Novel Strategies for Personalized Clinical Decisions in Knee Arthroplasty

摘要 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）患者对术后轨迹和时间了解不足 恢复后，这一选择性的程序，和2）急性后护理通常是根据一个提供- 一刀切的协议，这是不足以与个别病人的决策。我们的工作探索 一种“像我一样的患者”方法，用于指导围绕TKA的决策。简单地说，对于任何新病人 考虑到TKA，来自类似历史患者的结局数据可用于创建个性化的 参考图表（PRC），以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填补（预测均值匹配）的新扩展。与 PRC、临床医生和患者可以更准确地判断恢复是否进展得更好， 与病人的同龄人相比，或者说与预期的一样。与预期轨迹的偏差可能是 快速检测和处理。患者可以更好地了解预后，资源可以 更有效地分配：对那些恢复滞后的人进行更多的访问;对那些表现出色的人进行更少的访问。 通过这一建议，我们建议将中国的创新付诸实践。首先（目标1），我们将 为3个重要的功能结果开发优化和验证PRC算法的程序 在TKA之后。其次（目标2），我们将把这些算法纳入一个软件应用程序， 在常规实践中在护理点产生PRC。最后（目标3），我们将测试 PRC在改善TKA术后功能结局以及共同决策质量方面的作用， 其他结果，如急性期后护理利用率。完成这项工作后，我们将定位 1）进行更大规模的随机分组试验，正式测试PRC知情护理的有效性 途径，以及2）使基于Web的PRC应用程序可广泛使用。最终，我们预见 PRC方法学作为一种为不同患者推进个性化医疗的手段 人口数量。我们的团队包括临床专家（Dawn Waugh博士，PT和Michael代顿博士，MD）， 分析专家（Kathryn Colborn博士和Stef货车Buuren博士），共同决策专家 制定和实施科学（丹尼尔马特洛克博士，医学博士，公共卫生硕士）和TKA临床研究专家 (Dr. Jennifer Stevens-Lapsley，PT，PhD和Andrew Kittelson博士，PT，PhD）。 这项研究符合AHRQ的优先事项。它促进了对主要健康状况（TKA）的护理， 通过为共同决策提供一个新的框架，为重点人群（老年人）提供了一个新的框架。PRCS 代表着从传统的“一刀切”方法的范式转变，有机会实现更高质量， 为个人提供量身定制的护理，并在学习型医疗保健系统中实现更有效的资源分配。

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.