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Main objectiveSystematically review and synthesize preoperative and intraoperative factors associated with pain after total knee arthroplasty (TKA) in patients with osteoarthritis.MethodsBased on a peer-reviewed protocol, we searched Medline, Embase, CINAHL, Cochrane Library, and PEDro for prospective observational studies (January 2000 to February 2023) investigating factors associated with pain after TKA. The primary outcome was pain twelve months after TKA. Pain at three and six months were secondary outcomes. Multivariate random-effects meta-analyses were used to estimate mean correlation (95% CIs) between factors and pain. Sensitivity analysis was performed for each risk of bias domain and certainty of evidence was assessed.ResultsOf 13,640 studies, 29 reports of 10,360 patients and 61 factors were analysed. The mean correlation between preoperative factors and more severe pain at twelve months was estimated to be 0.36 (95% CI, 0.24, 0.47; P < .000; moderate-certainty evidence) for more catastrophizing, 0.15 (95% CI; 0.08, 0.23; P < .001; moderate-certainty evidence) for more symptomatic joints, 0.13 (95% CI, 0.06, 0.19; P < .001; very low-certainty evidence) for more preoperative pain. Mean correlation between more severe radiographic osteoarthritis and less pain was -0.15 (95% CI; -0.23, -0.08; P < .001; low-certainty evidence). In sensitivity analysis, the estimated correlation coefficient for pain catastrophizing factor increased to 0.38 (95% CI 0.04, 0.64). At six and three months, more severe preoperative pain was associated with more pain. Better preoperative mental health was associated with less pain at six months.Conclusion and relevanceMore pain catastrophizing, more symptomatic joints and more pain preoperatively were correlated with more pain, while more severe osteoarthritis was correlated with less pain one year after TKA. More preoperative pain was correlated with more pain, and better mental health with less pain at six and three months. These findings should be further tested in predictive models to gain knowledge which may improve TKA outcomes.

BackgroundOne in five patients report chronic pain following total knee arthroplasty (TKA) and are considered non-improvers. Psychological interventions such as cognitive behavioral therapy (CBT), combined with exercise therapy and education may contribute to reduced pain an improved function both for patients with OA or after TKA surgery, but the evidence for the effectiveness of such interventions is scarce. This randomized controlled trial with three arms will compare the clinical effectiveness of patient education and exercise therapy combined with internet-delivered CBT (iCBT), evaluated either as a non-surgical treatment choice or in combination with TKA, in comparison to usual treatment with TKA in patients with knee OA who are considered candidates for TKA surgery.MethodsThe study, conducted in three orthopaedic centers in Norway will include 282 patients between ages 18 and 80, eligible for TKA. Patients will be randomized to receive the exercise therapy + iCBT, either alone or in combination with TKA, or to a control group who will undergo conventional TKA and usual care physiotherapy following surgery. The exercise therapy will include 24 one hour sessions over 12 weeks led by a physiotherapist. The iCBT program will be delivered in ten modules. The physiotherapists will receive theoretical and practical training to advise and mentor the patients during the iCBT program. The primary outcome will be change from baseline to 12 months on the pain sub-scale from the Knee Injury and Osteoarthritis Outcome Score (KOOS). Secondary outcomes include the remaining 4 sub-scales from the KOOS (symptoms, function in daily living, function in sports and recreation, and knee-related quality of life), EQ-5D-5L, the Pain Catastrophizing Scale, the 30-s sit-to-stand test, 40-m walking test and ActiGraph activity measures. A cost-utility analysis will be performed using QALYs derived from the EQ-5D-5L and registry data.DiscussionThis is the first randomized controlled trial to investigate the effectiveness of exercise therapy and iCBT with or without TKA, to optimize outcomes for TKA patients. Findings from this trial will contribute to evidence-based personalized treatment recommendations for a large proportion of OA patients who currently lack an effective treatment option.Trial registrationClinicaltrials.gov : NCT03771430 . Registered: Dec 11, 2018.

IntroductionOne in five patients undergoing total knee arthroplasty (TKA) experience unchanged or worse pain and physical function 1 year after surgery. Identifying risk factors for unfavourable outcomes is necessary to develop tailored interventions to minimise risk. There is a need to review more current literature with updated methodology that addresses the limitations of earlier systematic reviews and meta-analyses. We present a Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols compliant protocol for a systematic review and meta-analysis of predictors of chronic pain and impaired function after TKA.Methods and analysisThis review will include prospective longitudinal observational studies, or randomised trials (including cluster and crossover designs) that report arm-wise predictors of chronic postsurgical pain or impaired physical function at 3 months, 6 months or 12 months. A comprehensive literature search of studies published between 2000 and 2019 will be performed in Medline, Embase, CINAHL, Cochrane Library and PEDro. Blinded assessment with consensus agreement will be applied for inclusion of studies, data extraction and assessment of bias risk (Quality in Prognosis Studies tool). The co-primary outcomes, pain and impaired function, at 12 months after TKA will be analysed separately. Estimates of association between each outcome and any preoperative or intraoperative factor that may predict chronic pain or impaired physical function will be extracted from the included studies, where possible. For randomised studies, results will only be extracted from TKA arms (or the first period of crossover trials). Estimates of association from the primary evidence will be synthesised narratively, and quantitatively using multivariate meta-analysis to provide 'pooled' estimates of association. Subgroup and sensitivity analyses will be performed. Certainty of evidence for each predictor will be derived from the Grading of Recommendations Assessment, Development and Evaluation framework.Ethics and disseminationNo ethical issues are associated with this project. The results from this review will be published in peer-reviewed journals and presented at international conferences.Prospero registration numberCRD42018079069.

Objective: Preoperative risk prediction models can support shared decision‐making before total hip arthroplasties (THAs). Here, we compare different machine‐learning (ML) approaches to predict the six‐month risk of adverse events following primary THA to obtain accurate yet simple‐to‐use risk prediction models. Methods: We extracted data on primary THAs (N = 262,356) between 2010 and 2018 from the Nordic Arthroplasty Register Association dataset. We benchmarked a variety of ML algorithms in terms of the area under the receiver operating characteristic curve (AUROC) for predicting the risk of revision caused by periprosthetic joint infection (PJI), dislocation or periprosthetic fracture (PPF), and death. All models were internally validated against a randomly selected test cohort (one‐third of the data) that was not used for training the models. Results: The incidences of revisions because of PJI, dislocation, and PPF were 0.8%, 0.4%, and 0.3%, respectively, and the incidence of death was 1.2%. Overall, Lasso regression with stable iterative variable selection (SIVS) produced models using only four to five input variables but with AUROC comparable to more complex models using all 32 variables available. The SIVS‐based Lasso models based on age, sex, preoperative diagnosis, bearing couple, fixation, and surgical approach predicted the risk of revisions caused by PJI, dislocations, and PPF, as well as death, with AUROCs of 0.61, 0.67, 0.76, and 0.86, respectively. Conclusion: Our study demonstrates that satisfactory predictive potential for adverse events following THA can be reached with parsimonious modeling strategies. The SIVS‐based Lasso models may serve as simple‐to‐use tools for clinical risk assessment in the future. [ABSTRACT FROM AUTHOR]