Novel Strategies for Personalized Clinical Decisions in Knee Arthroplasty

膝关节置换术个性化临床决策的新策略

• 批准号: 10227895
• 负责人: Jennifer E. Stevens-Lapsley
• 金额: $ 37.41万
• 依托单位: UNIVERSITY OF COLORADO DENVER
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10227895
• 关键词: Novel; Strategies; Personalized; Clinical; Decisions

Abstract This proposal seeks to address two major problems in the delivery of care surrounding Total Knee Arthroplasty (TKA): 1) patients are under-informed regarding the trajectory and timing of postoperative recovery following this elective procedure, and 2) post-acute care is typically delivered according to one- size-fits-all protocols, which are inadequate for decision-making with individual patients. Our work explores a “patients-like-me” approach to guide decision-making surrounding TKA. Briefly, for any new patient considering TKA, outcomes data from similar historical patients can be used to create a personalized reference chart (PRC) to describe the anticipated recovery profile of the new patient. The method for selecting patients-like-me is a novel extension of multiple imputation (predictive mean matching). With the PRC, clinicians and patients can more precisely judge whether recovery is proceeding better than, worse than, or just as expected, compared to a patient's peers. Deviations from the expected trajectory can be rapidly detected and addressed. Patients can be better informed regarding prognosis, and resources can be more efficiently allocated: more visits for those whose recovery is lagging; fewer visits for those excelling. With this proposal, we propose to bring the PRC innovation into practice. First (Aim 1), we will develop procedures for optimizing and validating PRC algorithms for 3 important functional outcomes following TKA. Second (Aim 2), we will incorporate these algorithms into a software application capable of producing PRCs at the point of care in routine practice. Finally (Aim 3), we will test preliminary efficacy of PRCs in improving functional outcomes following TKA as well as the quality of shared decision making and other outcomes such as post-acute care utilization. Following completion of this work, we will be positioned to 1) conduct a larger cluster-randomized trial to formally test the effectiveness of PRC-informed care pathway, and 2) make a web-based PRC application available for widespread use. Ultimately, we foresee PRC methodology as a means of advancing personalized medicine for a number of diverse patient populations. Our team includes clinical experts (Dr. Dawn Waugh, PT, and Dr. Michael Dayton, MD), experts in analytics (Dr. Kathryn Colborn, PhD and Dr. Stef van Buuren, PhD), an expert in shared decision making and implementation science (Dr. Daniel Matlock, MD, MPH) and experts in clinical research for TKA (Dr. Jennifer Stevens-Lapsley, PT, PhD and Dr. Andrew Kittelson, PT, PhD). This research responds to AHRQ priorities. It advances care for a major health condition (TKA), in a priority population (older adults), by providing a novel framework for shared decision-making. PRCs represent a paradigm shift from traditional one-size-fits-all approaches, with opportunities for higher quality, tailored care for individuals and more efficient resource allocation in a learning healthcare system.

抽象的 该提案旨在解决全膝关节护理中的两个主要问题 关节置换术 (TKA)：1) 患者对术后的轨迹和时间了解不足 此选择性程序后的恢复，以及 2) 急性后护理通常根据以下方式提供： 我们的工作探索了放之四海而皆准的方案，这些方案不足以做出针对个体患者的决策。 为任何新患者提供“像我一样的患者”的方法来指导 TKA 决策。 考虑 TKA，来自类似历史患者的结果数据可用于创建个性化的治疗方案 参考图（PRC）描述新患者的预期恢复情况的方法。 选择像我这样的患者是多重插补（预测均值匹配）的新扩展。 PRC、圣人和病人可以更准确地判断康复进展是好还是坏 与患者的同龄人相比，或正如预期的那样，可能会偏离预期的轨迹。 患者可以更好地了解预后并获得资源。 更有效地分配：对恢复滞后的人进行更多的探访；对表现良好的人进行更少的探访。 通过此提案，我们建议首先将中国的创新付诸实践（目标 1）。 开发优化和验证 PRC 算法的程序，以实现 3 个重要的功能结果 继 TKA 后（目标 2），我们将把这些算法整合到能够执行以下操作的软件应用程序中： 最后（目标 3），我们将测试初步疗效。 PRCs 改善 TKA 后的功能结果以及共同决策和决策的质量 完成这项工作后，我们将定位其他结果，例如急性后护理利用。 1) 进行更大规模的整群随机试验，以正式测试 PRC 知情护理的有效性 路径，以及 2）最终使基于网络的 PRC 应用程序可供广泛使用。 PRC 方法作为为许多不同患者推进个性化医疗的一种手段 我们的团队包括临床专家（Dawn Waugh 博士，PT 和 Michael Dayton 博士）， 分析专家（Kathryn Colborn 博士和 Stef van Buuren 博士），共享决策专家 TKA 的制定和实施科学（Daniel Matlock 博士，医学博士、公共卫生硕士）和临床研究专家 （Jennifer Stevens-Lapsley 博士，PT，PhD 和 Andrew Kittelson 博士，PT，PhD）。 这项研究响应了 AHRQ 的优先事项，它促进了对重大健康状况 (TKA) 的护理。 优先人群（老年人），通过提供新的共同决策框架。 代表了传统一刀切方法的范式转变，并提供了更高质量的机会， 为个人提供量身定制的护理，并在学习医疗保健系统中更有效地分配资源。