Precision Medicine Approach to Exercise-Based Interventions for Veterans with Knee Osteoarthritis

对患有膝骨关节炎的退伍军人进行基于运动的干预的精准医学方法

• 批准号: 10640577
• 负责人: Kelli D. Allen
• 金额: --
• 依托单位: DURHAM VA MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10640577
• 关键词: Arthritis; Caring; Characteristics; Data; Degenerative polyarthritis; Development; Effectiveness; Evidence based intervention; Exercise; Foundations; Funding; Goals; Health Services Accessibility; Health education; Healthcare; Healthcare Systems; Heterogeneity; Home; Human Resources; Individual; Intervention; Knee Osteoarthritis; Machine Learning; Measurement; Measures; Methodology; Methods; Mission; Modeling; Nature; Outpatients; Pain; Participant; Patients; Persons; Physical Medicine; Physical Rehabilitation; Physical therapy; Process; Quality of Care; Quality of life; Randomized, Controlled Trials; Reporting; Research; Resources; Rural Health; Science; Self Direction; Services; Symptoms; Testing; Treatment outcome; Trees; United States Department of Veterans Affairs; Veterans; Veterans Health Administration; Western Ontario and McMaster Universities Arthritis Index; Work; arm; clinically relevant; cost; cost effectiveness; design; disability; effectiveness testing; exercise intervention; exercise program; experience; improved; indexing; individual patient; innovation; machine learning algorithm; machine learning model; novel; opioid use; patient oriented; patient subsets; pragmatic trial; precision medicine; programs; randomized, clinical trials; rehabilitation service; response; standard of care; supervised learning; treatment effect; treatment guidelines; treatment strategy; trial comparing

Background: Knee osteoarthritis (OA) is a leading cause of pain and disability, and Veterans have markedly greater rates than non-Veterans. Exercise is a core component of care for knee OA, associated with modest average improvements in pain and function. However, there is tremendous variability in the degree of improvement individual patients experience following exercise-based interventions for knee OA. Further, there are different types of exercise-based interventions for knee OA, ranging from self-directed programs to individual physical therapy (PT), and it is likely that any given patient will not experience the same magnitude of response to each of these different approaches. The overall objective of our research is to improve the effectiveness, efficiency and patient-centeredness of exercise-based services for Veterans with knee OA through a precision medicine approach that matches the intervention type with key patient characteristics. Significance / Impact: There is currently no guidance or evidence regarding which patients benefit most from different exercise-based interventions for knee OA. Thus, there is no clarity regarding which patients should be directed to different types of exercise-based services. Innovation: This will be the first study to examine heterogeneity of treatment effects in the context of different exercise-based interventions among Veterans with knee OA. Methods will involve novel, robust machine learning analyses. Specific Aims: 1. Develop a precision medicine treatment strategy that optimizes improvement in pain, stiffness and function, measured by the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), by tailoring exercise-based treatment to individual patients with knee OA. Aim 1.a. Use a causal inference-based machine learning (ML) approach to estimate patient-specific estimates of treatment outcomes (improvement in WOMAC scores) for each of the four treatments (Group PT, Individual PT, STEP-KOA, and health education control). Aim 1.b. Apply uplift tree ML modeling to produce an interpretable tree-based model for optimal assignment of treatment for knee OA. Aim 2. Apply results of ML analyses, along with a robust partner-engaged development process, to design a randomized clinical trial (RCT) that will test the effectiveness and cost-effectiveness of a precision medicine approach to delivering exercise-based interventions to Veterans with knee OA. Methodology: This project will involve analysis of two VA RCTs. One RCT compared Group vs. Individual PT and found comparable overall mean improvements in pain and function. The second RCT examined the STEP- KOA intervention, which begins with home-based exercise and progresses to PT only if participants do not make clinically relevant improvements; STEP-KOA was also associated with mean improvements in pain and function. Individual PT, Group PT, and STEP-KOA are all evidence-based interventions for knee OA, varying in the amount and type of support provided to patients and therefore the associated costs to the VA. We are preparing to scale Group PT and STEP-KOA in the VA. However, because Veterans in our RCTs varied substantially in their degree of improvement following these interventions, we believe these programs will ultimately be of much higher value to the VA and Veterans if we are able to target their delivery using a precision medicine approach. In this project, we will apply robust ML approaches to uncover subgroups of patients who benefit most (and least) from Individual PT, Group PT and STEP-KOA. Next Steps / Implementation: Study results will directly inform an RCT that will test whether a precision medicine approach is more effective than a “one size fits all” approach. Specifically, we plan for a 2-arm pragmatic trial that will compare Individual PT for all patients (current standard of care) with a precision medicine arm that assigns patients to one of three exercise-based interventions, based on key characteristics.

背景：膝骨关节炎 (OA) 是疼痛和残疾的主要原因，退伍军人明显患有膝骨关节炎 (OA)。 比非退伍军人更高的比率。运动是膝关节 OA 护理的核心组成部分，与适度的锻炼相关 疼痛和功能的平均改善。但其程度存在巨大差异 通过基于运动的膝关节骨关节炎干预措施，个体患者的体验得到改善。此外，还有 是针对膝关节 OA 的不同类型的基于运动的干预措施，范围从自我指导计划到 个体物理治疗 (PT)，任何特定患者都可能不会经历相同程度的治疗 对每种不同方法的反应。我们研究的总体目标是改善 为患有膝骨关节炎的退伍军人提供基于运动的服务的有效性、效率和以患者为中心 通过将干预类型与关键患者特征相匹配的精准医学方法。 意义/影响：目前没有关于哪些患者从中受益最多的指导或证据 针对膝关节骨关节炎的不同基于运动的干预措施。因此，尚不清楚哪些患者应该 针对不同类型的基于运动的服务。 创新：这将是第一项研究不同背景下治疗效果异质性的研究 对患有膝骨关节炎的退伍军人进行基于运动的干预。方法将涉及新颖、坚固的机器 学习分析。 具体目标： 1. 制定精准医疗治疗策略，优化疼痛改善， 硬度和功能，通过西安大略大学和麦克马斯特大学骨关节炎指数测量 (WOMAC)，通过为膝关节骨关节炎患者量身定制基于运动的治疗。目标 1.a.使用因果关系 基于推理的机器学习 (ML) 方法来估计患者特定的治疗结果估计值 四种治疗（组 PT、个体 PT、STEP-KOA 和 健康教育控制）。目标 1.b。应用提升树 ML 建模来生成可解释的基于树的模型 膝关节骨关节炎治疗的最佳分配。目标 2. 应用机器学习分析的结果以及稳健的 合作伙伴参与的开发流程，设计一项随机临床试验（RCT）来测试 精准医学方法提供基于运动的有效性和成本效益 对患有膝骨关节炎的退伍军人进行干预。 方法：该项目将涉及两项 VA RCT 的分析。一项随机对照试验比较了组与个体 PT 并发现疼痛和功能的整体平均改善具有可比性。第二个 RCT 检查了 STEP- KOA 干预，从家庭锻炼开始，只有当参与者不这样做时才进行 PT 进行临床相关的改进； STEP-KOA 还与疼痛和疼痛的平均改善有关 功能。个体 PT、团体 PT 和 STEP-KOA 都是针对膝关节 OA 的循证干预措施，各有不同 向患者提供的支持的数量和类型，以及退伍军人管理局的相关费用。我们是 准备在 VA 扩大 Group PT 和 STEP-KOA。然而，由于我们的随机对照试验中的退伍军人各不相同 就这些干预措施后的改善程度而言，我们相信这些计划将 如果我们能够利用 精准医学方法。在这个项目中，我们将应用强大的机器学习方法来发现子组 从个体 PT、团体 PT 和 STEP-KOA 中获益最多（和最少）的患者。 后续步骤/实施：研究结果将直接告知随机对照试验，该随机对照试验将测试精度是否 医学方法比“一刀切”方法更有效。具体来说，我们计划使用 2 臂 实用性试验，将比较所有患者的个体 PT（当前护理标准）与精确度 医学部门根据关键特征为患者分配三种基于运动的干预措施之一。