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：膝关节骨关节炎(OA)是导致疼痛和残疾的主要原因，退伍军人明显 比非退伍军人的比率更高。锻炼是膝盖骨关节炎护理的核心组成部分，与适度 疼痛和功能的平均改善。然而，在程度上存在着巨大的差异 膝骨性关节炎运动干预后患者个体体验的改善。此外，还有 对膝骨性关节炎有不同类型的基于运动的干预措施，从自我指导的计划到 个人物理治疗(PT)，很可能任何给定的患者都不会经历同样的程度 对每一种不同方法的反应。我们研究的总体目标是改善 退伍军人膝骨性关节炎运动服务的有效性、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：目前还没有关于哪些患者受益最大的指导意见或证据 膝关节骨性关节炎的不同运动干预。因此，不清楚哪些患者应该 针对不同类型的以锻炼为基础的服务。 创新：这将是第一项在不同背景下检验治疗效果异质性的研究 退伍军人膝骨性关节炎患者的运动干预。方法将涉及新的、健壮的机器 学习分析。 具体目标：1.制定优化疼痛改善的精准药物治疗策略， 由西安大略大学和麦克马斯特大学骨关节炎指数测量的僵硬和功能 (WOMAC)，为膝骨性关节炎患者量身定做基于运动的治疗。目标1.A。使用因果关系 基于推理的机器学习(ML)方法估计特定患者的治疗结果 (WOMAC评分的改善)四种治疗方法(PT组、单独PT、STEP-KOA和 健康教育对照)。目标1.b.应用提升树ML建模以产生可解释的基于树的模型 对膝骨性关节炎的治疗进行最佳分配。目标2.应用最大似然分析的结果，以及稳健的 合作伙伴参与的开发过程，以设计一项随机临床试验(RCT)，以测试 提供以运动为基础的精确医学方法的有效性和成本效益 退伍军人膝骨性关节炎的干预。 方法：本项目将涉及对两个退伍军人随机对照试验的分析。一项随机对照试验比较小组与个人PT 并发现疼痛和功能的总体平均改善程度相当。第二个RCT检查了这一步骤-- KOA干预，开始于基于家庭的锻炼，只有在参与者不这样做的情况下才会进行PT 临床上相关的改善；阶梯式膝关节骨性关节炎也与疼痛和 功能。单独PT、组PT和STEP-KOA都是膝骨性关节炎的循证干预措施，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 准备在退伍军人管理局扩大PT集团和STEP-KOA的规模。然而，因为我们RCT中的退伍军人不同 在这些干预措施之后，他们的改善程度大大提高，我们相信这些计划将 最终，对于退伍军人和退伍军人来说，如果我们能够使用 精准医学方法。在这个项目中，我们将应用健壮的ML方法来发现 从单独PT、PT组和STEP-KOA获益最多(和最少)的患者。 下一步/实施：研究结果将直接通知随机对照试验，该试验将测试精确度 医学方法比“一刀切”的方法更有效。具体地说，我们计划推出一款双臂 将所有患者的个别PT(当前护理标准)与精确度进行比较的实用试验 根据关键特征，将患者分配到三种基于运动的干预措施中的一种。