Ditton Elizabeth, Johnson Sarah, Hodyl Nicolette, Flynn Traci, Pollack Michael, Ribbons Karen, Walker Frederick Rohan, Nilsson Michael
Centre for Rehab Innovations, The University of Newcastle, Callaghan, NSW, Australia.
Hunter Medical Research Institute, New Lambton Heights, NSW, Australia.
Front Psychol. 2020 May 29;11:1061. doi: 10.3389/fpsyg.2020.01061. eCollection 2020.
Total knee arthroplasty (TKA) is a commonly implemented elective surgical treatment for end-stage osteoarthritis of the knee, demonstrating high success rates when assessed by objective medical outcomes. However, a considerable proportion of TKA patients report significant dissatisfaction postoperatively, related to enduring pain, functional limitations, and diminished quality of life. In this conceptual analysis, we highlight the importance of assessing patient-centered outcomes routinely in clinical practice, as these measures provide important information regarding whether surgery and postoperative rehabilitation interventions have effectively remediated patients' real-world "quality of life" experiences. We propose a novel precision medicine approach to improving patient-centered TKA outcomes through the development of a multivariate machine-learning model. The primary aim of this model is to predict individual postoperative recovery trajectories. Uniquely, this model will be developed using an interdisciplinary methodology involving non-linear analysis of the unique contributions of a range of preoperative risk and resilience factors to patient-centered TKA outcomes. Of particular importance to the model's predictive power is the inclusion of a comprehensive assessment of modifiable psychological risk and resilience factors that have demonstrated relationships with TKA and other conditions in some studies. Despite the potential for patient psychological factors to limit recovery, they are typically not routinely assessed preoperatively in this patient group, and thus can be overlooked in rehabilitative referral and intervention decision-making. This represents a research-to-practice gap that may contribute to adverse patient-centered outcomes. Incorporating psychological risk and resilience factors into a multivariate prediction model could improve the detection of patients at risk of sub-optimal outcomes following TKA. This could provide surgeons and rehabilitation providers with a simplified tool to inform postoperative referral and intervention decision-making related to a range of interdisciplinary domains outside their usual purview. The proposed approach could facilitate the development and provision of more targeted rehabilitative interventions on the basis of identified individual needs. The roles of several modifiable psychological risk and resilience factors in recovery are summarized, and intervention options are briefly presented. While focusing on rehabilitation following TKA, we advocate for the broader utilization of multivariate prediction models to inform individually tailored interventions targeting a range of health conditions.
全膝关节置换术（TKA）是一种常用于治疗终末期膝关节骨关节炎的择期手术治疗方法，从客观医学结果评估来看成功率较高。然而，相当一部分TKA患者术后报告有显著的不满，涉及持续疼痛、功能受限和生活质量下降。在本概念分析中，我们强调在临床实践中常规评估以患者为中心的结果的重要性，因为这些指标提供了有关手术和术后康复干预是否有效改善患者现实世界“生活质量”体验的重要信息。我们提出一种新颖的精准医学方法，通过开发多变量机器学习模型来改善以患者为中心的TKA结果。该模型的主要目标是预测个体术后恢复轨迹。独特的是，该模型将采用跨学科方法开发，涉及对一系列术前风险和恢复力因素对以患者为中心的TKA结果的独特贡献进行非线性分析。对该模型预测能力特别重要的是纳入对可改变的心理风险和恢复力因素的全面评估，在一些研究中这些因素已表明与TKA及其他病症有关。尽管患者心理因素有可能限制恢复，但在该患者群体中术前通常不进行常规评估，因此在康复转诊和干预决策中可能被忽视。这代表了一个从研究到实践的差距，可能导致不良的以患者为中心的结果。将心理风险和恢复力因素纳入多变量预测模型可以改善对TKA术后结果欠佳风险患者的检测。这可以为外科医生和康复提供者提供一个简化工具，以指导与他们通常职责范围之外的一系列跨学科领域相关的术后转诊和干预决策。所提出的方法可以促进根据确定的个体需求制定和提供更有针对性的康复干预措施。总结了几种可改变的心理风险和恢复力因素在恢复中的作用，并简要介绍了干预选项。在关注TKA术后康复的同时，我们主张更广泛地利用多变量预测模型来指导针对一系列健康状况的个性化干预措施。
