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; 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，表现出巨大的社会负担。膝盖 膝盖OA患者的疼痛是造成身体残疾的主要因素，也是医院的主要原因 访问。对膝盖疼痛病因的一种改进的了解，部分受到了膝关节的阻碍 整个关节的多因素和进行性疾病；因此，膝盖疼痛的进展可能是 随着时间的流逝，几种不同结构特征的局部或区域异常的结果。长期目标是 加快进入临床试验的最佳筛查开发以测试有希望的 症状改善的治疗方法。该应用程序的总体目的是研究 不同的基于MRI的特征以及各种膝盖疼痛测量的时间模式（例如，膝盖疼痛 OA中的频率和严重性）。中心假设是有一些时间膝关节疼痛表型 以及各种MRI定义的结构特征（例如骨髓病变）与表型有关。 该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（大多数），半定量（SQ）读数，复杂的膝盖 OAI和大多数研究中的疼痛测量以及机器/深度学习的项目，以准确 预测在OAI和大多数的MRI的SQ读数 研究。中心假设将通过追求两个具体目标来检验：1）确定不同的时间膝关节 基于所有可用纵向膝盖疼痛测量和相关膝盖疼痛风险的疼痛表型 最多的因素和OAI； 2）将基线时MRI定义的结构特征与 鉴定出颞膝疼痛表型。本应用程序中提出的研究是创新的 方式。它考虑了膝盖疼痛的各种定义和超级疼痛测量数据 大型纵向OAI和大多数研究并应用机器学习，深度学习和统计方法 确定膝盖疼痛表型并将其与基于MRI的因素相关联。这个新的和实质性的 预计理解膝盖疼痛的不同方法将克服现有研究的局限性 （例如，单膝疼痛测量和横截面研究），从而为 检测不同的时间膝盖疼痛表型，并允许识别具有高风险的个体 各种颞膝疼痛表型，以便将更多针对性的入学率用于临床试验。