Mentoring in Patient Oriented Research in Osteoarthritis

以患者为导向的骨关节炎研究的指导

• 批准号: 10689126
• 负责人: Amanda E Nelson
• 金额: $ 19.66万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-25 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10689126
• 关键词: Accelerometer; Address; Age; Age Years; Algorithms; Area; Artificial Intelligence; Award; Big Data Methods; Biological Markers; Black race; Chronic Disease; Clinical Data; Clinical Investigator; Clinical Research; Cohort Studies; Computational algorithm; County; Creatinine; Data; Data Sources; Degenerative polyarthritis; Development; Disease; Enrollment; Epidemiology; Ethnic Origin; Event; Funding; Future; Gait speed; Glean; Goals; Health; Healthcare Systems; Hispanic; Image; Individual; Inequity; Infrastructure; Institution; Intervention; Joints; Knee; Knee Osteoarthritis; Longitudinal Studies; Machine Learning; Measures; Medical; Mentors; Methodology; Methods; Midcareer Investigator Award in Patient-Oriented Research; Mission; Modeling; Musculoskeletal Diseases; National Institute of Arthritis, and Musculoskeletal, and Skin Diseases; Outcome; Pain; Patient Self-Report; Patients; Performance; Persons; Phenotype; Physical activity; Population Heterogeneity; Publishing; Pulmonary function tests; Questionnaires; Race; Research; Research Methodology; Research Personnel; Research Project Grants; Resources; Rheumatism; Risk; Risk Factors; Role; Sampling; Scientist; Self Assessment; Sex Differences; Socioeconomic Status; Structure; Subgroup; Symptoms; Testing; Therapeutic; Time; Training; Validation; Woman; Work; aged; algorithm development; artificial intelligence algorithm; career; clinical center; cohort; deep learning algorithm; diversity and equity; effective therapy; epidemiology study; experience; health equity; health inequalities; improved; indexing; individual variation; insight; interest; knee pain; men; novel; osteoarthritis pain; pain catastrophizing; patient oriented research; population based; precision medicine; programs; racial difference; radiological imaging; sex; skills; socioeconomics

Project summary This K24 Midcareer Investigator Award in Patient Oriented Research (POR) will leverage my existing POR program, enhance my professional development and that of my trainees, and support my ongoing efforts to provide essential mentoring to early stage clinical investigators in POR in the rheumatic and musculoskeletal diseases (RMDs), with a focus on osteoarthritis (OA) and health equity. Despite being a highly prevalent and serious disease, OA lacks effective therapies and remains a large burden on individuals and the health care system. We have published extensively on sex, race, and socioeconomic differences in various aspects of OA based on our experience with two diverse population-based cohorts, the Johnston County OA Project (JoCoOA) and the Johnston County Health Study (JoCoHS). My POR career has focused on aspects of OA epidemiology, imaging, and biomarkers, with a more recent emphasis on identifying phenotypes using machine learning and precision medicine to improve studies and management of this chronic disease. It is essential to understand the role of artificial intelligence, including machine learning, in either improving or perpetuating health inequities such as those known to exist in OA. Such advanced approaches, if applied appropriately, will likely provide new insights on phenotypes of OA, and inform therapeutics in diverse populations with the objective of improving health equity. Further, the intersection of POR, health equity, and machine learning is of great interest to trainees and an area of critical need for training future clinician scientists in RMDs, which is the goal of the K24 mechanism. We will leverage existing data from the JoCoOA, a 30-year longitudinal study of over 4000 Black and White men and women aged 45 and older, and the new JoCoHS, an actively enrolling cohort (2019-, n~1500) that includes individuals who are 35-70 years of age and who identify as Hispanic, Black, or White. We will utilize these rich data sources and our extensive institutional resources to address two specific aims around subgroups of pain and symptoms and their associations with structure and function, with a focus on health equity. In Aim 1, we will work to validate and optimize a deep learning algorithm in these diverse cohorts to better define the association between radiographic features and the pain experience in knee OA. In Aim 2, we will characterize symptomatic phenotypes in the JoCoHS cohort (which has more extensive available symptomatic data), and associate these with the radiographic information gleaned in Aim 1 as well as validated assessments of function and physical activity, to identify subgroups that could most benefit from targeted interventions. This K24 project will provide crucial protected time and resources to promote my professional development and enhance my mentoring capabilities while simultaneously providing a springboard for trainees to 1) develop their research skills in the context of individualized projects, 2) develop critical skills in big data analytics and machine learning-based methodology, and 3) answer key POR questions in RMDs and OA through a diversity and health equity lens.

项目摘要 这项K24中级护理研究员以患者为导向研究（POR）将利用我现有的POR 计划，增强我的专业发展和学员的发展，并支持我持续的努力 为风湿性和肌肉骨骼的POR提供必不可少的指导 疾病（RMD），重点是骨关节炎（OA）和健康公平。尽管很普遍， 严重疾病，OA缺乏有效的疗法，并且对个人和医疗保健仍然承担很大负担 系统。我们已经在OA的各个方面发表了有关性，种族和社会经济差异的广泛发表 根据我们在两个基于人群的同伙的经验，约翰斯顿县OA项目 （Jocooa）和约翰斯顿县健康研究（Jocohs）。我的POR职业专注于OA的各个方面 流行病学，成像和生物标志物，最近强调使用机器识别表型 学习和精确医学，以改善对这种慢性疾病的研究和管理。至关重要 了解人工智能的作用，包括机器学习，改善或永久性 健康不平等，例如已知存在于OA中的不平等。这样的高级方法（如果适当地应用）将 可能会提供有关OA表型的新见解，并向不同人群的治疗剂提供信息 改善健康公平的目标。此外，POR，健康公平和机器学习的交集是 对学员的兴趣以及对培训未来临床医生在RMD的迫切需要的领域，这是 K24机制的目标。我们将利用Jocooa的现有数据，这是一项30年的纵向研究 超过450岁的45岁及45岁以上的黑白男女，以及新的Jocohs，一个积极的注册 队列（2019-，n〜1500），其中包括35-70岁并且被认为是西班牙裔的人 黑色或白色。我们将利用这些丰富的数据来源和我们广泛的机构资源来解决两个 围绕疼痛和症状的亚组及其与结构和功能的关联的具体目的 关注健康公平。在AIM 1中，我们将努力验证和优化这些深度学习算法 各种各样的人群可以更好地定义射线照相特征与膝盖疼痛体验之间的关联 OA。在AIM 2中，我们将表征Jocohs队列中的症状表型（其更广泛 可用的症状数据），并将其与AIM 1收集的X线照相信息以及 经过验证的功能和体育活动评估，以确定可以从中受益最大的亚组 有针对性的干预措施。这个K24项目将提供关键的保护时间和资源，以促进我 专业发展并增强我的指导能力，同时提供 为受训者提供的跳板至1）在个性化项目的背景下发展他们的研究技能，2） 大数据分析和基于机器学习的方法的关键技能，以及3）回答关键问题 在RMD和OA中，通过多样性和健康平等镜头。