Abstract
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.
Highlights
Osteoarthritis is a common chronic musculoskeletal condition affecting approximately 9% of Australians (Australian Bureau of Statistics [ABS], 2019) and an estimated 250 million people globally (Vos et al, 2012)
We propose the development of a nonlinear, multivariate, machine-learning Total Knee Arthroplasty (TKA) recovery prediction model, drawing on the research base and expertise of multiple disciplines
This would involve the preoperative assessment of individual modifiable factors that impact individual recovery trajectories to facilitate the development of individualized multidisciplinary rehabilitation plans based on patients’ unique risk and resilience profiles
Summary
Osteoarthritis is a common chronic musculoskeletal condition affecting approximately 9% of Australians (Australian Bureau of Statistics [ABS], 2019) and an estimated 250 million people globally (Vos et al, 2012). Because there is no known cure and symptoms tend to worsen in severity over time, osteoarthritis can have a progressively debilitating impact on an individual’s health and functioning (Hunter and Bierma-Zeinstra, 2019; Törmälehto et al, 2019), when conservative management interventions are unsuccessful in restricting disease progression (Jones et al, 2007; O’Brien et al, 2019). Symptoms can lead to disrupted sleep and fatigue (Sasaki et al, 2014a), and reliance on a caregiver (Hunter et al, 2014) These difficulties affect mood, psychological wellbeing and health-related quality of life (Jones et al, 2007; Sasaki et al, 2014a; Törmälehto et al, 2019)
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