，并提出了一个巨大的社会负担。膝 膝关节OA患者的疼痛是导致身体残疾的主要因素，也是住院的主要原因 探访对膝关节疼痛病因学的进一步了解受到阻碍，部分原因是膝关节OA是一种 全关节的多因素和进行性疾病;因此，膝关节疼痛进展可能是 随着时间的推移，几种不同结构特征的局部或区域异常的结果。长期目标是 加快发展最佳筛选入组临床试验，以测试有前途的 治疗症状改善。本申请的总体目标是研究 具有各种膝关节疼痛测量的时间模式的不同的基于MRI的特征（例如，膝关节疼痛 频率和严重程度）。中心假设是，有一些颞膝关节疼痛表型 以及各种MRI定义的结构特征（例如，骨髓病变）与表型相关。 这一假设主要基于初步研究，包括骨关节炎倡议 (OAI)、多中心骨关节炎研究（MOST）、半定量（SQ）读数、复杂膝关节 OAI和MOST研究中的疼痛测量，以及机器/深度学习项目， 预测OAI和MOST中没有现有放射科医生读数的MRI的SQ读数 问题研究中心假设将通过追求两个具体目标来检验：1）识别不同的颞膝关节 基于所有可用的纵向膝关节疼痛测量和相关膝关节疼痛风险的疼痛表型 MOST和OAI中的因素;以及2）将基线时MRI定义的结构特征与 确定颞膝关节疼痛表型。本申请中提出的研究在几个方面具有创新性 的方式它考虑了膝关节疼痛的各种定义和超级疼痛测量数据， 大型纵向OAI和MOST研究和应用机器学习、深度学习和统计方法 确定膝关节疼痛表型，并将其与基于MRI的因素相关联。这一新的和实质性的 一种不同的方法来理解膝关节疼痛，有望克服现有研究的局限性 (e.g.,单膝关节疼痛测量和横断面研究），从而为 检测不同的暂时性膝关节疼痛表型，并允许识别处于高风险的个体， 各种颞膝关节疼痛表型，以便更有针对性地纳入临床试验。