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-centred 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-centred 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-centred 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-centred 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 summarised, and intervention options are briefly presented. While focusing on rehabilitation following TKA, we advocate for the broader utilisation of multivariate prediction models to inform individually-tailored interventions targeting a range of health conditions. (shrink)

ObjectivesThe purpose of this study was to identify risk factors for delirium after total joint arthroplasty and provide theoretical guidance for reducing the incidence of delirium after TJA.MethodsThe protocol for this meta-analysis is registered with PROSPERO. We searched PubMed, the Cochrane Library and Embase for observational studies on risk factors for delirium after TJA. Review Manager 5.3 was used to calculate the relative risk or standard mean difference of potential risk factors related to TJA. STATA 14.0 was used for (...) quantitative publication bias evaluation.ResultsIn total, 25 studies including 3,767,761 patients from 9 countries were included. Old age has been widely recognized as a risk factor for delirium. Our results showed that the main risk factors for delirium after TJA were patient factors, comorbidities, surgical factors and drug factors.ConclusionsMultiple risk factors were associated with delirium after TJA. These results may help doctors predict the occurrence of delirium after surgery and determine the correct treatment.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier: CRD42020170031. (shrink)