Novel Strategies for Personalized Clinical Decisions in Knee Arthroplasty

摘要 该提案旨在解决在提供全膝关节护理方面的两个主要问题 关节成形术(TKA)：1)患者对术后轨迹和时机知之甚少 在这一选择性手术后恢复，2)急诊后护理通常根据一项- 适用于所有患者的方案，不适用于个别患者的决策。我们的工作探索 “病人如我”的方法来指导围绕TKA的决策。简单地说，对于任何新患者 考虑到TKA，来自相似历史患者的结果数据可以用于创建个性化的 描述新患者预期恢复情况的参考图表(PRC)。本发明公开了一种用于 选择像我一样的病人是多重归因(预测均值匹配)的一种新的扩展。与 中华人民共和国，临床医生和患者可以更准确地判断康复进展是好还是差 与病人的同龄人相比，比预期的要好，或者正如预期的那样。与预期轨迹的偏差可能是 快速检测和寻址。患者可以更好地了解预后，资源可以 更有效的分配：为那些复苏滞后的人提供更多的访问；为那些表现出色的人提供更少的访问。 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 针对3个重要的功能结果制定优化和验证PRC算法的程序 在TKA之后。其次(目标2)，我们将把这些算法整合到一个软件应用程序中，该应用程序能够 在常规实践中，在护理点制作PRC。最后(目标3)，我们将测试 PRC在改善TKA后的功能结果以及共享决策和 其他结果，如急性后护理的利用情况。在这项工作完成后，我们将定位 1)进行更大规模的整群随机试验，正式测试PRC知情护理的有效性 途径，以及2)使基于网络的PRC应用程序可供广泛使用。最终，我们预见到 PRC方法学作为一种手段，为许多不同的患者推进个性化医疗 人口。我们的团队包括临床专家(PT的Dawn Waugh博士和MD的Michael Dayton博士)， 分析专家(Kathryn Colborne博士和Stef van Buuren博士博士)，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博士和Andrew Kittelson博士，PT，博士)。 这项研究回应了AHRQ的优先事项。它促进了对主要健康状况(TKA)的护理，在 优先人口(老年人)，为共同决策提供一个新的框架。临时区议会 代表着从传统的一刀切方法的范式转变，有机会获得更高的质量， 为个人量身定做护理，并在学习型医疗保健系统中更有效地分配资源。

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.